Bone reconstruction and orthopedic implants

Abstract

The present application relates to bone reconstruction and bone surgical implants. A surgical navigation system includes a signal receiver communicatively connected to a main processor, the main processor being programmed to calculate changes in the three-dimensional position of an inertial measurement unit mounted to a surgical tool using a sequential Monte Carlo algorithm, the processor being communicatively connected to a first memory storing tool data unique to each of a plurality of surgical tools and a second memory storing model data sufficient to construct a three-dimensional model of an anatomical feature, the main processor being communicatively connected to a display providing visual feedback regarding the three-dimensional position of the surgical tool relative to the anatomical feature.

Description

[0001] This application is a divisional application of the invention patent application with Chinese application number 201480073304.9 (corresponding to PCT international application number PCT / US2014 / 069411), application date December 9, 2014, and invention name “Bone Reconstruction and Bone Surgery Implants”.

[0002] CROSS REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Provisional Patent Application Serial No. 61 / 913,608, filed December 9, 2013, entitled “CRANIUM AND POSTCRANIAL BONE AND SOFT TISSUE RECONSTRUCTION,” U.S. Provisional Patent Application Serial No. 61 / 951,221, filed March 11, 2014, entitled “CRANIUM AND POSTCRANIAL BONE AND SOFT TISSUE RECONSTRUCTION,” U.S. Provisional Patent Application Serial No. 61 / 977,984, filed April 10, 2014, entitled “CRANIUM AND POSTCRANIAL BONE AND SOFT TISSUE RECONSTRUCTION,” and U.S. Provisional Patent Application Serial No. 61 / 977,984, filed April 10, 2014, entitled “CRANIUM AND POSTCRANIAL BONE AND SOFT TISSUE RECONSTRUCTION,” and U.S. Provisional Patent Application Serial No. 61 / 951,221, filed March 11, 2014, entitled “CRANIUM AND POSTCRANIAL BONE AND SOFT TISSUE RECONSTRUCTION,” AND SOFT TISSUE RECONSTRUCTION), the disclosure of each of which is incorporated herein by reference. Technical Field

[0004] The present disclosure relates to various aspects of orthopedic surgery, including bone and tissue reconstruction, patient-specific and mass customized orthopedic surgical implants, gender and race-specific orthopedic surgical implants, cutting guides, trauma shields, bone graft cutting and placement guides, patient-specific instrumentation, the use of inertial measurement units for anatomical tracking of kinematics and pathology, and the use of inertial measurement units for navigation during orthopedic surgery. SUMMARY OF THE INVENTION

[0005] A first aspect of the present invention provides a surgical navigation system comprising: a signal receiver communicatively connected to a main processor, the main processor being programmed to calculate changes in the three-dimensional position of an inertial measurement unit mounted to a surgical tool using a sequential Monte Carlo algorithm, the processor being communicatively connected to a first memory storing tool data unique to each of a plurality of surgical tools and a second memory storing model data sufficient to construct a three-dimensional model of an anatomical feature, the main processor being communicatively connected to a display providing visual feedback regarding the three-dimensional position of the surgical tool relative to the anatomical feature.

[0006] In a more detailed embodiment of the first aspect, the surgical navigation system further comprises a reference inertial measurement unit communicatively coupled to a first onboard processor and a first wireless transmitter to transmit data to the main processor, the reference inertial measurement unit being configured to be attached to the anatomical feature, where the first onboard processor directs the transmission of data from the reference inertial measurement unit to the first wireless transmitter, wherein the inertial measurement unit mounted to the surgical tool comprises a utility inertial measurement unit communicatively coupled to a second onboard processor and a second wireless transmitter, the second onboard processor being configured to be mounted to one of the plurality of surgical tools, and wherein the main processor is communicatively coupled to a main receiver, the main receiver being configured to receive data from the first wireless transmitter and data from the second wireless transmitter. In yet another more detailed embodiment, the second onboard processor directs the communication of the identity of the surgical tool to which the utility inertial measurement unit is mounted via the second wireless transmitter. In another detailed embodiment, the inertial measurement unit includes at least three accelerometers and three magnetometers, each of the at least three accelerometers outputs data for a total of no less than nine accelerometer data streams relative to three axes, each of the at least three magnetometers outputs data for a total of no less than nine magnetometer data streams relative to three axes, and the host processor uses the nine accelerometer data streams and the nine magnetometer data streams to calculate a change in the three-dimensional position of the inertial measurement unit mounted to the surgical tool. In yet another detailed embodiment, the model data stored in the second memory includes a three-dimensional virtual model of the anatomical feature, the tool data stored in the first memory includes a three-dimensional virtual model of the plurality of surgical tools, the display displays the three-dimensional virtual model of the anatomical feature, the display displays the three-dimensional virtual model of the surgical tool, the host processor effectively uses data from the reference inertial measurement unit to reposition the three-dimensional virtual model of the anatomical feature, and the host processor effectively uses data from the utility inertial measurement unit to reposition the three-dimensional virtual model of the surgical tool. In a more detailed embodiment, the main processor effectively uses data from the inertial measurement unit to reposition the three-dimensional virtual model of the surgical tool relative to the three-dimensional virtual model of the anatomical feature in real time. In a more detailed embodiment, the sequential Monte Carlo algorithm includes a von Mises-Fisher density algorithm component. In another more detailed embodiment, the tool data stored in the first memory includes position data indicating a relative distance between an end effector of the surgical tool and a mounting location on the surgical device for the inertial measurement unit, and the surgical tool includes at least one of a reamer, a positioning cup, an impactor, a drill, a saw, and a cutting guide.In yet another more detailed embodiment, the inertial measurement unit includes at least 3 magnetometers and the display is connected to at least one of the surgical tool or the main processor.

[0007] A second aspect of the present invention provides a calibration system for an inertial measurement unit including a magnetometer and an accelerometer, the system comprising: (a) an initial platform that can be rotated and repositioned relative to an intermediate platform along a first axis; (b) a final platform that can be rotated and repositioned relative to the intermediate platform along a second axis, the second axis being perpendicular to the first axis, the final platform including a stop configured to be mounted to an inertial measurement unit; and (c) a processor and associated software configured to be communicatively connected to the inertial measurement unit, the software effectively utilizing data output from a magnetometer associated with the inertial measurement unit when the initial platform is rotated relative to the intermediate platform and when the final platform is rotated relative to the intermediate platform and recording a data set similar to an ellipsoid, the software effectively fitting a sphere to the data set and generating a magnetometer correction calculation to account for distortions in the local magnetic field, thereby normalizing future data output from the magnetometer.

[0008] In a more detailed embodiment of the second aspect, the initial platform is stationary. In yet another more detailed embodiment, the initial platform at least partially houses a motor, the motor being configured to cause the intermediate platform to rotate relative to the initial platform. In another detailed embodiment, the software effectively utilizes a first data set output from an accelerometer associated with the inertial measurement unit when the inertial measurement unit is in a first stationary position and effectively utilizes a second data set output from the accelerometer at a second stationary position different from the first stationary position to generate an accelerometer correction calculation to normalize future data output from the accelerometer. In yet another detailed embodiment, the first stationary position corresponds to the initial platform being at a first fixed position relative to the intermediate platform and the final platform being at a second fixed position relative to the intermediate platform, and the second stationary position corresponds to at least one of the initial platform being at a third fixed position relative to the intermediate platform and the final platform being at a fourth fixed position relative to the intermediate platform. In a more detailed embodiment, the final platform includes a plurality of stops, wherein each of the plurality of stops is configured to be mounted to at least one of the plurality of inertial measurement units.

[0009] A third aspect of the present invention provides a method for calibrating an inertial measurement unit including a magnetometer, the method comprising: (a) rotating a first inertial measurement unit including a first inertial measurement unit around a first rotation axis and a second rotation axis, wherein the first rotation axis is perpendicular to the second rotation axis, while receiving raw local magnetic field data from the first magnetometer; (b) performing uniform calculations on the raw local magnetic field data to calculate distortions in the local magnetic field; and (c) normalizing the raw local magnetic field data received from the magnetometer by accounting for the calculated distortions in the local magnetic field to provide refined local magnetic field data.

[0010] In a more detailed embodiment of the third aspect, the first inertial measurement unit includes a first accelerometer, and the method further includes: (i) holding the first inertial measurement unit stationary in a first three-dimensional position while receiving raw accelerometer data from the first accelerometer; (ii) holding the first inertial measurement unit stationary in a second three-dimensional position while receiving raw accelerometer data from the first accelerometer, the second three-dimensional position being different from the first three-dimensional position; and (iii) normalizing the data received from the first accelerometer to reflect zero acceleration when the first accelerometer is stationary. In yet another more detailed embodiment, the first inertial measurement unit includes a second accelerometer, and the method further includes: (i) when the first accelerometer is stationary, holding the second inertial measurement unit stationary in a third three-dimensional position while receiving raw accelerometer data from the second accelerometer; (ii) when the first accelerometer is stationary, holding the second inertial measurement unit stationary in a fourth three-dimensional position while receiving raw accelerometer data from the second accelerometer, the fourth three-dimensional position being different from the third three-dimensional position; and (iii) normalizing the data received from the second accelerometer to reflect zero acceleration when the second accelerometer is stationary. In another detailed embodiment, the raw local magnetic field data represents an ellipsoid in three dimensions, and the refined local magnetic field data represents a sphere in three dimensions. In yet another detailed embodiment, the unified calculation includes fitting a sphere to the raw local magnetic field data, and normalizing the raw local magnetic field data includes subtracting the calculated distortion from the raw local magnetic field data to provide refined local magnetic field data. In a more detailed embodiment, the method also includes a second inertial measurement unit having its own first accelerometer. In a more detailed embodiment, the second inertial measurement unit has its own first accelerometer.

[0011] A fourth aspect of the present invention provides a method for identifying a surgical tool when connected to an inertial measurement unit, the method comprising: (a) mounting the inertial measurement unit to one of a plurality of surgical tools, each of the plurality of surgical tools having a unique interface; and (b) reading the unique interface to transmit a signal to a processor communicatively connected to the inertial measurement unit, thereby identifying one of the plurality of surgical tools in response to reading the unique interface.

[0012] In a more detailed embodiment of the fourth aspect, the inertial measurement unit is operatively connected to a plurality of switches, the unique interface occupies at least one of the plurality of switches, and the step of reading the unique interface includes determining, by the processor, which of the plurality of switches are occupied by the unique interface. In yet another more detailed embodiment, the processor is connected to the inertial measurement unit, and the processor and the inertial measurement unit are housed in a common housing. In another detailed embodiment, the processor is remote from the inertial measurement unit, and the processor and the inertial measurement unit are not housed in a common housing.

[0013] A fifth aspect of the present invention provides a method for performing surgical navigation, the method comprising: (a) generating acceleration data and magnetic measurement data using multiple inertial measurement units; (b) calibrating the multiple inertial measurement units near a surgical operating position; (c) aligning the relative positions of first and second inertial measurement units that make up the multiple inertial measurement units, wherein aligning the relative positions comprises mounting the first inertial measurement unit to an alignment tool that uniquely engages a patient's anatomical structure in a specific position and orientation, and wherein aligning the relative positions comprises mounting the second inertial measurement unit to the patient; (d) attaching the first inertial measurement unit to a surgical tool after alignment; (e) repositioning the surgical tool and the first inertial measurement unit to a surgical site associated with the patient's anatomical structure; and (f) providing visual feedback about at least one of the position and orientation of the surgical tool when at least one of the anatomical structure is not visible or the operating end of the surgical tool is not visible.

[0014] A sixth aspect of the present invention provides a method for performing surgical navigation, the method comprising: (a) generating acceleration data and magnetic measurement data using multiple inertial measurement units; (b) calibrating the multiple inertial measurement units near a surgical operation position; (c) aligning the relative positions of first and second inertial measurement units that make up the multiple inertial measurement units, wherein aligning the relative positions comprises mounting the first inertial measurement unit to an alignment tool that uniquely engages a patient's anatomical structure in a specific position and orientation, and wherein aligning the relative positions comprises mounting the second inertial measurement unit to the patient; (d) attaching the first inertial measurement unit to a surgical tool after alignment; (e) repositioning the surgical tool and the first inertial measurement unit to a surgical site associated with the patient's anatomical structure; and (f) providing visual feedback about the position and orientation of the surgical tool for a predetermined surgical plan, wherein the predetermined surgical plan identifies at least one of an allowable range of positions and an allowable range of orientations that the surgical tool may occupy.

[0015] The seventh aspect of the present invention provides a method for generating a trauma protection plate for a specific bone, the method comprising: (a) accessing a database containing multiple three-dimensional bone models of a specific bone; (b) evaluating characteristics including at least one of a longitudinal profile and a cross-sectional profile for each of the multiple three-dimensional bone models, wherein the longitudinal profile is obtained along the main dimension of the multiple three-dimensional bone models; (c) clustering the multiple three-dimensional bone models based on the evaluated characteristics to generate a plurality of clusters, wherein the plurality of clusters are less than 10% of the plurality of three-dimensional bone models in number; and (d) generating a trauma protection plate for each of the multiple clusters.

[0016] In a more detailed embodiment of the seventh aspect, generating a trauma plate for each of the plurality of clusters includes selecting a fixation position to avoid attachment of soft tissue to the particular bone. In yet another more detailed embodiment, the plurality of three-dimensional bone models include at least one commonality, wherein the commonality includes at least one of gender, race, age range, and height range. In another detailed embodiment, generating a trauma plate for each of the plurality of clusters includes incorporating at least one of an average longitudinal profile and an average cross-sectional profile of the particular cluster.

[0017] The eighth aspect of the present invention is to provide a method for generating a patient-specific trauma protection plate for a specific bone, the method comprising: (a) obtaining patient-specific image data for a specific bone that has been damaged or degenerated; (b) using the patient-specific image data to analyze at least one of those parts of the specific bone that are missing and those parts of the specific bone that exist; (c) generating a patient-specific virtual bone model of the specific bone in a unified state, which includes bones that are not visible in the patient-specific image data; (d) evaluating the contour of the patient-specific virtual bone model; and (e) using the patient-specific virtual bone model to generate a patient-specific trauma protection plate.

[0018] A ninth aspect of the present invention is to provide a method for using inertial measurement unit kinematics to track the movement of a patient's anatomical structure, the method comprising: (a) mounting a first inertial measurement unit to the outside of a first target anatomical feature of the patient; (b) mounting a second inertial measurement unit to the outside of a second target anatomical feature of the patient; (c) using the first inertial measurement unit to align the position of the patient's first anatomical feature with a virtual model of the patient's first target anatomical feature; (d) using the second inertial measurement unit to align the position of the patient's second anatomical feature with the virtual model of the patient's second target anatomical feature; (e) using the first inertial measurement unit to dynamically associate the position of the patient's first target anatomical feature with the virtual model of the first anatomical feature; and (f) using the second inertial measurement unit to dynamically associate the position of the patient's second target anatomical feature with the virtual model of the second anatomical feature. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic diagram of the overall process of producing mass-customized and patient-specific molds from partial anatomical structures.

[0020] Figure 2 is a schematic diagram detailing how to add new anatomical structures to the statistical atlas in order to generate correspondences.

[0021] Figure 3 is corresponding to Figure 2 An overview of multi-resolution 3D registration algorithms in Multi-resolution 3D Registration.

[0022] Figure 4 It is a multi-scale registration using feature points with multi-scale characteristics.

[0023] Figure 5 yes Figure 3 A low-level decomposition of multiresolution registration outlined in .

[0024] Figure 6 is a graphical representation of the deterioration of the captured population after correspondence generation.

[0025] Figure 7 is a schematic representation of the complete bone reconstruction process using partial, deformed, or fragmented anatomy.

[0026] Figure 8 is a schematic diagram of the defect classification process used to generate defect templates.

[0027] Fig. 9 is a graphical example of an existing AAOS classification for acetabular defects.

[0028] Fig.10 is a graphical example of the existing Paprosky acetabular defect classification.

[0029] Fig.11 is a graphical example of an existing subclassification of the Paprosky acetabular defect.

[0030] Fig.12 is a table and related graphs showing the reconstruction results of different defects on the pelvis, which is Figure 7 An exemplary application and validation of the complete bone reconstruction depicted in .

[0031] Fig.13 is the distance map of the mean REM error of the reconstructions of different defects on the pelvis, which verifies Figure 7 The accuracy of the complete skeleton reconstruction depicted in .

[0032] Fig.14 On the left is a 3D model representation of a patient with severe pelvic discontinuity. On the right is an example of a 3D model of the patient's pelvis shown on the left.

[0033] Fig.15 is a comparison of the reconstructed left model and the original patient model, as well as the right and left anatomical structures.

[0034] Fig.16 is the distance map between the reconstructed model and the mirror image of the reconstructed pelvic model.

[0035] Fig.17 Figure 2 is a patient with complete pelvic discontinuity and the reconstruction results, with an RMS error of 1.8 mm.

[0036] Fig.18 It is the average distance map of the reconstruction results and reconstruction errors of part of the skull.

[0037] Fig.19 It is the result of reconstruction of a shattered femur.

[0038] Fig. 20is a schematic diagram of the process of generating patient-specific reconstructive implants.

[0039] Fig.21 yes Fig. 20 A schematic diagram of the process of implant generation is depicted in FIG.

[0040] Fig. 22 is a process flow chart illustrating the various steps in reconstructing a patient's complete anatomy from partial anatomy and producing a patient-specific acetabular cup implant for a pelvic discontinuity.

[0041] Fig.23 is a pictorial representation of a patient-specific placement guide for a patient-specific acetabular implant.

[0042] Fig.24 Included are images investigating the relationship between the three attachment sites of the implant and the acetabular cup orientation for mass customization.

[0043] Fig.25 The included images illustrate the process of mass customization of acetabular cages consistent with the present disclosure.

[0044] Fig.26 is a schematic diagram of a method for manufacturing custom acetabular components for mass production using modular design.

[0045] Fig. 27 is a schematic diagram of the process of generating a patient-specific femoral stem for reconstructive surgery.

[0046] Fig.28 is a schematic diagram of the process used to produce mass customized implants.

[0047] Fig.29 is a schematic diagram depicting the process of using a statistical atlas to produce both mass customized and patient-specific hip implants.

[0048] Fig.30 is a schematic diagram depicting the process of using a statistical atlas to produce both mass customized and patient-specific hip implants.

[0049] Fig.31 is a schematic diagram depicting an overview of the process used to design population-specific femoral stem components.

[0050] Fig.32 is a graphical representation showing the locations of proximal femoral landmarks.

[0051] Fig.33 It is a 3D model of the femur, exporting the medullary waist in the middle of the femur and the femoral waist along the length of the femur.

[0052] Fig.34is a graphical representation showing the location of the proximal femoral axis.

[0053] Fig.35 is a graphical representation showing where the neck center calculation is located.

[0054] Fig.36 It is a graphical representation of the two points that define the anatomical axis of the proximal femur.

[0055] Fig.37 It is a graphical representation of the 3D proximal femur measurement.

[0056] Fig.38 An exemplary Dorr ratio is shown, which is typically assumed to be 2D (from XR).

[0057] Fig.39 It is a graphical representation of the B / A ratio at the isthmus of the IM.

[0058] Fig.40 It is a graphical representation of the IM tube measurement.

[0059] Fig.41 are the contour and the fitted circle.

[0060] Fig.42 is a graphical representation of the measurements taken to obtain the IM canal to femoral radius ratio.

[0061] Fig.43 Two femur models are depicted showing the effect of varying the radius ratio, with the left model having a radius ratio of 0.69 and the right model having a radius ratio of 0.38.

[0062] Fig.44 Figure 2 is a plot of BMD versus RRFW for males and females, along with the best straight-line fit for each data set (males, females).

[0063] Fig.45 It is a graphic representation of the medial contour, neck axis, and head point of the proximal femur before alignment.

[0064] Fig.46 It is a graphical representation of the alignment of the anatomical axes with the Z direction.

[0065] Fig.47 It is a graphic representation of the medial profile aligned using the femoral neck pivot point.

[0066] Fig.48 is a graphical representation of the different models produced using interpolation between the models to show the smoothness of the interpolation.

[0067] Fig.49 It is a graphical and pictorial representation of the three-dimensional mapping of bone density.

[0068] Fig.50 is an X-ray showing the IM width at 3 heights as well as the proximal axis, head constriction, and femoral head.

[0069] Fig.51 It is a diagram of the proximal angle for head constriction.

[0070] Fig.52 is a plot of proximal angle versus head height.

[0071] Fig.53 It is a graph of head constriction versus head height.

[0072] Fig.54 is the histogram of the proximal angles.

[0073] Fig.55 is a graph depicting clusters of head constriction and calcar diameter in females and males.

[0074] Fig.56 is a diagram depicting clusters of head constriction and proximal angles in females and males.

[0075] Fig.57 is the histogram of head constriction.

[0076] Fig.58 is a histogram of IM sizes.

[0077] Fig.59 is a graphic representation of the female measurement of the proximal femur.

[0078] Fig.60 It is a graphic representation of the male measurement of the proximal femur.

[0079] Fig.61 is a graphic representation of the female measurement of the greater trochanter height.

[0080] Fig.62 is a graphic representation of the male measurement of the greater trochanter height.

[0081] Fig.63 is a graphical representation and table showing the differences in the shape of the intramedullary canal between males and females.

[0082] Fig.64 A female femur and intramedullary canal representing normal bone density and quality are depicted.

[0083] Fig.65 A female femur and intramedullary canal representing subnormal bone density and quality are depicted.

[0084] Fig.66 A female femur and intramedullary canal representing osteoporosis are depicted.

[0085] Fig.67is a graph of the dataset with the interpolated values ​​and the histogram with the head constricted.

[0086] Fig.68 is a graph containing a histogram of the Medullary Canal Sizes dataset.

[0087] Fig.69 The medial contours and head center distributions of each different femoral group are depicted.

[0088] Fig.70 is a graph showing head constriction for a particular size group of femurs.

[0089] Fig.71 is a table reflecting the anteversion angle measurements for men and women.

[0090] Fig.72 is a picture depicting how to measure front-to-back height.

[0091] Fig.73 is a plot of the head height of the femur relative to its pivot point versus the anterior-posterior head height for males and females, which includes the best linear fit through each data set (male, female).

[0092] Fig.74 is a plot of the head height of the femur relative to the midpoint of its anatomical axis versus the anterior-posterior head height for males and females, which includes the best linear fit through each data set (male, female).

[0093] Fig.75 is a graphical depiction of parameters used in generating a family of femoral stem implants based on gender and / or race in accordance with the present disclosure, Fig.75 Shape parameters extracted from clusters for mass customization of femoral stem components.

[0094] Fig.76 The original femoral stem is depicted in both assembled and exploded views.

[0095] Fig.77 The revised femoral stem is depicted in both assembled and exploded views.

[0096] Fig.78 is a graphical representation of surface points and the use of planes to separate acetabular cup geometry consistent with the present disclosure.

[0097] Fig.79 A plurality of virtual 3D acetabular cup anatomy templates generated in accordance with the present disclosure are illustrated.

[0098] Fig.80 The anatomical shapes of the acetabular cup and femoral stem ball are illustrated, showing multiple acetabular cup radii.

[0099] Fig.81It is a two-dimensional depiction of the curvature match between the acetabular cup and the femoral head.

[0100] Fig.82 is a graphic depiction of the contours of the pelvis used for cross-sectional analysis of the acetabular cup.

[0101] Fig.83 is a graphic depiction of automatic detection of the transverse acetabular ligament consistent with the present disclosure as a method of determining acetabular cup implant orientation.

[0102] Fig.84 is a graphic depiction of the process used to extract hole shape and size from a patient's micro-computed tomography scan to match the patient's bone anatomy.

[0103] Fig.85 is an exemplary process diagram for producing pet-specific implants and cutting guides in accordance with the present disclosure.

[0104] Fig.86 is an exemplary process diagram for producing mass customized orthopedic implants for pets using a statistical atlas in accordance with the present disclosure.

[0105] Fig.87 is an exemplary process diagram for producing a patient-specific cutting and placement device for an implant system in accordance with the present disclosure.

[0106] Fig.88 It comes from Fig.87 Schematic diagram of an exemplary process for non-rigid registration of and generation of patient-specific three-dimensional pelvic and proximal femur models from X-rays in accordance with the present disclosure.

[0107] Fig.89 are pictures and multiple X-rays used to reconstruct the pelvis and proximal femur in accordance with the present disclosure.

[0108] Fig.90 Yes Fig.87 An exemplary process for automatically segmenting the pelvis and proximal femur from MRI and CT scans as described in .

[0109] Fig.91 Yes Fig.87 An exemplary process diagram for automatically segmenting complex and fragmented anatomical structures from MRI or CT scans is outlined in FIG.

[0110] Fig.92 is an illustration of an exemplary process for creating virtual templates for both an acetabular cup and a femoral stem for hip replacement surgery.

[0111] Fig.93 is an illustrative process diagram for automated placement of a femoral stem using distal fixation, which is a specific instance of the general process outlined in FIG. 92 .

[0112] Fig.94 is an exemplary process diagram of automated placement of a femoral stem using a press fit and three-point contact, which is Fig.92 A specific example of the general process outlined in .

[0113] Fig.95 is a graphical depiction of automatically finding pelvic landmarks consistent with the present disclosure.

[0114] Fig.96 is a pictorial depiction of automated acetabular cup orientation and placement consistent with the present disclosure.

[0115] Fig.97 A series of X-ray images are included with overlays of measurements and calculations used to evaluate acetabular cup and femoral stem placement in accordance with the present disclosure.

[0116] Fig.98 is a graphic depiction of evaluating acetabular cup and femoral stem placement to ensure overall limb length restoration and orientation in accordance with the present disclosure.

[0117] Fig.99 is a screenshot of a pre-designed interface for evaluating and modifying implant placement and sizing in accordance with the present disclosure.

[0118] Fig.100 Included is a series of process images depicting an exemplary process for using patient-specific tools for femoral stem resection and placement.

[0119] Fig.101 Included is a series of process images depicting an exemplary process for using a patient-specific guide for acetabular cup reaming and placement.

[0120] Fig.102 Depicted are a series of 3D virtual representations of an acetabulum that may be used to generate patient-specific tools and lock mechanisms consistent with the present disclosure.

[0121] Fig.103 is an exemplary process diagram for using an inertial measurement unit as part of surgical navigation during hip replacement surgery.

[0122] Fig.104 is a series of process images illustrating an exemplary process of using an inertial measurement unit as part of surgical navigation during hip replacement surgery.

[0123] Fig.105 is a series of process images illustrating an exemplary process of using an inertial measurement unit as part of surgical navigation specific to the femur during hip replacement surgery.

[0124] Fig.106Illustrated are exemplary tools and processes for calibrating the position of an inertial measurement unit for use in later surgical navigation during hip replacement surgery.

[0125] Fig.107 is an exemplary process flow diagram for preparing for and using an inertial measurement unit during a surgical procedure, and for using inertial measurements to evaluate surgical outcomes after the surgical procedure is completed.

[0126] Fig.108 Depicted is a series of images showing an inertial measurement unit chassis / enclosure mounted to various different tools as part of facilitating surgical navigation during a surgical procedure.

[0127] Fig.109 Depicted are a series of images showing an Inertial Measurement Unit (IMU) chassis / housing, a picture calibrating the position of the IMU relative to the patient's anatomy, a picture showing the IMU chassis being used for surgical navigation of a reamer, and finally a picture showing the IMU chassis being used for surgical navigation of an acetabular cup impactor.

[0128] Fig.110 A picture-in-picture is depicted showing the use of an IMU chassis with an acetabular cup impactor, and a graphical interface (inset) showing a model of the patient's anatomy (in this case, the pelvis), with the distal end of the impactor color coded to confirm that the orientation of the impactor is consistent with the surgically pre-planned orientation.

[0129] Fig.111 is a picture of an IMU used in accordance with the present disclosure and a reference ruler used to characterize the relative dimensions of the IMU.

[0130] Fig.112 is an exemplary process flow chart for producing wound guards and fixation devices for a given population in accordance with the present disclosure.

[0131] Fig.113 is an image from an average bone showing localized points on the bone surface that are localized across a population of bones in a statistical atlas in order to determine the shape of a bone or trauma plate.

[0132] Fig.114 is an image of a skeleton showing the push of the guard plate sites across the population, with a single instance shown here.

[0133] Fig.115 is a graph showing the extraction of the bone / trauma plate midline curve after advancement at the plate site.

[0134] Fig.116 It is a graphical representation of the results of calculating the 3D radius of curvature (parameter) for the midline curve of the wound shield.

[0135] Fig.117 is a diagram showing how to calculate the length of the wound guard after the guard site is pushed.

[0136] Fig.118 is a diagram showing how the mid-width of a wound shield is calculated after the shield site has been pushed.

[0137] Fig.119 is a diagram showing how the cross-sectional radius of a wound guard is calculated after the guard site has been pushed.

[0138] Fig.120 A graph showing fender dimensional data for determining the optimum number of clusters.

[0139] Fig.121 Includes 2D and 3D plots of fender dimensional data used to generate clusters (in Fig.111 denoted as “clusters” in Figure 2).

[0140] Fig.122 A number of images are depicted, which reflect the parameterization of the shield dimensions (in Fig.111 ) shown as “parameterized curves” and “generated model” in .

[0141] Fig.123 is an exemplary image showing a bone plate / trauma plate for a particular cluster being fit to one of the bone models from that cluster to assess compliance / fit with that population.

[0142] Fig.124 is a 3D surface distance map reflecting the spacing between the bottom side of the bone / trauma plate surface and the surface of the bone model selected to evaluate plate fit.

[0143] Fig.125 Validation of the designed protective plate on cadavers is shown to avoid muscle and ligament impingement.

[0144] Fig.126 is an exemplary diagram illustrating the interaction between elements of an exemplary patient-fitted clavicle trauma system consistent with the present disclosure.

[0145] Fig.127 yes Fig.126 An exemplary process flow diagram of the pre-planned elements is depicted in FIG.

[0146] Fig.128 is Fig.126 An exemplary process flow chart for intraoperative guidance, in this case using fluoroscopy, is depicted in FIG.

[0147] Fig.129Fluoroscopic image of the clavicle, alongside an illustration of the clavicle from a top view with some of the surrounding anatomy.

[0148] Fig.130 is Fig.126 An exemplary process flow chart for intraoperative guidance, in this case using ultrasound, is depicted in FIG.

[0149] Fig.131 is a graphical representation matched to X-ray or fluoroscopic images acquired during various motions and graphs showing post-operative assessment of shoulder kinematics using one or more inertial measurement units.

[0150] Fig.132 are a pair of three-dimensional illustrations of the clavicle and surrounding anatomical structures.

[0151] Fig.133 Depicted are two different views of a clavicle bone model and points along the bone model used to identify the midline curvature of the clavicle.

[0152] Fig.134 Depicted is a clavicle bone model and where muscles are attached to the bone model.

[0153] Fig.135 A series of surface plots of the average clavicle model for males and females in a given population are shown, along with the extent of shape differences within each population.

[0154] Fig.136 are a pair of three-dimensional illustrations of the clavicles that relate contour differences to muscle attachment sites.

[0155] Fig.137 is a series of cross sections of the clavicle taken in a population of men and women, illustrating the differences in contour of the clavicle at various muscle attachment sites.

[0156] Fig.138 is a series of cross sections of the clavicle taken in male and female populations, illustrating the differences in the contour of the clavicle along the length of the clavicle.

[0157] Fig.139 Shown are left and right clavicle models generated in response to population data from a statistical atlas, reflecting the morphological differences between the left and right clavicles.

[0158] Fig.140 Depicted are a clavicle bone model fitted with a superior lateral plate consistent with the present disclosure (left), the plate midline curve (middle), and the midline plate curvature showing the radius of curvature (right).

[0159] Fig.141is a diagram depicting the superior lateral plate clusters for male and female clavicle populations, and Table 1 includes data related thereto.

[0160] Fig.142 Depicted are a clavicle bone model fitted with an anterior mid-trunk 7h plate consistent with the present disclosure (left), the plate midline curve (middle), and the midline plate curvature showing a single radius of curvature (right).

[0161] Fig.143 is a diagram depicting the anterior mid-trunk 7h plate cluster for male and female clavicle populations, and Table 2 includes data related thereto.

[0162] Fig.144 Depicted are a clavicle bone model fitted with an upper mid-trunk plate consistent with the present disclosure (left), the plate midline curve (middle), and the midline plate curvature showing different radii of curvature (right).

[0163] Fig.145 is a graph depicting the upper midstem plate clusters for male and female clavicle populations, and Table 3 includes data associated therewith.

[0164] Fig.146 Depicted are a clavicle bone model fitted with an anterior lateral plate consistent with the present disclosure (left), the plate midline curve (middle), and the midline plate curvature showing different radii of curvature (right).

[0165] Fig.147 is a graph depicting the anterior lateral plate cluster for male and female clavicle populations, and Table 4 includes data related thereto.

[0166] Fig.148 Depicted are a clavicle bone model fitted with an anterior mid-stem long plate consistent with the present disclosure (left), the plate midline curve (middle), and the midline plate curvature showing different radii of curvature (right).

[0167] Fig.149 is a graph depicting the anterior midstem plate cluster for male and female clavicle populations, and Table 5 includes data related thereto.

[0168] Fig.150 is a flow chart of an exemplary process for producing a customized plate placement guide for wound reconstruction surgery in accordance with the present disclosure.

[0169] Fig.151 is a flow chart of an exemplary process for producing a customized cutting and placement guide for reconstructive surgery using a bone graft in accordance with the present disclosure.

[0170] Fig.152 is an exemplary process flow chart for generating a wound plate template and placement tool in accordance with the present disclosure.

[0171] Fig.153 is a flow chart of an exemplary process for generating a hip revision cage template and placement tool in accordance with the present disclosure.

[0172] Fig.154 is a flow chart of an exemplary process for soft tissue and motion tracking of body anatomy using an inertial measurement unit in accordance with the present disclosure.

[0173] Fig.155 Included are a pair of screen shots of a kinematic software interface consistent with the present disclosure, which identifies the position of soft tissue on a skeletal model and tracks soft tissue deformities.

[0174] Fig.156 A skeletal model containing the femur, tibia, and fibula depicting points on the respective skeletal models where ligaments (MCL, LCL) are attached, wherein the points are color coded to identify points with a higher or lower likelihood of ligament attachment.

[0175] Fig.157 A skeletal model of the distal femur is included, depicting points on the corresponding skeletal model where ligaments (ACL, PCL) are attached, where the points are color coded to identify points with a higher or lower likelihood of ligament attachment.

[0176] Fig.158 A skeletal model of the proximal tibia is included, which depicts the points on the corresponding skeletal model where ligaments (ACL, PCL) are attached, where the points are color coded to identify points with a higher or lower likelihood of ligament attachment.

[0177] Fig.159 Anterior, posterior and lateral views of a 3D virtual model of a knee joint including ligament attachments consistent with the present disclosure are depicted.

[0178] Fig.160 Depicted using fluoroscopic images for Fig.159 Kinematic movement modeling of a fully assembled knee joint model.

[0179] Fig.161 Included are diagrams of a distal femur bone model and a proximal tibia bone model consistent with the present disclosure, reflecting real-time tracking of anatomical axes.

[0180] Fig.162 Includes knee models obtained with multiple motions and reconstructed spiral axes.

[0181] Fig.163 A skeletal model of the knee joint is included, depicting the anatomical axes in the coronal plane.

[0182] Fig.164 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0183] Fig.165 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0184] Fig.166 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0185] Fig.167 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0186] Fig.168 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0187] Fig.169 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0188] Fig.170 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0189] Fig.171 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0190] Fig.172 is an exemplary diagram of using an inertial measurement unit to record motion data for clinical examination of a knee joint in accordance with the present disclosure.

[0191] Fig.173 Included is a series of photos, the first showing a patient donning a pair of inertial measurement unit (IMU) packs, the second showing the relative size of the IMU packs relative to a single IMU, and the third showing the relative size of a single IMU relative to a quarter.

[0192] Fig.174 is a screenshot of a user interface consistent with the present disclosure depicting a proximal tibia model that is dynamically updated based on input received from an inertial measurement unit to provide feedback regarding load distribution as a patient's knee undergoes various motions.

[0193] Fig.175is a photograph of a patient's lower back showing inertial measurement units (IMUs) placed separately above the L1 and L5 vertebrae for tracking relative motion of each vertebra during various motions, and an auxiliary diagram showing that each IMU is capable of outputting data indicative of motion across 3 axes.

[0194] Fig.176 Contains a series of photographs showing the patient and Fig.175 IMU.

[0195] Fig.177 is a diagram showing a process for determining the relative orientation of at least two objects using inertial measurement unit data in accordance with the present disclosure.

[0196] Fig.178 A pair of graphs are included showing the absolute change in orientation of the anatomical axes associated with the vertebrae (particularly L1, L5) during a patient's lateral bending activity, wherein the "(A)" data graph represents a healthy patient and the "(B)" data graph represents a patient exhibiting spinal degeneration.

[0197] Fig.179 A pair of images have been included, one being a top view of the proximal tibia and the second being an elevated perspective view of the proximal tibia, showing a surgical navigation tool with a regularized IMU to ensure the correct orientation and position of the tibia implant assembly.

[0198] Fig.180 is an overhead perspective view of an exemplary inertial measurement unit calibration apparatus consistent with the present disclosure.

[0199] Fig.181 Shown are local magnetic field maps (isometric front and top views) generated by data output from the inertial measurement unit before calibration (the top series of 3 images approximate an ellipsoid) and local magnetic field maps (isometric front and top views) generated by data output from the inertial measurement unit after calibration (the bottom series of 3 images approximate a sphere).

[0200] Fig.182 Contains a series of figures showing an exemplary location of a magnetometer relative to an inertial measurement unit (A), what the magnetic field detected from the magnetometer should look like if regularized to account for distortions (B), and the results of local distortions in the magnetic field at the magnetometer if no regularization is performed.

[0201] Fig.183 is a series of projections of different surgical tools, each of which has a unique top surface to allow the inertial measurement unit processor to intelligently identify the surgical tool to which the IMU is mounted.

[0202] Fig.184is a diagram showing the outline of the IMU housing and depicting Fig.185 The interaction between one of the projections and the bottom cavity of the IMU housing.

[0203] Fig.185 is a flow chart of an exemplary process for preparing a proximal humerus and implanting a humeral component as part of a shoulder replacement procedure using an inertial measurement unit consistent with the present disclosure.

[0204] Fig.186 is a flow chart of an exemplary process for preparing a shoulder socket and implanting a glenoid cup as part of a shoulder replacement surgery using an inertial measurement unit consistent with the present disclosure.

[0205] Fig.187 is a flow chart of an exemplary process for preparing a proximal humerus and implanting a humeral component as part of a reverse shoulder replacement procedure using an inertial measurement unit consistent with the present disclosure.

[0206] Fig.188 is a flow chart of an exemplary process for preparing a shoulder socket and implanting a glenosphere as part of a reverse shoulder replacement procedure using an inertial measurement unit consistent with the present disclosure. Detailed Description

[0207] Exemplary embodiments of the present disclosure are described and illustrated below to cover various aspects of orthopedic surgery, including bone and tissue reconstruction, patient-specific and mass-customized orthopedic implants, gender and race-specific orthopedic implants, cutting guides, trauma protection plates, bone graft cutting and placement guides, and patient-specific instruments. Of course, it will be apparent to those of ordinary skill in the art that the embodiments discussed below are exemplary in nature and can be reconstructed without departing from the scope and spirit of the present invention. However, for clarity and precision, the exemplary embodiments discussed below may include optional steps, methods, and features that are not necessarily within the scope of the present invention that should be recognized by those of ordinary skill.

[0208] Complete anatomy reconstruction

[0209] refer to Figure 1-8Reconstruction of deformed anatomy or portions of anatomy is one of the complex challenges facing health care providers. Loss of anatomy may be the result of a birth condition, tumor, disease, personal injury, or a previous surgical failure. As part of providing treatment for a variety of different patients, health care providers may find it beneficial to reconstruct or construct anatomy to facilitate the treatment of a variety of different conditions, which may include but are not limited to bone fractures / fractures, bone degeneration, bone surgical implant revisions, joint degeneration, and custom instrumentation design. For example, prior art hip reconstruction solutions require the creation of a mirror image of the healthy patient's anatomy, which is difficult to achieve due to the presence of a defective bone structure. Figure 15-19 Due to the naturally occurring asymmetry shown in , the mirror image may not be an accurate reflection of the healthy anatomy.

[0210] The present disclosure provides systems and methods for bone and tissue reconstruction. In order to perform such reconstruction, the system and related methods utilize anatomical images representing one or more people. These images are processed to generate a virtual three-dimensional (3D) tissue model or a series of virtual 3D tissue models simulating the correct anatomical structure in question. Subsequently, the system and related methods are used to generate molds and / or other devices (e.g., fixtures, transplant devices, patient-specific implants, patient-specific surgical guides) for reconstruction surgery.

[0211] like Figure 1 As shown in, an overview of an exemplary system flow begins with receiving input data representing an anatomical structure. The anatomical structure may be included in a partial anatomical structure in a tissue degeneration or tissue loss situation caused by genetics, or the anatomical structure may include a deformed anatomical structure caused by genetics or environmental conditions, or the anatomical structure may include a broken tissue caused by one or more anatomical structure fragments. The anatomical structure input data includes a two-dimensional (2D) image or a three-dimensional (3D) surface representation of the anatomical structure in question, which may, for example, take the form of a surface model or a point cloud. In the case of using 2D images, these 2D images are used to construct a 3D virtual surface representation of the anatomical structure in question. Those skilled in the art are familiar with using 2D images of anatomical structures to construct 3D surface representations. Therefore, for increased simplicity, a detailed explanation of this process has been omitted. For example, the anatomical structure input data may include one or more of an X-ray image, a computed tomography (CT) image, a magnetic resonance image (MRI) or any other imaging data, from which the 3D surface representation of the tissue in question may be generated.

[0212] refer to Fig.50, and Table I, in the case of constructing a virtual 3D skeletal model using X-ray images, it has been found that bone rotation during imaging plays an important role in correctly constructing the model. In other words, if one attempts to compile X-ray images in the presence of bone rotation between images, the X-ray images need to be regularized to account for this bone rotation.

[0213] For example, in the case of the proximal femur, it has been found that 6 and 15 degrees of bone rotation cause significant changes in the measurements extracted from the X-ray images. For example, these measurements include, but are not limited to, proximal angle, head constriction, and intramedullary canal width. As reflected in Table 1, for the same femur imaged by X-ray at zero degrees (i.e., the starting point established by the initial X-ray), a rotation of 6 degrees and a rotation of 15 degrees exhibit different proximal angles, head constriction, and intramedullary canal widths, as calculated using pixels, where each pixel size is approximately 0.29 mm. Specifically, the proximal angle increases with increasing rotation, as does the head constriction, but the intramedullary canal width is not. In this exemplary table, three transverse planes are spaced along the longitudinal axis, each of which corresponds to a position at which the width of the intramedullary canal is measured. As reflected in Table 1, for the same position, the width of the intramedullary canal changes with the angle of rotation. Therefore, as will be discussed in more detail later, when constructing a 3D virtual model of a bone using X-ray images, one must account for rotational deviations in the sense that rotation of the bone occurs during imaging.

[0214] However, it should be understood that the foregoing is an exemplary description of anatomical structures that can be used in exemplary systems and methods, and therefore is in no way intended to limit other anatomical structures to be used in accordance with the methods of the present disclosure. As used herein, tissue includes bone, muscle, ligament, tendon, and any other defined type of structural material having a specific function in a multicellular organism. Therefore, when the exemplary systems and methods are discussed in the context of bones, those skilled in the art should recognize the applicability of the systems and methods to other tissues.

[0215] Reference again Figure 1, the anatomical data input to the system is directed to three modules, two of which involve the processing of anatomical data (complete skeleton reconstruction module, patient-specific module), and the third (abnormal database module) catalogs the anatomical data as part of the database. The first processing module, i.e., the complete skeleton reconstruction module, uses the data received from the statistical atlas module to process the input anatomical data to produce a virtual 3D model of the skeleton in question. The 3D model is a complete, normal reconstruction of the skeleton in question. The second processing module, i.e., the patient-specific module, uses the data received from the complete skeleton reconstruction module to process the input anatomical data to produce one or more molds, fixation systems, implant forming tools and renderings in addition to one or more final bone surgical implants. Rendering refers to the visualization of the reconstructed anatomical structure for feedback on the expected surgical results. More specifically, the patient-specific module is suitable for producing a fully customized device designed to accurately fit the patient-specific anatomical structure, although the patient's anatomical structure deviates seriously from normal. In addition, the patient-specific module uses the virtual 3D reconstructed bone model from the complete skeleton reconstruction module to automatically identify anatomical regions and features (such as fitting regions and / or shapes) for device design parameters. In this way, patient-specific data is used to determine design parameters so that the output instrument and any implants accurately fit the patient's specific anatomical structure. The exemplary use of patient-specific modules will be discussed in more detail later. In order to understand the functions and processes of the system in more detail, the modules of the system will be explained starting with the statistical atlas module below.

[0216] like Figure 1 and 2 As shown in , the statistical atlas module records a virtual 3D model of one or more anatomical structures (e.g., bones) to capture the anatomical variations inherent in a given population. In an exemplary form, the statistical atlas records a mathematical representation of the anatomical features of one or more anatomical structures, which is represented as an average representation and variations around the average representation. By representing the anatomical features as mathematical representations, the statistical atlas allows automatic measurement of anatomical structures and, as will be discussed in more detail below, allows reconstruction of lost anatomical structures.

[0217] In order to extract anatomical variation in the entire common anatomical structure, the input anatomical structure data is compared with a common reference frame in the entire population, which is generally referred to as a template 3D model or an anatomical 3D template model. This template 3D model is intuitively represented as a 3D model on a graphical display, which can be rotated and otherwise intuitively manipulated, but contains a mathematical representation of the anatomical surface features / representations of all anatomical structures in the entire statistical atlas of the tissue in question (i.e., for a given bone, all properties of the bone are shared in the entire population of the statistical atlas generated from the template 3D model). The template 3D model can be a combination of multiple anatomical representations or a single representative case, and can represent the lowest entropy state of the statistical atlas. For each anatomical structure to be added to the statistical atlas (i.e., the input anatomical structure data), an anatomical 3D model is generated, and both the anatomical 3D model and the template 3D model are subjected to a regularization process.

[0218] In the regularization process, the anatomical 3D model is regularized relative to the scale of the template 3D model. The regularization process may include scaling one or both of the anatomical 3D model and the template 3D model to have a common unit scale. After the anatomical 3D model and the template 3D model are regularized, the regularized anatomical 3D model and the template 3D model are given scale invariance so that shape features can be used independently of scale (in this case, meaning size). After the regularization is completed, the two 3D models are processed through a scale space mapping and feature extraction process.

[0219] Scale space mapping and feature extraction is essentially a multi-resolution feature extraction process. Specifically, this process extracts shape-specific features at multiple feature scales. First, multiple anatomical features are selected, each of which represents a feature that exists at a different scale space. Subsequently, for each scale space representation of the selected anatomical feature, model-specific features are extracted. These extracted features are used to retrieve robust (with respect to noise) registration parameters between the template 3D model and the anatomical 3D model. After this multi-resolution feature extraction process, the extracted data is processed by a multi-resolution 3D registration process.

[0220] refer to Figure 2-5 The multi-resolution 3D registration process uses the characteristics of scale space extraction to perform affine registration calculations between the anatomical 3D model and the template 3D model to register the two models. Specifically, the anatomical 3D model and the template 3D model are processed through a rigid registration process. Figure 5As shown in , this rigid registration process effectively aligns the anatomical 3D model with the template 3D model to ensure that the two models are in the same space and there are no posture singularities. In order to align the 3D models, the centroids associated with each model are aligned. In addition, the major axes of each 3D model are aligned so that the main directions of the two 3D models are the same. Finally, the pose differences between the 3D models are minimized by performing iterative closest point calculations.

[0221] After rigid registration, the 3D models are registered using a similarity registration process. This process includes iteratively aligning the template 3D model and the anatomical 3D model by calculating a similarity transformation that best aligns the normal scale features (i.e., ridges) of both the template 3D model and the anatomical 3D model. The iterative similarity alignment algorithm is a variation of the iterative closest point algorithm. In each iteration, the rotation, translation, and scaling ratio between the point pairs are calculated until convergence. The pairing or correspondence between the two point sets is evaluated using a distance query calculated using a Kd-tree or some other spatial segmentation data structure. Specifically, the ridges of the two models are used to perform a process of calculating matching point pairs. In this exemplary description, a ridge refers to a point where a single principal curvature on the 3D model has an extreme value along its curvature line. As part of the process of calculating matching point pairs, points that match each other on the ridges of the 3D models are identified. Next, a similarity transformation calculation process is performed on the ridges of the two 3D models, in which the rotation, translation, and scaling ratio that best align the ridges of the two models are calculated. The transform point process is then performed, which effectively applies the calculated rotation, translation and scaling to the template 3D model spine. The RMS error or distance error is then calculated between each matched set of points, and then the change in the relative RMS error or distance error compared to the previous process is calculated. If the change in the relative RMS error or distance error is within a predetermined threshold, the transform process is performed to apply the final rotation, translation and scaling to the template 3D model.

[0222] The articulated registration process follows the similarity registration process and receives input data from the scale-space feature process. In the scale-space feature process, features are extracted from the template 3D model and the anatomical 3D model at different scales. Each scale is determined by convolving the original anatomical 3D model with a Gaussian smoothing function.

[0223] The purpose of the articulated registration process is to match the "n" scale-space features of the template 3D model with the "m" scale-space features calculated on the anatomical 3D model. The difference between the number of features detected on the template 3D model and the anatomical 3D model is caused by anatomical variability. The difference in the number of features detected may cause many associations between the template 3D model and the anatomical 3D model. Therefore, a two-way mutual feature matching is performed to accommodate this variability and to achieve accurate matching between all mutual features. Specifically, a feature set is calculated on the template 3D model in scale space. In this exemplary process, a feature set is a connected point set representing a prominent anatomical structure (e.g., an acetabular cup in the pelvis, a spinous process in the lumbar vertebra). Similarly, a feature set is calculated on the anatomical 3D model in scale space. The matching feature pair process uses shape descriptors (e.g., curvature, shape index, etc.) to match the feature set calculated on the template 3D model to the feature set on the anatomical 3D model. The result of this process is the "nm" mapping of the feature set between the template 3D model and the anatomical 3D model. If necessary, a regrouping process is performed to regroup the matched feature sets into a single feature set (for example, if the acetabular cup is detected to be two pieces, this process regroups the two pieces into a single feature set). Then, a calculation process is performed to calculate the correspondence between each point in the matched feature sets on the template 3D model and the anatomical 3D model. Subsequently, an affine calculation transformation process is performed to calculate the rotation, translation, and shearing that transforms each matched feature set on the template 3D model into its corresponding feature set on the anatomical 3D model. Subsequently, the template 3D model is transformed using the calculated affine transformation parameters (i.e., rotation, translation, and shearing). Finally, a rigid alignment process is performed to align each matched feature set on the template 3D model and the anatomical 3D model.

[0224] The non-rigid registration process, which is performed after the articulated registration process and the normal scale feature process, includes matching all surface vertices on the template 3D model to the vertices of the anatomical 3D model and calculating the initial correspondence. The correspondence is then used to calculate the deformation field that moves each vertex on the template 3D model to the matching point on the anatomical 3D model. Matching is performed between vertices in the same category (i.e., scale space feature vertices, normal scale feature vertices, or non-feature vertices). In the case of the normal scale feature process, shape features on the template 3D model and the anatomical 3D model are calculated in the original scale space (ridges), meaning the original input model.

[0225] Specifically, as part of the non-rigid registration process, scale-space features are calculated on the template 3D model (TMssf) and the anatomical 3D model (NMssf). Each feature set on the template 3D model and the anatomical 3D model is grown using "k" neighboring points. An alignment process is used for the template 3D model scale-space features to match its corresponding features on the anatomical 3D model. Considering two point clouds, namely reference (X) and translation (Y), the goal is to repeatedly align the two point clouds to minimize the overall error measure under the constraints of minimum relative root mean square error and maximum angle threshold. A realignment process is performed using iterative nearest points at normal scale to align the feature set on the template 3D model with the matching set on the anatomical 3D model. After realignment, point correspondences between points in each feature set on the template 3D model and the matching feature set on the anatomical 3D model are calculated. The matching points on the anatomical 3D model should have a surface normal direction close to the template 3D model points. The output is forwarded to the calculation deformation field step.

[0226] In parallel with the scale-space feature calculation process, the non-feature points of the template 3D model (TMnfp) and the anatomical 3D model (NMnfp) or the remaining point set on the template 3D model surface that does not belong to the scale-space feature or the normal scale feature are processed according to the correspondence calculation to calculate the point correspondence between the non-feature points on the template 3D model and the non-feature points on the anatomical 3D model. The matching points on the new model should have a surface normal direction close to the template model points. The output is forwarded to the calculation deformation field step.

[0227] In addition, in parallel with the scale-space feature calculation process, the normal scale features (i.e., ridges) on the template 3D model (TM nsf) are aligned with the normal scale features (i.e., ridges) on the anatomical 3D model (NM nsf) using AICP. AICP is a variation of iterative closest point calculation, where in each iteration, the translation, rotation, and scaling ratio between the matched point sets are calculated. After the alignment process, the correspondence process is performed.

[0228] The outputs from the scale-space feature computation process, the correspondence process, and the alignment process are subjected to a deformation process, in which a deformation field is calculated to move each point on the template 3D model towards its matching point on the anatomical 3D model.

[0229] The output of the non-rigid registration process is subjected to a relaxation process so that the vertices of the template 3D model mesh are moved closer to the surface of the anatomical 3D model after the multi-resolution registration step and the output model is smoothed. Specifically, the template 3D model (TM ns) in the regular space and the anatomical 3D model (NM ns) in the regular space are processed by corresponding calculations to calculate the vertices closest to the anatomical 3D model on the template 3D model using a spherical search algorithm with normal constraints. This calculation uses the closest vertices of the two models to generate corresponding vectors from each vertex in the template 3D model and its matching vertex in the anatomical 3D model, which may generate more than one matching point from the anatomical 3D model. Using the matching point of each vertex on the template 3D model, the weighted average of the matching points on the anatomical 3D model is calculated based on the Euclidean distance from the point and the matching point. At this point, the template 3D model is updated using the weighted average so that each point on the template 3D model is moved using the calculated weighted average distance. After the weighting process is calculated, a relaxation process is performed on each point on the template model in order to find the closest point on the surface of the anatomical 3D model and move it to that point. Finally, a smoothing operation is performed on the deformed template 3D model to remove noise. The resulting registered 3D model (i.e., the template and the anatomical 3D model) is then subjected to a free deformation process.

[0230] The free deformation process uses the surface of the anatomical 3D model to deform the surface of the template 3D model. More specifically, the surface of the template 3D model is repeatedly moved on a weighted point-to-point basis using matching points on both the template 3D model surface and the anatomical 3D model surface.

[0231] refer to Figure 2 and 6 , after the free deformation process, a correspondence calculation process is performed on the anatomical 3D model to determine the deviation between the anatomical 3D model and the deformed template 3D model. This correspondence calculation process refines the template 3D model from the free deformation step to perform a final match between the template deformed 3D model and the selected landmark positions on the deformed anatomical 3D model. In this way, the correspondence calculation process calculates and records the variation in size and shape between the 3D models, which is recorded as deviations around the average model. The output of this correspondence calculation process is the addition of the regularized anatomical 3D model and the modified template 3D model that has been updated to take into account the variation in the anatomical 3D model. In other words, Figure 2 The output of the process outlined in is a regularized anatomical 3D model that has been modified to have properties (e.g., point correspondences) consistent with the modified template 3D model to facilitate complete anatomical structure reconstruction (e.g., complete skeletal reconstruction).

[0232] refer to Figure 1 and 7 , the input from the statistical atlas module and the anatomical structure data is directed to the full anatomical structure reconstruction module. For example, the anatomical structure in question can be a bone or multiple bones. However, it should be noted that the anatomical structure outside the bone can be reconstructed using the exemplary hardware, processes and techniques described herein. In an exemplary form, the full anatomical structure reconstruction module can receive input data about a partial, deformed or broken pelvis. The input anatomical data includes a two-dimensional (2D) image or a three-dimensional (3D) surface representation of the anatomical structure in question, which can, for example, take the form of a surface model or a point cloud. In the case of using 2D images, these 2D images are used to construct a 3D surface representation of the anatomical structure in question. Those skilled in the art are familiar with using 2D images of anatomical structures to construct 3D surface representations. Therefore, for the sake of brevity, the detailed explanation of this process is omitted. For example, the input anatomical data can include one or more of an X-ray image, a computed tomography (CT) scan, a magnetic resonance image (MRI) or any other imaging data that can produce a 3D surface representation. As will be discussed in more detail below, this input anatomical data can be used, without limitation, for: (1) identifying the starting point for the closest statistical atlas 3D skeletal model; (2) alignment using a set of 3D surface vertices; and (3) a final relaxation step for the reconstructed output.

[0233] like Figure 7 As shown in , the input anatomical data (e.g., a skeletal model of a patient) is used to identify the anatomical model (e.g., a skeletal model) in the statistical atlas that most closely resembles the anatomical structure of the patient in question. This step is Figure 3 is depicted as finding the closest bone in the atlas. In order to first identify the bone model in the statistical atlas that is most similar to the patient's bone model, the patient's bone model is compared with the bone model in the statistical atlas using one or more similarity metrics. The result of the initial similarity metric is a bone model selected from the statistical atlas as an "initial guess" for the subsequent alignment step. The alignment step uses the selected atlas bone model (i.e., the initial guess bone model) to align the patient's bone model so that the output is a patient's bone model aligned with the atlas bone model. After the alignment step, the shape parameters of the aligned "initial guess" are optimized to make the shape match the patient's bone shape.

[0234] The shape parameters, in this case from a statistical atlas, are optimized so as to minimize the error between the reconstruction and the patient's bone model using areas of non-deformed or existing bone. Varying the shape parameter values ​​allows for the representation of different anatomical shapes. This process is repeated at different scales until convergence of the reconstructed shape is achieved (possibly measured as the relative surface change between iterations or the maximum number of iterations allowed).

[0235] A relaxation step is performed to deform the optimized tissue to best match the original patient 3D tissue model. Consistent with the exemplary case, the lost anatomical structures from the reconstructed pelvic model output from the convergence step are applied to the patient-specific 3D pelvic model, thereby generating a patient-specific 3D model of the patient's reconstructed pelvis. More specifically, the surface points on the reconstructed pelvic model are directly relaxed (i.e., deformed) onto the patient-specific 3D pelvic model so that the reconstructed shape best matches the patient-specific shape. The output of this step is a fully reconstructed patient-specific 3D tissue model that represents what the patient's normal / complete anatomical structure should be.

[0236] refer to Figure 1 , an abnormality database is used as data input to train the defect classification module. Specifically, the abnormality database contains data specific to abnormal anatomical features, including anatomical structure surface representations and related clinical and demographic data.

[0237] refer to Figure 1 and 8 , a fully reconstructed patient-specific 3D tissue model representing normal / intact tissue and input anatomical data (i.e., a 3D surface representation or data from which a 3D surface representation can be generated) representing abnormal / incomplete tissue from an abnormal database are input to a defect classification module. The anatomical data from the abnormal database can be a partial anatomical structure in a tissue degeneration situation or a tissue loss caused by genetics, or such an anatomical structure can be a deformed anatomical structure caused by genetic or environmental conditions (e.g., surgical revision, disease, etc.), or such an anatomical structure can be a fragmented tissue caused by the fragmentation of one or more anatomical structures. For example, the input anatomical data can include one or more of an X-ray image, a computed tomography (CT) scan, a magnetic resonance image (MRI), or any other imaging data from which a 3D surface representation can be generated.

[0238] The defect classification module extracts multiple abnormal 3D surface representations from an abnormal database coupled to a normal 3D representation of the anatomical structure in question to produce a quantitative defect classification system. The defect classification system is used to generate a "template" for each defect class or cluster. More generally, the defect classification module classifies anatomical defects into categories consisting of closely related defects (i.e., those with similar shapes, clinical, appearance, or other characteristics) to facilitate the generation of health care solutions to address these defects. The defect classification module uses software and hardware to automatically classify defects as a means of eliminating or reducing the difference between preoperative data and visual data observed by an intraoperative observer. Traditionally, preoperative radiographs are obtained as a means of qualitatively analyzing the necessary degree of anatomical reconstruction, but this produces preoperative plans that are at best arbitrary. Currently, the intraoperative observer makes the final determination of the degree of anatomical defect and in many cases concludes that the preoperative plan that relies on radiographs is flawed or incomplete. As a result, the defect classification module improves existing classification systems by reducing the inter-observer and intra-observer variation associated with defect classification and providing a quantitative measure for classifying new defect cases.

[0239] As part of the defect classification module, the module can take as input one or more classification types for use as an initial state. For example, in the case of the pelvis, the defect classification module can use as input defect characteristics corresponding to the bone defect classification structure of D'Antonio et al. of the American Academy of Orthopaedic Surgeons (AAOS). This structure includes the following four different types: (1) Type I, corresponding to segmental bone defects; (2) Type II, corresponding to cavity bone defects; (3) Type III, corresponding to combined segmental and cavity bone defects; and (4) Type IV, corresponding to pelvic discontinuity. Optionally, the defect classification module can be used in Fig.10The defect classification module can be programmed using the Paprosky bone defect classification structure illustrated for the pelvis in FIG. This structure includes three different categories as follows: (1) Type I, corresponding to a supporting rim without osteolysis; (2) Type II, corresponding to a distorted hemisphere with an intact supporting column and less than 2 cm of superomedial or lateral migration; and (3) Type III, corresponding to more than 2 cm of superior migration and severe sciatic lysis with Kohler's line broken or intact. In addition, the defect classification module can be programmed using a modified Paprosky bone defect classification structure. This structure includes 6 different categories as follows: (1) Type 1, corresponding to a supportive rim with no component migration; (2) Type 2A, corresponding to a distorted hemisphere with less than 3 cm of superior migration; (3) Type 2B, corresponding to a larger hemisphere distortion with less than 1 / 3 of the rim circumference, and the dome is still supportive; (4) Type 2C, corresponding to an intact rim with medial migration to Kohler's line, and the dome is still supportive; (5) Type 3A, corresponding to more than 3 cm of superior migration and severe schizolysis with intact Kohler's line; and (6) Type 3B, corresponding to more than 3 cm of superior migration and severe schizolysis with a broken Kohler's line, and a rim defect exceeding half of the circumference. Using the output classification type and parameters, the defect classification module compares the anatomical data with the reconstructed data to identify which classification type the anatomical data most closely resembles, thereby corresponding to the resulting assigned classification.

[0240] As an initial step, the step added to the statistical atlas is included in the generation of correspondence between the normal atlas 3D skeleton model and the abnormal 3D skeleton model. More specifically, the 3D skeleton models are compared to distinguish the bones in the normal 3D model that are not present in the abnormal 3D model. In an exemplary form, the missing / abnormal bones are identified by comparing the points on the surface of each 3D skeleton model and generating a list of discrete points on the surface of the normal 3D skeleton model that are not present on the abnormal 3D skeleton model. This system can also record and list (i.e., identify) those surface points shared between the two models, or summarize and point out that, unless recorded as points that are not present on the abnormal 3D skeleton model, all other points are present in both skeleton models (i.e., on both normal and abnormal skeleton models). Therefore, the output of this step is a list of the abnormal 3D skeleton model corresponding to the statistical atlas and the features (points) from the normal atlas 3D skeleton model, and the list indicates whether the features (points) exist or are missing in the abnormal 3D skeleton model.

[0241] After the correspondence between the normal atlas 3D skeleton model (generated from the complete skeleton reconstruction module) and the abnormal 3D skeleton model (generated from the anatomical data of the input) is generated, the loss / abnormal area from the abnormal 3D skeleton model is positioned on the normal atlas 3D skeleton model. In other words, the normal atlas 3D skeleton model is compared with the abnormal 3D skeleton model to identify and record the bones lost from the abnormal 3D skeleton model that exist in the normal atlas 3D skeleton model. Positioning can be carried out in a variety of ways, including but not limited to curvature comparison, surface area comparison and point cloud area comparison. Finally, in an exemplary form, the loss / abnormal bone is positioned as a group of boundary points of the geometric boundary of the identification loss / abnormal area.

[0242] Using the boundary points, the defect classification module extracts features from the missing / abnormal region using the input clinical data. In an exemplary form, the extracted features may include shape information, volume information, or any other information used to describe the overall characteristics of the defect (i.e., missing or abnormal) region. These features may be refined based on existing clinical data such as ongoing defect classification data or patient clinical information (demographic information, medical history, etc.) that is not necessarily related to anatomical features. The output of this step is a mathematical descriptor representing the defect region, which is used in subsequent steps to group similar tissue (e.g., bone) deformations.

[0243] The mathematical descriptors are clustered or grouped based on statistical analysis. Specifically, the descriptors are statistically analyzed and compared with other descriptors from other patients / cadavers to identify unique defect classes within a given population. Obviously, this classification is premised on multiple descriptors from multiple patients / cadavers, which can refine the classification and identification of discrete groups as the number of patients / cadavers increases. The output from this statistical analysis is a set of defect classes, which are used to classify newly input anatomical data and determine the number of templates.

[0244] The output of the defect classification module is directed to the template module. In an exemplary form, the template module includes data specific to each defect classification identified by the defect classification module. For example, for a given defect classification, each template includes a surface representation of the defective bone, the location of the defect, and measurements associated with the defective bone. The template data can take the form of surface shape data, point cloud representation, one or more curvature distributions, dimensional data, and physical quantity data. The output from the template module and the statistical atlas is used by the mass customization module to design, test, and allow the manufacture of mass customized implants, fixtures, instruments, or molds. Exemplary uses of the mass customization module will be discussed in more detail later.

[0245] Patient-Specific Reconstructive Implants

[0246] refer to Figure 1 and 20 , described is the exemplary process and system for producing patient-specific bone surgery implant guide and relevant patient-specific bone surgery implant for partial, deformed and / or broken anatomical structure patient.For the purpose of example discussion, the total hip replacement for the patient with partial anatomical structure will be described.However, it should be understood that the exemplary process and system are applicable to any bone surgery implant suitable for patient-specific customization when there is incomplete or deformed anatomical structure.For example, the exemplary process and method are applicable to shoulder replacement and knee replacement when there is bone degeneration (partial anatomical structure), bone deformation or broken bone.Therefore, although what is discussed hereinafter is hip implant, those skilled in the art will appreciate the applicability of the system and process to other bone surgery implants, guides, tools, etc. for original bone surgery or bone surgery revision surgery.

[0247] Pelvic discontinuity is a unique form of bone loss most commonly associated with total hip arthroplasty (THA), in which osteolysis or acetabular rupture can cause the upper side of the pelvis to become separated from the lower side. The amount and severity of bone loss, as well as the bio-ingrowth potential of the implant, are some of the factors that can influence the treatment options for a particular patient. In cases of severe bone loss and loss of pelvic integrity, a custom three-flange cup can be used. First introduced in 1992, this implant has several advantages over existing cages. It can provide stability for pelvic discontinuities, eliminate the need for structural implantation and intraoperative contouring of the cage, and promote osseointegration of the construct into the surrounding bone.

[0248] Regardless of the context, and regardless of whether the patient's partial, deformed, and / or fragmented anatomy is in question, exemplary systems and processes for producing patient-specific implants and / or guides utilize the aforementioned exemplary systems and processes for 3D bone model reconstruction (see Figure 1-7 and its aforementioned exemplary discussion) to generate a three-dimensional model of the patient's reconstructed anatomical structure. More specifically, in the case of a total hip replacement involving a discontinuous pelvis, the exemplary patient-specific system utilizes the patient's pelvic data to generate a 3D model of the patient's complete pelvis, which is body side specific (right or left). Therefore, for the sake of brevity, the discussion of the system and process of using the patient's anatomical structure data for a partial anatomical structure and generating a 3D reconstruction model of the patient's anatomical structure is omitted. Therefore, the description of the process and system for generating a patient-specific bone surgical implant guide and a related patient-specific bone surgical implant for a patient with a partial, deformed and / or broken anatomical structure will be described after the three-dimensional reconstruction model is formed.

[0249] Specific reference Figure 20-22 and 27, after generating the patient-specific reconstructed 3D skeletal model of the pelvis and femur, both the incomplete patient-specific 3D skeletal model (for the pelvis and femur) and the reconstructed 3D skeletal model (for the pelvis and femur) are used to generate patient-specific bone surgery implants and patient-specific placement guides for implants and / or their fasteners. Specifically, the step of extracting the defect shape includes generating correspondences between the patient-specific 3D model and the reconstructed 3D model (correspondences between pelvic models and correspondences between femur models, but not between a femur model and a pelvic model). More specifically, the 3D models are compared to identify which bone in the reconstructed 3D model does not exist in the patient-specific 3D model. In an exemplary form, missing / abnormal bones are identified by comparing points on each 3D model surface and generating a list of discrete points on the reconstructed 3D model surface that do not exist on the patient-specific 3D model. The system may also record and list (i.e., identify) those surface points that are common between the two models, or generally note that unless otherwise recorded as not being present on the patient-specific 3D model, other points are common to both models (i.e., on both the reconstructed and patient-specific 3D models).

[0250] refer to Fig.21 , after generating a correspondence between the reconstructed 3D model (generated from the complete skeleton reconstruction module) and the patient-specific 3D model (generated from the input anatomical data), the missing / abnormal areas from the patient-specific 3D model are positioned on the reconstructed 3D model. In other words, the reconstructed 3D model is compared with the patient-specific 3D model to identify and record the bones that are missing from the patient-specific 3D model and are present in the reconstructed 3D model. Positioning can be performed in a variety of ways, including but not limited to curvature comparison, surface area comparison, and point cloud area comparison. Finally, in an exemplary form, the missing / abnormal bones are positioned, and the output includes two lists: (a) a first list that identifies the vertices of bones that are not present or deformed in the patient-specific 3D model corresponding to the reconstructed 3D model; and (b) a second list that identifies the vertices of bones that are also present and normal in the patient-specific 3D model corresponding to the reconstructed 3D model.

[0251] refer to Fig.21 , 22 and 27, after the defect shape extraction step, the implant site step is performed. Input two vertex lists from the defect shape extraction step and a 3D model of a normal bone (e.g., pelvis, femur, etc.) from a statistical atlas (see Figure 1 and 2And its aforementioned exemplary discussion) to distinguish the fixed position for femur or pelvic implant.More specifically, automatically select fixed position (i.e. implant site) so that each fixed position is located at the place where the patient has residual bone.On the contrary, fixed position is not selected in the defect area of ​​the patient's residual bone.In this way, the selection of fixed position does not depend on final implant design / shape.The selection of fixed position can be automatically carried out using shape information and statistical atlas position.

[0252] like Fig.21 As shown in, after the implant site step, the next step is to produce patient-specific implant parameters.In order to complete this step, input implant parameterized template, it defines described implant by a group of multiple parameters that are enough to define the potential shape of implant.For example, in the case of pelvic reconstruction with replacement / increase missing or degenerated acetabulum, implant parameterized template comprises the angle parameter for replacing the orientation of acetabular cup and the depth parameter for accommodating the dimension of femoral head.Other parameters of acetabular implant may include but are not limited to acetabular cup diameter, towards, flange position and shape, position and orientation of fixing bolt.In the case of porous implant, the position and structural characteristics of pore should be included.For example, in the case of femoral reconstruction with replacement / increase missing or degenerated femur, implant parameterized template comprises the cross-sectional analysis of angle parameter, neck length, head constriction, proximal angle and femur outside and intercondylar passage for replacing the orientation of femoral head.Those skilled in the art will appreciate that the selected parameter for defining the potential shape of implant will change with the anatomical structure to be replaced or supplemented. Therefore, an exhaustive list of parameters sufficient to define the potential shapes of an implant is impractical. Fig. 22 As shown in, the radius of the acetabular cup can be obtained by utilizing the 3D pelvic bone model of reconstruction, the pelvic bone containing the upper ridge around the acetabular cup is identified, and the orientation of the acetabular cup relative to the residual pelvic bone is identified. In addition, the parameters can be refined taking into account the implant site so that the implant best / better fits the patient-specific anatomical structure.

[0253] After finally determining the set of multiple parameters sufficient to define the potential shape of the implant, the design of the implant is performed. More specifically, an initial iteration of the entire implant surface model is constructed. The initial iteration of the entire implant surface model is defined by a combination of patient-specific contours and contours estimated for the implant area. The estimated contours are determined from the reconstructed 3D bone model, the lost anatomical bones, and the features extracted from the reconstructed 3D bone model. These features and the location of the implant site can be automatically determined and are used to determine the overall shape of the implant, as in Fig. 22 An acetabular cup implant is depicted in FIG.

[0254] Reference again Fig. 20, according to customization (i.e. patient-specific) planning process, the initial iteration of whole implant surface model is processed.This customization planning process can include the input from surgeon and engineer as a part of iteration review and design process.Specifically, surgeon and / or engineer can review whole implant surface model and the 3D bone model of reconstruction, to determine whether need to make change to whole implant surface model.This review can cause the iteration of whole implant surface model, until reaching unanimity between engineer and surgeon.The output coming from this step is the surface model of final implant, and it can take the form of CAD file, CNC machine code or rapid manufacturing instruction, to produce final implant or solid model.

[0255] refer to Fig. 20 , 22 And 23, simultaneously with or after the design of patient-specific bone surgical implant, is the design of patient-specific placement guide.In the case of acetabular cup implant as discussed in the exemplary form above, one or more surgical surgical instruments can be designed and manufactured to assist in placing the patient-specific acetabular cup.After the patient-specific implant is designed to have the size and shape that matches the residual bone, the contour and shape of the patient-specific implant can be utilized and incorporated as a part of the placement guide.

[0256] In exemplary form, acetabulum placement guide comprises 3 flanges, which are configured to contact ilium, ischium and pubic surface, wherein the 3 flanges are interconnected by ring. In addition, the flange of the placement guide can have shape, size and profile consistent with acetabular cup implant, so that the placement guide has the consistent position planned for acetabular cup implant. In other words, the acetabular placement guide is molded as the negative shape feature of patient's anatomical structure (partial surface of ilium, ischium and pubic bone), just like acetabular cup implant, so that the placement guide is perfectly fitted on patient's anatomical structure. But the significant difference of implant guide and implant is that it comprises one or more fixing holes, and the fixing hole is configured to guide drilling and / or place fastener. In exemplary form, the placement guide is included in the hole that has determined size and orientation on the basis of image analysis (such as micro CT), to ensure the correct orientation of other guides (such as dowels) used when any drill bit or acetabular cup implant is fixed to residual pelvic bone. The number and orientation of the hole change with residual bone, and residual bone also affects the shape of acetabular cup implant. Fig.23Describe the example of the patient-specific placement guide used in total hip replacement surgery. In another case, the guide can be manufactured to fit it in the implant and only guide the direction of the fixing bolt. In this form, the guide is molded as the negative shape of the implant so that it can be placed directly on the implant. However, the incorporation of at least a portion of the size, shape and profile of the patient-specific reconstruction implant is a subject to be handled later, no matter which bone the target bone to which the patient-specific implant will be coupled is.

[0257] Utilization of the exemplary systems and methods described herein can provide a wealth of information that can result in greater accuracy in bone surgical placement, better anatomical integration, and the ability to calculate true angles and plane orientations from a reconstructed three-dimensional model prior to surgery.

[0258] Manufacturing customized implants using mass-customizable components

[0259] refer to Fig.26 , describes an exemplary process and system for producing customized bone surgery implants using mass-customizable components. For the purpose of exemplary discussion, a total hip replacement surgery for a patient with a severe acetabular defect will be described. However, it should be understood that the exemplary process and system are applicable to any bone surgery implant suitable for mass customization in the presence of an incomplete anatomical structure.

[0260] Serious acetabular defect needs special surgery and implant assembly to repair.A kind of method is customized three flanges, and it is fully customized implant, is made of acetabular cup and three flanges attached to ilium, ischium and pubis.Contrary to described exemplary process and system, the three flange implants of prior art comprise single complex assembly, and it is cumbersome to make and needs to redesign whole implant (i.e. fully patient-specific) for each case.Described exemplary process and system produce a kind of customized three flange implant, and it also utilizes the assembly that can be customized on a large scale in a modular manner except fully customized assembly, to allow customized assembly and porosity.

[0261] The pre-planning step that conforms to the exemplary process is carried out to determine the orientation of the three flanges relative to the acetabular cup, the flange contact position, and the orientation and size of the acetabular cup. This pre-planning step is carried out according to the "patient-specific implant" discussion immediately before this part. For example, according to the implant site step and as discussed in the previous part, the specific location of the implant fixation is determined using the data input in its preface. By recalling that as part of the implant site step, the two vertex lists from the extraction defect shape step and the 3D model of the normal pelvis from the statistical atlas (see Figure 1 and 2And its aforementioned exemplary discussion) input, to distinguish the fixed position of customized three flanges. More specifically, the fixed position (i.e. implant site) is selected to be located at each of the patient's residual bones. In other words, the fixed position is not selected in the defective area of ​​the patient's residual pelvic bone. In this way, the selection of the fixed position does not depend on the final implant design / shape.

[0262] After determining the fixed position, use " patient-specific implant " before just following this part to discuss and design three flange assemblies (i.e. flanges).Described flange is designed to be oriented with respect to displacement acetabular cup, makes described acetabular cup orientation provide acceptable coupling function.In addition, the contact surface profile of flange matches the pelvic anatomical structure of patient, because the contact surface of three flanges is modeled into " negative shape " of the bone surface of pelvis. Fig.23 An exemplary process for rapid prototyping flanges (or using conventional computer numerical control (CNC) equipment) utilizes the final step of the process depicted in Figure 17. After the flanges are manufactured, further machining steps may be performed to provide cavities within which porous material may be added to the three flanges.

[0263] One part of the three-flange system that does not have to be a custom assembly is the acetabular cup assembly. In this exemplary process, a batch of acetabular cups are first manufactured, and they provide a basis for building a three-flange system thereon. These "empty" cups are kept in inventory for use when needed. If the acetabular cup is required to have a specific porosity, mechanical features that allow the porous material to be pressure-fitted into the acetabular cup are added to the acetabular cup. Alternatively, if the acetabular cup is required to have a specific porosity, the acetabular cup can be coated with one or more porous coatings.

[0264] After the empty cup is formed and any porosity issues discussed above are resolved, the acetabular cup is made patient-specific by machining the acetabular cup to receive the flange. Specifically, using a virtual model of the flange, the system software builds a virtual locking mechanism for the flange, which is converted into machine code to machine the locking mechanism into the acetabular cup. These locking mechanisms allow the acetabular cup to be fastened to the flange so that when the flange is fixed to the residual bone of the patient, the acetabular cup is correctly oriented relative to the residual pelvic bone. This machining can use conventional CNC equipment to form the locking mechanism in the empty cup.

[0265] After manufacturing the lock mechanism as part of the empty cup, the flange is fixed to the acetabular cup using the engagement between the lock mechanisms. The three-flange set (i.e., the final implant) is subjected to an annealing process to promote strong bonding between the components. After the three-flange implant is annealed, a sterilization step is performed, followed by appropriate packaging to ensure a sterile environment for the three-flange implant.

[0266] Production of mass-customized implants

[0267] refer to Fig.28 , describes the exemplary process and system of the bone surgery implant guide and the related bone surgery implant of mass customization produced for the patient with partial, deformed and / or broken anatomical structure.For the purpose of exemplary discussion, the total hip arthroplasty for the patient who needs primary joint replacement will be described.However, it should be appreciated that the exemplary process and system are applicable to any bone surgery implant and guide that are suitable for mass customization when there is incomplete anatomical structure.For example, the exemplary process and system are applicable to the shoulder replacement and knee replacement in which there is bone degeneration (partial anatomical structure), bone deformation or broken bone.Therefore, although what is discussed hereinafter is hip joint implant, those skilled in the art should understand the applicability of the system and process to other bone surgery implants, guides, tools, etc. for primary bone surgery or bone surgery revision surgery.

[0268] The exemplary process utilizes input data from both macroscopic and microscopic perspectives. Specifically, the macroscopic perspective includes determining the overall geometry of the bone surgical implant and the corresponding anatomical structure. In contrast, the microscopic perspective includes estimating the shape and structure of the cancellous bone and its porosity.

[0269] The macroscopic view includes a database in communication with a statistical atlas module that records virtual 3D models of one or more anatomical structures (e.g., bones) to capture the anatomical variability inherent in a given population. In an exemplary form, the statistical atlas records mathematical representations of anatomical features of one or more anatomical structures expressed as an average representation for a given anatomical population and variations around the average representation. Figure 2 Discussion of the aforementioned atlas and how one can add anatomical structures to an atlas for a given population. Output from the atlas is directed to the automatic landmark finding module and the surface / shape analysis module.

[0270] The automatic landmark search module uses input from the statistical atlas (such as areas that may contain specific landmarks) and local geometric analysis to calculate anatomical landmarks for each anatomical structure instance in the statistical atlas. This calculation is specific to each landmark. For example, the approximate shape of the area is known, and the position of the landmark being searched relative to the local shape feature is known. For example, the positioning of the medial epicondyle point of the distal femur is achieved by refining the search based on the approximate position of the medial epicondyle point in the statistical atlas. Therefore, it is known that the medial epicondyle point is the innermost point in the search window, so a search for the innermost point is performed for each bone model in the medial epicondyle region defined in the statistical atlas, where the output of the search is identified as the landmark of the medial epicondyle point. After automatically calculating anatomical landmarks for each virtual 3D model in the statistical atlas cluster, the virtual 3D model of the statistical atlas is directed to the feature extraction module together with the shape / surface analysis output.

[0271] The shape / surface output comes from the shape / surface module, which also receives input from the atlas. In the case of the shape / surface module, the virtual 3D model within the atlas is analyzed to obtain shape / surface features that are not covered by the automatic landmark finding. In other words, features that correspond to the overall 3D shape of the anatomical structure but are not features defined in the previous automatic landmark finding step are also calculated. For example, curvature data is calculated for the virtual 3D model.

[0272] The output from the surface / shape analysis module and the automatic landmark finding module is directed to the feature extraction module. Using a combination of landmarks and shape features, mathematical descriptors (i.e., curvature, dimension) related to implant design are calculated for each instance in the atlas. These descriptors are used as input for the clustering process.

[0273] On the basis of statistical analysis, described mathematical descriptor is clustered or grouped.Specifically, described descriptor is carried out statistical analysis and compared with other descriptors coming from residual anatomical structure colony, to identify the group (anatomical structure) with similar characteristics in colony.Obviously, the premise of this clustering is to cross the multiple descriptors coming from multiple anatomical structures of whole colony.When presenting the new instance that does not exist in initial clustering to clustering, the cluster of output is refined to represent new colony better.The output coming from this statistical analysis is the limited number of implants (comprising implant family and size) covering all or most anatomical colonies.

[0274] For each cluster, the parameterization module extracts the mathematical descriptor in the cluster. The mathematical descriptor forms the parameters (such as CAD design parameters) for the final implant model. The mathematical descriptor extracted is sent to the implant surface generation module. This module is responsible for converting the mathematical descriptor into a surface descriptor, for each cluster to produce the 3D virtual model of the anatomical structure. The 3D virtual model supplements the microscopic perspective before stress testing and implant manufacturing.

[0275] In microscopic perspective, for each anatomical structure of a given colony, data indicating structural integrity are obtained. In an exemplary form, the data may include micro CT data for a skeleton, which provides structural information for cancellous bone. More specifically, the micro CT data may include images of the skeleton in question (multiple micro CT images of multiple bones of the entire colony). These images are then segmented by extracting the cancellous bone structure module to extract the three-dimensional geometric structure of cancellous bone and to generate a virtual 3D model for each bone in the colony. The 3D virtual model obtained is input to the hole size and shape module. As in Fig.84 In the figure, described 3D virtual model comprises hole size and shape information, and it is assessed by hole size and shape module, to determine the hole size and size of cancellous bone.This assessment can be used for analyzing the hole size and shape in the intramedullary canal of bone, so that the dry coating of femoral implant can be processed or otherwise processed to show porous appearance, to promote the integration between the residual bone of femur and the femoral implant.The output coming from this module is combined with the 3D virtual model that produces module output from implant surface, and is directed to virtual stress test module.

[0276] The stress test module will merge the implant porosity data from the hole size and shape module with the implant shape data from the implant surface generation module, to determine the final implant shape model and property. For example, the shape and property include providing a porous coating that roughly matches the cancellous bone porosity of the skeleton in question for the final implant model. Once incorporated with the shape and property, the final implant model undergoes virtual stress testing (finite element and mechanical analysis) to verify the functional quality of the model. In the case where the functional quality is unacceptable, the parameters defining the implant shape and porosity are modified until acceptable performance is obtained. Assuming that the final implant model meets the criterion of stress testing, the final implant model is used to generate the machine instructions (which can be further refined by manufacturing process known to those skilled in the art) that the virtual model is transformed into the necessary real implant. In an exemplary form, the machine instructions can include rapid manufacturing machine instructions to manufacture the final implant by a combination of rapid prototyping process (to correctly capture porous structure) or traditional manufacturing and rapid prototyping.

[0277] The creation of sex / race-specific hip implants

[0278] refer to Figure 29-84 , described exemplary process and system for producing gender and / or race specific implant.For the purpose of exemplary discussion, the total hip replacement surgery of the patient who needs primary joint replacement will be described.However, it should be understood that the exemplary process and system are applicable to any bone surgery implant suitable for customization.For example, the exemplary process and system are applicable to shoulder replacement and knee replacement and other primary joint replacement surgeries.Therefore, although what is discussed hereinafter is hip implant, those skilled in the art should understand the applicability of the system and process to other bone surgery implants, guides, tools, etc. for primary bone surgery or bone surgery revision surgery.

[0279] The hip joint is formed by the head of the femur and the acetabulum of the pelvis. The anatomy of the hip joint makes it one of the most stable joints in the body. Stability is provided by the rigid socket-and-socket construction. The femoral head is almost spherical in its articulating portion, forming two-thirds of a ball. Data show that the femoral head diameter is smaller in women than in men. In a normal hip, it is assumed that the center of the femoral head is exactly aligned with the center of the acetabulum, and this assumption is used as the design basis for most hip systems. However, the depth of the natural acetabulum is not sufficient to cover all of the natural femoral head. The almost rounded portion of the femoral head is quasi-spherical rather than spherical, as the uppermost portion is slightly flattened. This quasi-spherical shape causes the load to be distributed in a ring-like pattern around the upper pole.

[0280] The geometric center of the femoral head is traversed by the three axes of the joint: the horizontal axis, the vertical axis, and the anterior-posterior axis. The femoral head is supported by the femoral neck, which is attached to the shaft. The axis of the femoral neck is tilted and extends medially, superiorly, and anteriorly. The angle of inclination of the femoral neck relative to the shaft in the frontal plane is the neck-shaft angle. In most adults, this angle varies between 90 and 135 degrees and is important because it determines the effectiveness of the hip abductors, limb length, and the forces applied to the hip joint.

[0281] Angle of inclination greater than 125 degrees is called coxa valgum, and angle of inclination less than 125 degrees is called coxa vara. Angle of inclination greater than 125 degrees corresponds to limb lengthening, hip abductor muscle effectiveness reduction, increased load on femoral head and increased stress on femoral neck. In the case of coxa vara, angle of inclination less than 125 degrees corresponds to limb shortening, hip abductor muscle effectiveness improvement, reduced load on femoral head and reduced stress on femoral neck. Femoral neck forms an acute angle with the transverse axis of femoral condyle. This angle is toward the inside and front, and is called anteversion. In adults, this angle is about 7.5 degrees on average.

[0282] The acetabulum is located at the confluence of the ilium, ischium and pubis on the side of the hip. These three separate bones unite to form the acetabulum, wherein the ilium and ischium each contribute about two-fifths of the acetabulum, and the pubis contributes one-fifth. The acetabulum is a mortise that is not deep enough to cover all of the femoral head, and has two parts that form an articulation and do not form an articulation. However, the acetabular labrum deepens the mortise to improve stability. Together with the labrum, the acetabulum covers slightly more than 50% of the femoral head. Only the side of the acetabulum is lined with articular cartilage, which is interrupted by a deep acetabular notch below. The central part of the acetabular cavity is deeper than the articular cartilage and is non-articular. This central part is called the acetabular fossa, and is separated by a thin plate from the interface of the pelvis. The acetabular fossa is a unique area for each patient, and is used to produce patient-specific guides for drilling and placement of acetabular cup components. In addition, the variability of anatomical features further guarantees the need for population-specific implant design.

[0283] Certain problems associated with the use of prior art cementless components can result in wide variations in the size, shape, and orientation of the femoral canal. One of the challenges in the design of osteosurgical implants for femoral stems is the large variability in the medial lateral and anterior-posterior dimensions. There is also significant variability in the ratio of proximal to distal femoral canal sizes. The different combinations of various arcs, taper angles, curves, and offsets in the normal population are surprising. But this is not the only problem.

[0284] Ancestral differences in femoral morphology and the lack of defined standards in modern populations make it difficult to design appropriate hip implant systems. For example, significant differences in anterior curvature, torsion, and cross-sectional shape exist between American Indians, African Americans, and white Americans. Differences in the femur between Asian and Western populations exist in the anterior arch of the femur, where Chinese femurs are more anteriorly curved and externally rotated compared with Caucasian femurs, and have smaller intramedullary canals and smaller distal condyles. Similarly, Caucasian femurs are larger in the distal condylar length dimension compared with Japanese femurs. There are also racial differences in proximal femoral bone mineral density (BMD) and hip axis length between African Americans and whites. The combined effects of higher BMD, shorter hip axis length, and shorter intertrochanteric width may explain the lower incidence of osteoporotic fractures in African American women compared with their white counterparts. Similarly, elderly Asian and African American men were found to have thicker cortices and higher BMD compared with white and Hispanic men, which may contribute to the greater bone strength in these racial groups. In general, African Americans have thicker cortical bone, narrower endosteal diameter, and greater BMD than white Americans.

[0285] For the original hip system, incorporating ancestral (and ethnic) differences in the femur and pelvis becomes even more challenging. Revision surgery creates even greater complexity. In addition to these normal anatomical and ethnic variability, the difficulties faced by the surgeon performing revision surgery are further exacerbated by the following factors: (a) femoral canal distortion caused by bone loss around the originally placed prosthesis; and (b) iatrogenic defects caused by the removal of components and bone cement.

[0286] All of the aforementioned factors have caused a large number of hip surgeons to look for ways to improve the design of cementless femoral prostheses. In total hip replacement (primary or revision), it is ideal to establish the best fit between the femoral ball and the acetabular cup. The femoral stem neck should have a cruciate cross-section to reduce rigidity. The length of the femoral stem should provide parallel contact between the femoral stem and the femoral wall within 2 to 3 femoral canal inner diameters. The proximal third of the femoral stem is coated with a porous coating or hydroxyapatite (HA). The femoral stem is cylindrical (i.e., not tapered) to control bending loads and allow all rotational and axial loads to be transmitted proximally. The femoral head position should reproduce the patient's own head center unless it is abnormal.

[0287] One way to attempt to meet these goals is to manufacture a femoral prosthesis individually for each patient. In other words, to manufacture a prosthesis specific to a particular patient rather than attempting to modify the patient's bone to fit an off-the-shelf prosthesis.

[0288] There are some common design rules for patient-specific (or mass customized) primary and revision hip replacements. These design rules include: (1) the stem should be collarless (except in revisions) to allow uniform load distribution to the femur; (2) the stem should have a modified rhomboid cross-section to maximize fit / fill, but rotational stability should be maintained; (3) the stem should be curved if necessary to conform to the patient's anatomy; (4) the stem should be inserted along a curved path with no gap between the prosthesis and the bone; (5) the stem neck should have a cruciate cross-section to reduce stiffness; (6) the length of the stem should provide parallel contact between the stem and the femoral wall within 2 to 3 femoral canal diameters; (7) the proximal third of the stem should be porous coated or coated with hydroxyapatite (HA); (8) the stem is cylindrical (i.e., not tapered) to control bending loads and allow all rotational and axial loads to be transmitted proximally; and (9) the position of the femoral head of the stem should reproduce the patient's own head center unless it is abnormal.

[0289] Below is taking the gender and / or race of patient population into account, producing the exemplary process and system of the bone surgery implant of mass customization for the patient of primary joint replacement in need.For the purpose of exemplary discussion, the total hip replacement surgery for the patient with partial anatomical structure is described.However, it should be understood that the exemplary process and system are applicable to any bone surgery implant suitable for mass customization when there is incomplete anatomical structure.For example, the exemplary process and system are applicable to the shoulder replacement and knee replacement of bone degeneration (partial anatomical structure), bone deformation or broken bone.Therefore, although what is discussed hereinafter is the femoral component of hip implant, those skilled in the art will appreciate the applicability of the system and process to other bone surgery implants, guides, tools, etc. for original bone surgery or bone surgery revision surgery.

[0290] refer to Fig.29 , depicts the overall process flow of using a statistical atlas to produce both mass-customized and patient-specific hip implants. First, the process includes a statistical atlas, which contains several instances of one or several bones to be analyzed. In the exemplary case of a hip implant, the statistical atlas includes several instances of skeletal models of the pelvis and femur. At least the acetabular component (i.e., acetabulum) and the proximal femoral component (i.e., femoral head) are subjected to joint surface geometry analysis. Specifically, the joint surface geometry analysis includes the calculation of landmarks, measurements, and shape characteristics for each bone from the statistical atlas of a given population. In addition, the joint surface geometry analysis includes generating quantitative values ​​such as statistical values ​​representing the calculations. From these calculations, the distribution of the calculations is plotted and parsed on the basis of the distribution. For example, for a bell-shaped distribution, it can be observed that approximately ninety percent (90%) of the population is grouped, so that non-patient-specific implants (e.g., mass-customized implants) can be designed and fully fit this grouping, thereby reducing the cost of the patient compared to patient-specific implants. For the remaining ten percent (10%) of the population, patient-specific implants may be a better approach.

[0291] In the situation of mass customized implant, can utilize statistical atlas to come quantitative assessment how many different groups (i.e. different implants) just can cover overwhelming given population.These quantitative assessments can produce the data cluster of the universal parameter of indication basic implant design, although described basic implant design is not patient-specific, but more specific than ready-made alternative.

[0292] In the case of patient-specific implants, a statistical atlas can be used to quantitatively evaluate the data of normal bone embodiment and the difference between the patient's bone and normal bone. More specifically, the statistical atlas can include curvature data associated with an average or template bone model. This template bone model can then be used to extrapolate the correct bone form of the patient and manufacture implants and surgical instruments for performing implant surgery.

[0293] Fig.30 The figure summarizes the use of statistical atlases in the design of mass-customized and patient-specific hip implants. In the case of the implant frame, again refer to Fig. 20 and 21 and the related discussion of these figures. Similarly, in the case of the designer box, refer again to Fig. 20 Finally, in the case of the patient-specific guide frame, refer again to Fig. 22 and related discussion of this figure.

[0294] like Fig.31 As shown in, a flowchart of an exemplary process that can be used to design and manufacture gender and / or race-specific hip implants is depicted. Specifically, the process includes utilizing a statistical atlas containing various different proximal femur (i.e., femurs including femoral heads) samples, which have been identified as being from males or females or the race of the person to whom the bones belong through relevant data. In addition, the statistical atlas module records virtual 3D models of one or more anatomical structures (e.g., bones) to capture the anatomical variability inherent in a population of a given gender and / or race. In an exemplary form, the atlas records a mathematical representation of the anatomical features of one or more anatomical structures, which is represented as an average representation and variations around the average representation for a given anatomical population that may have a common gender and / or race (or be grouped into one of a plurality of races with anatomical commonalities). Reference Figure 2 and the aforementioned statistical atlas and a discussion of how one can add anatomical structures to the atlas for a given population. Output from the statistical atlas is directed to an automatic landmark finding module and a surface / shape analysis module.

[0295] refer to Figure 31-43The automatic landmark finding module utilizes input from a statistical atlas (eg, regions that may contain specific landmarks) and local geometric analysis to compute anatomical landmarks for each anatomical structure instance in the statistical atlas. For example, various different proximal femoral landmarks are calculated for each 3D virtual model of the femur, including but not limited to: (1) the center of the femoral head, which is the center point of the approximately spherical femoral head; (2) the greater trochanter point, which is the point on the greater trochanter closest to the plane passing through the neck shaft point and perpendicular to the anatomical neck centerline; (3) the osteotomy point, which is the point 15 mm from the end of the lesser trochanter (approximately 30 mm from the lesser trochanter point); (4) the neck shaft point, which is the point where the section on the head ball encloses the smallest femoral neck cross section; (5) the femoral waist, which is the cross section with the smallest diameter along the femoral shaft; (6) the intramedullary canal waist, which is the cross section with the smallest diameter along the intramedullary canal; (7) the femoral neck pivot point, which is the point on the anatomical axis of the femur that forms an angle equal to the femoral neck angle with the center of the femoral head and the distal end of the anatomical axis of the femur; and (8) the lesser trochanter point, which is the most outwardly protruding point on the lesser trochanter area. As another example, various different proximal femoral axes are calculated for each 3D virtual model of the femur using the identified anatomical landmarks, including but not limited to: (a) a femoral neck anatomical axis, which is coaxial with a straight line connecting the center of the femoral head and the center of the femoral neck; (b) a femoral neck axis, which is coaxial with a straight line connecting the center point of the femoral head and the femoral neck pivot point; and (c) a femoral anatomical axis, which is coaxial with a straight line connecting two points located at 23% and 40% of the total femoral length from the proximal end of the femur.As another example, various different proximal femoral measurements are calculated for each 3D virtual model of the femur using the identified anatomical landmarks and axes, including but not limited to: (i) proximal angle, which is the 3D angle between the anatomical axis of the femur and the anatomical axis of the femoral neck; (ii) head constriction, which is the horizontal distance between the anatomical axis of the femur and the center of the femoral head; (iii) head height, which is the vertical distance between the lesser trochanter point (referenced to the front) and the center of the femoral head; (iv) greater trochanter to head center distance, which is the distance between the center of the head and the greater trochanter point (referenced to the front); (v) neck length, which is the distance between the center of the head and the neck pivot point (referenced to the front); (vi) head radius, which is the distance between the head center and the neck pivot point (referenced to the front); which is the radius of the sphere fitted to the femoral head; (vii) the neck diameter, which is the diameter of a circle fitted to the cross section of the neck in a plane normal to the anatomical axis of the femoral neck and passing through the center point of the neck (referenced to the front); (viii) the transepicondylar angle of the femoral neck anteversion, which is the angle between the transepicondylar axis and the axis of the femoral neck; (ix) the posterior condylar angle of the femoral neck anteversion, which is the angle between the posterior condylar axis and the axis of the femoral neck; (x) the LPFA, which is the angle between the mechanical axis and the vector pointing to the greater trochanter; (xi) the femoral calcar area index, which is defined by the equation (ZX) / Z, where Z is the area of ​​the femur 10 cm below the midpoint of the lesser trochanter and X is the area of ​​the intramedullary canal 10 cm below the midpoint of the lesser trochanter; (xii) the intramedullary calcar area ratio, which is the ratio between the area of ​​the intramedullary canal 3 cm below the mid-height of the lesser trochanter and the area of ​​the intramedullary canal 10 cm below the midpoint of the lesser trochanter; (xiii) XYR area, which is the ratio between the area of ​​the intramedullary canal 3 cm below the midpoint of the lesser trochanter and the area of ​​the intramedullary canal 10 cm below the midpoint of the lesser trochanter; (xiv) the minor axis / major axis ratio, which is the ratio between the minor axis and the major axis of an ellipse fitted to the cross-section of the intramedullary canal at the narrowest point on the intramedullary canal; and (xv) the femoral radius to intramedullary canal radius ratio, which is the ratio of the radii of a circle that best fits the circumference of the femur and the circumference of the intramedullary canal in a plane normal to the anatomical axis of the femur (this ratio reflects the thickness of the cortical bone and, therefore, cortical bone loss in the case of osteoporosis).

[0296] refer to Fig.31 and 45 -47, using the output from the automatic landmark finding module, the parameters of the femoral stem were evaluated for a given population, specifically, the medial contour, the neck angle, and the head constriction were evaluated, regardless of whether the population was grouped on the basis of race, gender, or a combination of both.

[0297] In the case of medial contours, for each femur in the population, the contour relative to the intramedullary canal is generated by intersecting the intramedullary canal with a plane extending through the femoral pivot point and having a normal axis perpendicular to both the femoral anatomical axis and the neck axis (vector cross product). After the contour is generated for each femur in the population, the population is subdivided into groups using the intramedullary canal size. When subdivided, the contour may be out of plane, so an alignment process is performed to align all contours relative to a common plane (e.g., an XZ plane). The alignment process includes aligning the axis normal to both the femoral neck axis and the anatomical axis with the Y axis, and then aligning the anatomical axis with the Z axis. In this way, all contours are translated relative to a specific point so that the contours have a common coordinate system.

[0298] After the profile has a common coordinate system, the femoral neck point is used to verify that the points of the profile are in the plane. Specifically, the femoral neck point reflects the true anatomy and ensures that the points on the profile are consistent points in the plane. By verifying that the points of the profile are in the plane, the alignment variability between the femurs of the population can be significantly reduced, which facilitates the use of the profile for head constriction and implant angle design.

[0299] refer to Fig.48 , the statistical atlas can also be used to interpolate between normal and osteoporotic bones. When designing a femoral stem and determining its size, a key consideration is the intramedullary canal dimension. For the femur, in the case of normal bones, the intramedullary canal is significantly narrower than in a femur that exhibits osteoporosis. This narrower intramedullary canal dimension is at least in part the result of a reduction in bone thickness (measured transversely to the dominant axis of the femur), which in turn causes the inner surface of the femur that defines the intramedullary cavity to recede. In this method, a synthetic population is generated by interpolating between healthy and severely osteoporotic bone thicknesses and generating a virtual 3D model with the thickness. Therefore, this data set contains bones corresponding to different stages of osteoporosis. This data set can now be used as input for implant stem design.

[0300] In an exemplary form, the statistical atlas includes a large number of normal non-osteoporotic bones and osteoporotic bones, in which case the bone is a femur. According to the process described herein for adding bones to the statistical atlas, each of these normal femurs in the atlas is quantified and represented as a 3D virtual model. Similarly, according to the process described herein for adding bones to the statistical atlas, each osteoporotic bone in the atlas is quantified and represented as a 3D virtual model. As a part of the 3D model of normal and osteoporotic bones, the intramedullary canal dimension along the longitudinal length of the femur is recorded. Using the point correspondence of the atlas, the intramedullary canal is identified as a fixed percentage (e.g., 5%) near the lesser trochanter across the total length of the bone and a second fixed percentage (e.g., 2%) near the distal cortical point on the atlas bone. In addition, the points falling within these proximal and distal boundaries on the outer surface of the bone are used to determine bone thickness, which is defined as the distance from an external point to the nearest point on the IM canal.

[0301] In the case of the proximal femur, Figures 51-62 Gender differences have been demonstrated in any racial group. Fig.59 and 60 As shown in, the template 3D model of the statistical atlas of the female proximal femur shows statistically significant measurements when compared with the template 3D model of the statistical atlas of the male proximal femur. Specifically, the female head constriction is reduced by about 9.3% compared with the male head constriction. In the current implant, the head constriction increases with the stem size, which is acceptable in the case of normal women. But problems arise when estimating the head constriction in the case of osteoporosis and osteopenia, in which bone loss causes the intramedullary canal size to increase, which means a larger stem size and a larger offset. Similarly, the neck diameter and head radius of the female are about 11.2% smaller than that of the male. And the neck length of the female is about 9.5% smaller than that of the male. In addition, the proximal angle of the female is about 0.2% smaller than that of the male. Finally, the femoral head height of the female is about 13.3% smaller than that of the male. Therefore, the gender bone data confirms that simply scaling the universal femoral implant (i.e., gender neutral) cannot explain the geometric differences of the bones, and therefore a gender-based femoral implant is needed.

[0302] refer to Figures 63-68, not only the dimensions of the proximal femur vary widely across the gender line, but also the cross-sectional shape of the femur along the length of the intramedullary canal. Specifically, across a given population in the statistical atlas of male and female femurs, males have intramedullary canal cross-sections that are closer to a circle than females. More specifically, females have intramedullary canal cross-sections that are 8.98% more eccentric than males. As will be discussed in more detail later, this gender-specific data constitutes a part of feature extraction data, which is plotted to obtain clusters, from which number and overall shape parameters are extracted to obtain gender-specific femoral implants.

[0303] like Figures 72-74 In the present invention, the statistical atlas includes the calculation corresponding to the measured value of the head center offset in the front-back (AP) direction in a given femur population (grouped by gender). In an exemplary form, the AP direction is determined by the vector of the front vertically pointing to the mechanical axis and the posterior ankle axis. The offset between the femoral head center and two reference points is measured, wherein the first reference point is the midpoint of the anatomical axis, and the second reference point is the femoral neck pivot point. In general, between male and female femurs, the AP head height relative to the neck pivot point and the anatomical axis does not show significant differences. Similarly, this gender-specific data constitutes a part of feature extraction data, which is mapped to obtain a cluster, and number and overall shape parameters are extracted from the cluster to obtain gender-specific femoral implants.

[0304] Reference again Fig.28 and 31 The in vivo head center offset, cross-sectional shape data of the intramedullary canal, and medial contour data of the femur form part of the extracted feature data, which are plotted to identify the number of clusters present within a given population (assuming that the atlas includes data on the ethnicity associated with each bone, one cluster is sex-specific and the second is gender-specific) in order to design a method that is consistent with the characteristics of the bone. Fig.28 The flowchart and related discussion of the invention are consistent with the gender- and / or race-specific mass-customized implants. The identified gender- and / or race-specific clusters are used to extract the parameters necessary for designing mass-customized femoral implants.

[0305] refer to Fig.76, depicts an exemplary mass-customized femoral component that meets the present disclosure. Specifically, the mass-customized femoral component comprises 4 basic elements, which include ball, neck, proximal stem and distal stem. Each basic element comprises an interchangeable interface to allow the interchange of ball, neck and stem with other interchangeable elements. In this way, if a larger femoral ball is needed, only the femoral ball will be replaced. Similarly, if a larger neck offset is needed, the neck element is replaced with a different neck element that provides the necessary offset, while if suitable, retaining other 3 elements. In this way, the femoral component can be customized within certain limits to fit the patient, without sacrificing the fitting or kinematics that would otherwise be abandoned when using a universal implant. Therefore, all femoral elements can be replaced with other mass-customized elements to better suit the patient's anatomical structure.

[0306] In this exemplary embodiment, the neck is configured to rotate around the axis of the proximal stem so that the rotational orientation of the neck relative to the proximal stem can be adjusted during surgery. Specifically, preoperative measurements can establish the planned rotational position of the neck relative to the proximal stem. However, intraoperative considerations such as in vivo motion testing can cause the surgeon to change the preoperative rotational orientation to provide improved kinematics or avoid specific collisions. For example, the neck includes a cylindrical bolt having an embedded peripheral groove with a textured surface. The cylindrical bolt is received in an axial cylindrical channel of the proximal stem. In addition to this cylindrical channel, a second channel intersects the cylindrical channel and is shaped to receive a plate having a semicircular groove, which also has a texture and is configured to engage the textured surface of the embedded peripheral groove. A pair of screws fastened to the proximal stem pushes the plate to engage with the cylindrical bolt so that rotational movement of the cylindrical bolt relative to the proximal stem is no longer possible. Therefore, when this fixed engagement is reached, the screws can be loosened to allow rotational movement between the cylindrical bolt and the proximal stem, as necessary to make rotational adjustments during surgery.

[0307] The engagement between the neck and the ball can be conventional, while the engagement between the proximal stem and the distal stem is unconventional. Specifically, the proximal stem includes a distal handle that is threaded and is used to be received in a threaded hole extending into the distal stem by threads. Therefore, the proximal stem is fixed to the distal stem by rotating the proximal stem relative to the distal stem so that the threads of the handle engage the threads of the distal stem hole. When the proximal stem is close to the distal stem, the rotation of the proximal stem relative to the distal stem is stopped. However, if a rotational adjustment is required between the proximal stem and the distal stem, a spacer corresponding to the correct rotational adjustment can be provided using a washer. As a further example, if a larger rotational adjustment is required, the thickness of the washer is larger, and a thinner washer will provide a smaller rotational adjustment accordingly.

[0308] Each basic element can be manufactured with a predetermined alternative that takes into account size and profile variability in a given gender and / or race. In this way, the alternatives of the basic element can be mixed and matched to make the patient-specific implant closer to the patient's anatomical structure in shape than the mass-customized femoral component, but the cost and process for producing the patient-specific femoral implant are only a fraction.

[0309] Fig.77 Described another kind of optional exemplary mass customization femoral component that meets the present disclosure.Specifically, the femoral component of described mass customization comprises 5 basic elements, and it comprises ball, neck, proximal stem, middle stem and distal stem.Each basic element comprises interchangeable interface, allows ball, neck and stem to be interchanged with other interchangeable elements.Those skilled in the art will appreciate that, by increasing the number of elements of the femoral component of mass customization, be similar to the fragment of stacking patient's natural femur to reproduce this bone, people can use the element of mass customization to more and more approach the fitting of patient-specific implant.

[0310] Similar to the anatomical differences in the proximal femur between sexes and races, Figures 78-83 Gender and ethnic differences were confirmed within the total pelvic population in the atlas. Fig.28 Using atlas data grouped on the basis of at least one of sex and race (i.e., pelvic populations), a series of mass-customized acetabular cup implants were designed and manufactured. Fig.78 As shown in FIG, the grouped atlas data is subjected to an automatic landmark finding process and a surface / shape analysis process to separate the geometry of the acetabular cup. Fig.82 and 83 As shown in FIG, the calibration (for the location of the acetabular ligaments) and contour analysis (for evaluating the contour of the acetabular cup) process leads to feature extraction, from which the anatomical acetabular cup implant surface is finally generated, as shown in FIG. Fig.79 This analysis shows that the acetabular cup and femoral head are not composed of a single radius of curvature but of several radii, such as Fig.80 and 81 as shown in .

[0311] Generation of animal-specific implants

[0312] refer to Fig.85 , exemplary systems and methods for designing and manufacturing animal-specific (i.e., patient-specific for an animal) implants and related instrumentation are similar to those described above for Fig. 20The process described and explained is incorporated herein. As a pre-portion, an image of an animal's anatomical structure is obtained and automatically segmented to obtain a virtual 3D skeletal model. Although a CT scan image is shown, it should be understood that other imaging modalities outside CT can be used, such as but not limited to MRI, ultrasound and X-ray. According to the exemplary disclosure above, the virtual 3D skeletal model of the affected anatomical structure is loaded into a statistical atlas. Subsequently, the input from the statistical atlas is used to reconstruct the skeleton and produce a reconstructed virtual 3D skeletal model. Bone landmarks are calculated on the surface of the reconstructed virtual 3D skeletal model to allow the correct implant size to be determined. The geometry of the affected skeleton is then mapped and converted into a parametric form, which is then used to produce an animal-specific implant that simulates the residual anatomical geometry. In addition to the animal-specific implant, an animal-specific instrument is manufactured and used for the preparation of the residual bone of the animal and the placement of the animal-specific implant.

[0313] refer to Fig.86 , an exemplary system and method for designing and manufacturing large-scale customized animal implants is similar to the one previously described for Fig.28 The process described and explained is incorporated herein. As a pre-process, a 3D animal bone model from a statistical atlas associated with the bone in question is subjected to automatic landmark finding and surface / shape analysis. The automatic landmark finding process uses information stored in the atlas (e.g., areas that may contain specific landmarks) and local geometry analysis to automatically calculate anatomical landmarks for each 3D animal bone model. For each bone in question in the statistical atlas, shape / surface analysis directly extracts the features and surface geometry of the 3D virtual animal bone model. Subsequently, a feature extraction process is performed on each 3D animal bone model using a combination of landmarks and shape features to calculate features associated with implant design. These features are used as input for a clustering process, in which a predetermined clustering method is used to divide the animal bone population into groups with similar features. Each cluster obtained represents instances of the shape and size used to define a single animal implant. Subsequently, a parameterization process is performed on each cluster center (implant size) to extract parameters (e.g., computer-aided design (CAD) parameters) for the overall implant model. Subsequently, the extracted parameters are used to generate overall implant surfaces and sizes for each cluster. Depending on the cluster the animal patient falls into, a mass customized implant is selected from the necessary group and implanted.

[0314] Generation of patient-specific cutting guides

[0315] refer to Figure 87-102, describes an exemplary process and system for integrating multi-dimensional medical imaging, computer-aided design (CAD), and computer graphics features for designing patient-specific cutting guides. For purposes of exemplary explanation only, patient-specific cutting guides are described in the context of total hip replacement surgery. However, those skilled in the art will recognize that the exemplary process and system are applicable to any surgical procedure that can utilize a cutting guide.

[0316] like Fig.87 As shown in, an overview of an exemplary system flow begins with receiving input data representing an anatomical structure. The anatomical data input includes a two-dimensional (2D) image or a three-dimensional (3D) surface representation of the anatomical structure in question, which can, for example, take the form of a surface model or a point cloud. In the case of using 2D images, these 2D images are used to construct a 3D surface representation of the anatomical structure in question. Those skilled in the art are familiar with using 2D images of anatomical structures to construct 3D surface representations. Therefore, a detailed explanation of this process is omitted for brevity. For example, the anatomical data input can include one or more of an X-ray (obtained from at least two angles), a computed tomography (CT) scan, a magnetic resonance image (MRI), or any other imaging data from which a 3D surface representation can be generated. In an exemplary form, the anatomical structure includes a pelvis and a femur.

[0317] However, it should be understood that the following is an exemplary description of an anatomical structure that can be used with the exemplary system, and it is by no means intended to limit other anatomical structures to be used with the present system. When used herein, tissue includes bone, muscle, ligament, tendon, and any other structural material of a determined type that has a specific function in a multicellular organism. Therefore, when the exemplary system and method are discussed in the context of bones related to the hip joint, those skilled in the art will recognize the applicability of the system and method to other tissues.

[0318] Depending on the type of input data, the system's femoral and pelvic input anatomical data is directed to one of two modules. In the case of X-ray data, the 2D X-ray images are input to the non-rigid module to extract the 3D bone contours. If the input data takes the form of CT scans or MRI images, these scans / images are directed to the automatic segmentation module, where the scans / images are automatically segmented to extract the 3D bone contours (and 3D cartilage contours).

[0319] refer to Fig.88, the non-rigid module uses multiple X-ray images acquired at at least two different angles to perform one or more pre-processing steps. These steps may include one or more of the following: noise reduction and image enhancement. The resulting pre-processed X-ray image is subjected to a calibration step to align the X-ray image. Preferably, the X-ray image is acquired in the presence of a fixed position calibration device so that the X-ray image is aligning relative to the fixed position calibration device. However, when a fixed position calibration device is not present in the X-ray image, the image can still be calibrated using features detected in common in multiple images. The output from this calibration process is the position of the anatomical structure relative to the imager, which is in Fig.88 The term "posture" is used to identify it.

[0320] The resulting pre-processed X-ray image is subjected to a feature extraction step. The feature extraction step includes performing one or more computations of image features using the pre-processed X-ray image. For example, these computations may include gradient features, contours, texture components, or features from any other image source. In this exemplary process, the feature extraction step outputs features derived from the X-ray image in the Fig.88 The contours of the anatomical structures (eg, bone shapes) are referred to as "contours" and the image features are referred to as "textures". Both the contoured anatomical structures and image feature data are directed to a non-rigid registration step.

[0321] The non-rigid registration step aligns the output from the feature extraction step and the calibration step to a 3D template model of the anatomical structure in question from a statistical atlas. For example, a 3D template model is generated in response to a non-linear principal component from an anatomical database that contains a portion of the statistical atlas. During the non-rigid registration step, the shape parameters (non-linear principal components) of the 3D template model are optimized to match the shape parameters of the X-ray image obtained from the pose, contour and texture data. The output from the non-rigid registration step is a 3D patient-specific skeletal model, which is similar to the 3D patient-specific skeletal model output from the automatic segmentation module for CT scans or MRI images, and is directed to a virtual template production module.

[0322] refer to Fig.91 The automatic segmentation process begins with acquiring, for example, a CT scan or MRI image and performing an automatic segmentation process. Fig.90The automatic segmentation process includes aligning the scan / image relative to a basic or starting 3D model of the anatomical structure in question. After aligning the scan / image with the basic 3D model, the scan / image is processed through an initial deformation process to calculate normal vectors, determine the locations of profile points, linearly interpolate intensity values, filter the resulting profile using a Savitsky-Golay filter, generate a gradient of the profile, weight the profile using a Gaussian weighted profile equation, determine the maximum profile, and use these maximum profiles to deform the basic 3D model. The resulting deformed 3D model is projected onto a template 3D model of the anatomical structure in question from a statistical atlas. Using the parameters of the template 3D model, the deformed 3D model is further deformed in a secondary deformation process to resemble the unique features of the template 3D model. After the latter deformation process, the deformed 3D model is compared with the scan / image to identify whether there are significant differences.

[0323] In the event that there is a significant difference between the deformed 3D model and the scan / image, the deformed 3D model and the scan / image are again subjected to the initial deformation process, followed by the secondary deformation process. This loop process is continued until the deformed 3D model is within a predetermined tolerance range for the difference between the deformed 3D model and the scan / image.

[0324] After it has been determined that the deformed 3D model exhibits less than a significant difference relative to the previous iteration or the maximum number of iterations has been achieved, the surface edges of the deformed 3D model are smoothed, and then a higher resolution meshing step is performed to further smooth the surface to produce a smoothed 3D model. The smoothed 3D model is subjected to an initial deformation process (consistent with the initial deformation process before the aforementioned surface smoothing) to produce a 3D segmented skeletal model.

[0325] Reference again Fig.91 , the 3D segmented skeleton model is processed to produce contours. Specifically, the intersection of the 3D segmented skeleton model and the scan / image is calculated, which produces a binary contour at each image / scan plane.

[0326] The 3D segmented bone model is also processed to generate a statistical 3D model of the patient-specific bone appearance. Specifically, the appearance of the bone and any anatomical abnormalities are modeled based on the image information present within and outside the contours.

[0327] The bone contour is then reviewed by the user of the segmentation system. The user can be a segmentation expert or an occasional user of the segmentation system who is concerned about one or more areas of the 3D model that are not associated with the segmentation area. This lack of association may exist in the case of a missing area or an obviously incorrect area. After identifying one or more error areas, the user can select a "seed point" indicating the center of the area where the error area exists on the model, or manually outline the outline of the missing area. The system's software uses the initial scan / image of the anatomical structure from CT or MRI, and uses the seed point to add or subtract the local outline of the seed point. For example, the user can select the area where osteophytes should exist, and the software compares the scan / image with the area on the 3D model to add osteophytes to the segmentation process. Any changes made to the 3D model are ultimately reviewed and verified or revoked by the user. This review and correction process can be repeated as many times as needed to cope with the anatomical differences between the scan / image and the 3D model. When the user is satisfied with the 3D model, the obtained model can be manually operated to remove the bridge and touch-up areas of the model as needed, and then output to the virtual template making module.

[0328] like Fig.87 and 92 As shown in, the virtual template making module receives the 3D patient-specific model from either one or both of the automatic segmentation module and the non-rigid registration module. In the case of hip joint, the 3D patient-specific model includes pelvis and femur, both of which are input into the automatic search for landmarks process. This automatic search for landmarks step uses the similar anatomical structures present in the statistical atlas and the local geometric structure search to calculate the anatomical landmarks relevant to implant placement on the femur and pelvis 3D models.

[0329] In such Fig.93 In the case of automatic placement of femoral stem using distal fixation as shown in, Automatically find landmarks include determining the axis on femur and implant. For femur, calculate anatomical femoral axis (AFA), then calculate proximal anatomical axis (PAA). Then calculate proximal neck angle (PNA), which is defined as the angle between AFA and PNA. For femoral implant, implant axis is the length along the implant stem, and implant neck axis is the length along the implant neck. Similar to the PNA of femur, implant angle is defined as the angle between implant axis and implant neck axis. Then select the implant with the implant angle closest to PNA. Then implant fitting angle (IFA) is defined as the intersection between the proximal anatomical axis and the vector drawn from the center of femoral head with the selected implant angle.

[0330] When Fig.93When using automated placement of the femoral stem using distal fixation and calculated anatomical landmarks as shown in , the implant sizing step determines / estimates the appropriate implant size for the femoral component. The implant size is selected by comparing the width of the implant to the width of the intramedullary canal and selecting the implant with the width that is closest to the intramedullary canal. The system then moves forward to the implant placement step.

[0331] In the implant placement step for distal fixed femoral stem, based on the surgeon's preferred surgical technique and previously calculated anatomical landmarks, determine / select initial implant position for all relevant implant components.Produce resection plane to simulate proximal femoral osteotomy, and assess the fitting of implant.Carry out fitting assessment by analyzing the implant and the femoral intramedullary canal aligned at various heights along the implant axis.Align the implant axis with the anatomical femoral axis, then flatly implant the implant so that the neck of the implant is in the approximate position of the proximal femoral neck, and align the implant with the femur.Then implant is rotated around the anatomical femoral axis to obtain required anteversion.

[0332] As part of this implant placement step, an iterative plan is used, which includes using an initial "guessing" for implant placement as a part of motion simulation, to evaluate the placement of the "guessing". In an exemplary form, motion simulation uses estimated or measured joint kinematics to make the implant (based on the placement of the selected implant) move through a certain range. Therefore, motion simulation can be used to determine the collision position and estimate the range of motion that the implant produces after implantation. When motion simulation produces unsatisfactory data (such as unsatisfactory range of motion, unsatisfactory natural motion simulation, etc.), another position for implant placement can be used, and then kinematic analysis is performed to further refine the placement of the implant, until satisfactory results are reached. After determining / selecting the implant position for all related implant components, the template data advances to the fixture generation module.

[0333] In such Fig.94In the case of automatic placement of femoral stem using press fit and three-point contact as shown in, automatically finding landmarks includes determining the axis on femur and implant. For femur, Calculate anatomical femoral axis (AFA), then calculate proximal anatomical axis (PAA). Then calculate proximal neck angle (PNA), which is defined as the angle between AFA and PNA. For femoral implant, implant axis is the length along the implant stem, and implant neck axis is the length along the implant neck. Similar to the PNA of femur, implant angle is defined as the angle between implant axis and implant neck axis. Then select the implant with the implant angle closest to PNA. Then implant fitting angle (IFA) is defined as the intersection between proximal anatomical axis and the vector drawn from the center of femoral head with selected implant angle.

[0334] When Fig.94 When using automatic placement of the femoral stem using press fit, three-point contact, and calculated anatomical landmarks as shown in, the implant sizing step determines / estimates the appropriate implant size for the pelvic and femoral components. The implant size is selected by aligning the implant axis with the anatomical femoral axis and aligning the implant with the femur. The implant is then rotated to align its neck axis with the femoral neck axis. The implant is then translated into an anatomically correct position within the proximal femur. Subsequently, the system moves forward to the implant placement step.

[0335] In the implant placement step for the press-fit femoral stem, the initial implant position is determined / selected for all related implant components based on the surgeon's preferred surgical technique and previously calculated anatomical landmarks. A resection plane is generated to simulate a proximal femoral osteotomy and to assess the fit of the implant. Fitting assessment is performed by analyzing the contours of the implant and the femoral intramedullary canal. The contour is generated as follows: the intramedullary canal is intersected with a plane normal to both the anatomical axis and the femoral neck axis and through the intersection of the anatomical axis and the femoral neck axis to generate a contour. When the implant and intramedullary canal contours are generated, only implants with a width less than the intramedullary canal width at the same position are retained, resulting in many possible correct implant sizes. The group of possible sizes is reduced by two strategies that reduce the mean square error of the distance between the implant and the intramedullary canal. The first strategy minimizes the mean square error (MSE) or other mathematical error measures of the distance between the medial and lateral sides of the implant and the intramedullary canal. The second strategy minimizes the MSE of the distance between the lateral side of the implant and the intramedullary canal.

[0336] As part of this implant placement step, an iterative plan is used, which includes using an initial "guessing" for implant placement as a part of motion simulation, to evaluate the placement of the "guessing". In an exemplary form, motion simulation uses estimated or measured joint kinematics to make the implant (based on the placement of the selected implant) move through a certain range. Therefore, motion simulation can be used to determine the collision position and estimate the range of motion that the implant produces after implantation. When motion simulation produces unsatisfactory data (such as unsatisfactory range of motion, unsatisfactory natural motion simulation, etc.), another position for implant placement can be used, and then kinematic analysis is performed to further refine the placement of the implant, until satisfactory results are reached. After determining / selecting the implant position for all related implant components, the template data advances to the fixture generation module.

[0337] Reference again Fig.87 , the fixture generation module generates a patient-specific guide model. More specifically, the shape and placement of the patient-specific implant relative to the patient's residual bone are understood from the template data and the associated planning parameters. Therefore, using the patient-specific 3D bone model, the virtual template making module calculates the position of the implant relative to the patient's residual bone, and thus provides the fixture generation module with information about how much of the patient's residual bone is intended to be retained. In accordance with this bone retention data, the fixture generation module uses the bone retention data to assign one or more bone saws to reduce the patient's current bone to the residual bone required to receive the implant as planned. Using the required bone saw, the fixture generation module generates a virtual 3D model of the cutting guide / fixture, which has a shape configured to cooperate with the patient's bone in a single position and orientation. In other words, the 3D model of the cutting fixture is generated as a "negative shape" of the anatomical surface of the patient's residual bone, so that the real cutting guide accurately matches the patient's anatomical structure. In this way, any guesswork related to the positioning of the cutting fixture is eliminated. After the fixture generation module generates a virtual 3D model of the cutting fixture, the module outputs the machine code necessary for a rapid prototyping machine, a CNC machine or the like to manufacture a real cutting guide. For example, an exemplary cutting fixture for the femoral head and neck incision includes a hollow slot that forms a related guide to limit the blade within a certain range of movement and maintain the blade in a predetermined orientation, which reproduces the virtual cutting from the surgical planning and template making module. The fixture generation module is also used to generate a placement fixture for the femoral stem.

[0338] refer to Fig.100, after cutting the femoral head and neck, intramedullary reaming is performed and then the femoral stem is inserted. In order to prepare the femur for the insertion of the femoral implant, the intramedullary canal needs to be reamed along an orientation consistent with the orientation of the femoral implant. If the reaming deviates, the orientation of the femoral implant may be impaired. To address this concern, the fixture generation module generates a virtual guide, which is a "negative shape" of the patient's residual or cut bone anatomical surface, so that a rapid prototyping machine, CNC machine or similar device can manufacture a cutting guide that accurately matches the patient's anatomy. For example, the reaming fixture may include an axial guide along which the reamer can move longitudinally back and forth. Using the reaming fixture, the surgeon performing the reaming operation ensures that the reaming is done in the correct orientation.

[0339] Intramedullary canal can receive femoral stem.Similarly, in order to ensure that femoral stem is correctly placed in intramedullary canal from both rotation viewpoint and angle viewpoint, fixture generation module produces femoral stem placement guide.For example, femoral stem placement guide is the "negative shape" of the anatomical surface of the residual or cut bone of the patient, and is also the "negative shape" of the top of femoral stem.In this way, placement guide slides on femoral shaft (the part of femoral stem connected with femoral ball), and includes the unique shape of cooperating with the residual or cut bone of the patient at the same time, so that femoral stem can only take single orientation relative to patient's femur, thus ensuring that femoral stem is correctly implanted consistent with preoperative plan.However, it should be noted that, although exemplary fixtures are described in the context of initial hip implant, it should be understood by those skilled in the art that above-mentioned exemplary process and system are not limited to initial hip implant or are limited to hip implant or revision surgery procedure.On the contrary, described process and system are applicable to any hip implant and relate to other areas of health including but not limited to the surgery of knee, ankle, shoulder, spine, head and elbow.

[0340] like Fig.101As shown in, in the case of acetabulum, the fixture generation module can generate instructions for manufacturing reaming and acetabular implant placement guides for acetabular cups. Specifically, from the template data and related planning parameters, the shape and placement of the patient-specific acetabular implant relative to the patient's residual pelvic bone are known. Therefore, using the patient-specific 3D acetabular model, the virtual template making module calculates the size and position of the acetabular cup implant relative to the patient's residual bone, and therefore provides the fixture generation module with information about how much patient's residual pelvic bone and the required implant orientation are intended to be retained. Consistent with this bone retention data, the fixture generation module uses the bone retention data to assign one or more bone saws / reamers to reduce the patient's current pelvis to the residual bone required for the planned acetabular implant. Using the required bone saw, the fixture generation module generates a virtual 3D model of a cutting guide / jig, and the cutting guide / jig has a shape that is configured to cooperate with two parts of the patient's pelvic bone by only one orientation. In other words, the 3D model of the cutting fixture is generated as a "negative shape" of the anatomical surface of the patient's pelvic bone, so that the actual reaming guide accurately matches the patient's anatomical structure. In this way, any guesswork related to the positioning of the reaming fixture is eliminated. After the fixture generation module generates the virtual 3D model of the reaming fixture, the module outputs the machine code necessary for the rapid prototyping machine, CNC machine or similar device to manufacture the actual reaming fixture. For example, the exemplary acetabular component fixture for acetabular reaming comprises a 4-part structure, wherein the first part is configured to be received in the natural acetabulum and temporarily mounted to the second part, until the second part is fixed to the pelvis using the first part as a placement guide. After the second part is fastened to the pelvis, the first part can be removed. Subsequently, the third part includes a cylindrical or partially cylindrical component, which uniquely cooperates with the second part to ensure that the reamer can move back and forth longitudinally relative to the third part, but its orientation is fixed using a combination of the first and third parts. After reaming, the reamer is removed and the third part is removed from the first part. The acetabular cup implant is installed to the reamed acetabulum using the fourth part. Specifically, the shape of the fourth part only uniquely engages the first part in a single orientation and is simultaneously shaped to be received inside the acetabular cup implant. After the acetabular cup implant is positioned, both the first and fourth parts are removed. It should also be noted that in order to drill one or more holes in the pelvis to place the acetabular implant, additional fixtures can be produced, wherein each drilling fixture is successively mounted to the first part to verify the orientation of the drill bit.

[0341] Surgical Navigation

[0342] refer to Figure 103-111, depicts an optional exemplary system and process for using one or more inertial measurement units (IMUs) during surgery to facilitate surgical navigation to correctly place bone surgical tools and bone surgical implants. This first optional exemplary embodiment is described in the context of performing total hip replacement surgery. However, the methods, systems and processes described below are applicable to any other surgical operations for which the guidance of surgical tools and implants is useful.

[0343] As schematically depicted, the initial steps of utilizing patient images (X-ray, CT, MRI, etc.) and performing segmentation or registration to obtain a virtual template of the patient's anatomy and appropriate implant size, shape, and placement are similar to those previously described. Fig.87 , 88 , 90-92. Somewhat different are the modules and processes used downstream of the virtual template making module.

[0344] Downstream of the virtual template making module is the initialization model generation module. This module receives template data and relevant planning parameters (i.e. the shape of patient-specific acetabular implant and the placement relative to the residual pelvis of the patient and the shape of patient-specific femoral implant and the placement relative to the residual femur of the patient are known). Using this patient-specific information, the initialization model generation module manufactures the 3D virtual model of the initialization device for the original acetabular cup of the patient and the 3D virtual model of the initialization device for the femoral implant. In other words, the 3D model of the acetabular initialization device is produced as the "negative shape" of the anatomical surface of the patient's acetabulum, so that the actual initialization device accurately matches the acetabulum of the patient. Similarly, the 3D model of the femoral stem initialization device is produced as the "negative shape" of the anatomical surface of the patient's residual femur and femoral implant, so that the actual initialization device only accurately matches the residual femur and femoral implant of the patient in a single position and a single orientation. In addition to producing these initialization devices, the initialization model generation module also produces the necessary machine code for the similar device of rapid prototyping machine, CNC machine or manufacturing actual acetabular initialization device and femoral initialization device. The actual acetabular initialization device and femoral initialization device are manufactured and mounted to (or molded or integrated into at the same time) or integrated into a surgical navigation tool configured with at least one IMU 1002.

[0345] IMUs 1002 capable of reporting orientation and translation data are incorporated into (e.g., mounted to) surgical tools to assist in surgical navigation, including positioning surgical equipment and implant devices. These IMUs 1002 are communicatively connected (wired or wireless) to a software system that receives output data indicating relative velocity and time from the IMU and allows the software to calculate the current position and orientation of the IMU, or the IMU 1002 calculates and sends the position and orientation of the surgical instrument, the position and orientation of the surgical instrument associated with the IMU, which will be discussed in more detail later. In this exemplary description, each IMU 1002 includes 3 gyroscopes, 3 accelerometers, and 3 Hall effect magnetometers (a set of 3 three-axis gyroscopes, accelerometers, and magnetometers), which can be integrated in a single circuit board or one or more sensors (e.g., gyroscopes, accelerometers, and magnetometers) constitute a separate board to output data related to 3 mutually perpendicular directions (e.g., X, Y, and Z directions). In this way, each IMU 1002 effectively generates 21 voltage or numerical outputs from 3 gyroscopes, 3 accelerometers, and 3 Hall effect magnetometers. In an exemplary form, each IMU 1002 includes a sensor board and a processing board, wherein the sensor board includes an integrated sensing module (LSM9DS, ST-Microelectronics) consisting of 3 accelerometers, 3 gyroscope sensors, and 3 magnetometers and two integrated sensing modules (LSM303, ST-Microelectronics) consisting of 3 accelerometers and 3 magnetometers. Specifically, the IMUs 1002 each include an angular momentum sensor that measures rotational changes in space on at least 3 axes: pitch axis (up and down), yaw axis (left and right), and roll axis (clockwise or counterclockwise rotation). More specifically, each integrated sensing module containing a magnetometer is placed at a different position on the circuit board, wherein each magnetometer is assigned to output a voltage proportional to the applied magnetic field, and also detects the polarity direction of the magnetic field at a spatial point in each of the three directions in the three-dimensional coordinate system. For example, the first magnetometer outputs a voltage proportional to the applied magnetic field at the first position and the polarity direction of the magnetic field in the X direction, Y direction, and Z direction, while the second magnetometer outputs a voltage proportional to the applied magnetic field at the second position and the polarity direction of the magnetic field in the X direction, Y direction, and Z direction, and the third magnetometer outputs a voltage proportional to the applied magnetic field at the third position and the polarity direction of the magnetic field in the X direction, Y direction, and Z direction. By using these three sets of magnetometers, the heading orientation of the IMU can be determined in addition to detecting local magnetic field fluctuations. Each magnetometer uses the magnetic field as a reference and determines the orientation deviation from the magnetic north. However, the local magnetic field can be distorted by iron or magnetic materials, which is usually referred to as hard or soft iron distortion. Examples of soft iron distortions are materials with low magnetic permeability such as carbon steel, stainless steel, etc.Hard iron distortions are caused by permanent magnets. These distortions produce inhomogeneous fields (see. Fig.184 ), which affects the accuracy of the algorithms used to process the magnetometer output and interpret the heading orientation. Therefore, as discussed in more detail later, a calibration algorithm is used to calibrate the magnetometer to restore uniformity in the detected magnetic field. Each IMU 1002 can be powered by a replaceable or rechargeable energy storage device, such as, but not limited to, a CR2032 button cell and a 200mAh rechargeable lithium-ion battery.

[0346] The sensing module integrated in the IMU 1002 may include a configurable signal modulation circuit and an analog-to-digital converter (ADC) that generates a digital output for the sensor. The IMU 1002 may use a sensor with a voltage output, wherein the signal modulation circuit, which may be a compensation amplifier, is configured to modulate the sensor output to the input range of a multi-channel 24-bit analog-to-digital converter (ADC) (ADS 1258, Texas Instrument). The IMU 1002 also includes an integrated processing module that includes a microcontroller and a wireless transmission module (CC2541, Texas Instrument). Alternatively, the IMU 1002 may use a separate low-power microcontroller (MSP430F2274, Texas Instrument) as a processor and a compact wireless transmission module (A2500R24A, Anaren) for communication. The processor may be integrated as part of each IMU 1002, or separate from each IMU but in communication connection with it. The processor may be Bluetooth compatible and provide wired or wireless communication to the gyroscope, accelerometer, and magnetometer, as well as providing wired or wireless communication between the processor and a signal receiver.

[0347] Each IMU 1002 is communicatively connected to a signal receiver that processes data received from multiple IMUs using a predetermined device identification number. The data rate is approximately 100 Hz for a single IMU and decreases when more IMUs are connected to a shared network. The software of the signal receiver receives signals from the IMU 1002 in real time and continuously calculates the current position of the IMU based on the received IMU data. Specifically, the acceleration measurements output from the IMU are integrated over time to calculate the current velocity of the IMU on each of the three axes. The velocity calculated for each axis is integrated over time to calculate the current position. However, in order to obtain useful position data, a reference framework must be established, which includes calibrating each IMU.

[0348] The present disclosure includes new systems and methods for calibrating one or more IMUs used in surgical navigation. Existing patent references that utilize IMUs as supportive aids in surgical navigation suffer from inoperability due to a number of reasons. Among these reasons are the placement of the IMU relative to metal surgical instruments and the lack of IMU calibration. More specifically, in the case where the IMU incorporates a magnetometer, on-site calibration of the magnetometer is imperative for surgical tracking of surgical instruments and related orthopedic components.

[0349] refer to Fig.180 According to the present disclosure, a new calibration tool 1000 is used to calibrate one or more IMUs 1002 that may incorporate a magnetometer. In an exemplary form, the calibration tool 1000 includes a stationary base 1006, which contains a controller 1008, a motor 1012, a transmission 1016, a drive shaft 1020, and a power source 1024. The drive shaft 1020 is mounted to a portion of the transmission 1016, as is the motor 1012, so that the motor effectively drives the transmission and rotates the drive shaft. Specifically, the motor 1012 includes an electric motor having a single drive shaft, and a transmission gear of the transmission 1016 is mounted to the drive shaft. The transmission gear meshes with a second gear mounted to the drive shaft 1020, so that the rotational motion of the motor 1012 is converted into the rotational motion of the drive shaft 1020.

[0350] In this exemplary configuration, the stationary base 1006 includes a circular exterior that partially defines a hollow interior to house the motor 1012, the transmission 1016, the controller 1008, the power supply 1024, and a portion of the drive shaft 1020. For example, a central vertical shaft extends through the stationary base 1006 that is coaxial with the central axis of the drive shaft 1020. This coaxial alignment reduces vibrations that occur as a result of rotation of the drive shaft 1020 relative to the stationary base 1006. The rotation of the drive shaft 1020 effectively rotates the external stage 1030 relative to the stationary base 1006.

[0351] In an exemplary form, an annular support plate 1034 is interposed between the top of the stationary base 1006 and the bottom of the external stage 1030. Both the stationary base 1006 and the support plate 1034 include corresponding axial openings that allow a portion of the drive shaft 1020 to pass through. The end of the drive shaft 1020 near the external stage 1030 is mounted to a slip ring 1038, which is further mounted to the external stage. In this way, rotation of the drive shaft 1020 relative to the stationary base 1006 causes the external stage 1030 to rotate about a central vertical axis. As will be discussed in more detail below, the IMU 1002 is calibrated in part by rotating the IMU about the central vertical axis.

[0352] In the exemplary embodiment herein, the external stage 1030 comprises a one-piece U-shaped cross-section having corresponding opposed forked attachments 1042. Each attachment 1042 is mounted to a roller bearing assembly 1046, which is received and pivotally mounted to a central shaft 1050. Each central shaft 1050 is also mounted to opposite sides of an internal platform 1054, which sits between the forked attachments 1042. The internal platform 1054 comprises a one-piece U-shaped cross-section that fits within the corresponding opposed forked attachments 1042 and includes a base having a plurality of upstanding protrusions 1058. As will be described in more detail below, the upstanding protrusions 1058 are each configured to engage a corresponding notch associated with each IMU 1002 to secure the position of the IMU relative to a portion of the calibration tool 1000. Each central shaft 1050 is longitudinally aligned along a central axis and mounted to an inner platform 1054 such that rotation of the central shaft corresponds to rotation of the inner platform 1054 relative to the outer stage 1030 .

[0353] In order to rotate the inner platform 1054 relative to the outer table 1030, the calibration tool includes a pulley 1060 mounted to one of the central shafts 1050. Specifically, one of the central shafts 1050 is longer than the other to accommodate the mounting of the pulley 1060 and the corresponding rotation of the pulley using a drive belt 1064 that also engages an electric motor 1068. In this exemplary embodiment, the driven shaft of the electric motor 1068 is mounted to its own pulley 1072, which engages the drive belt 1064 to ultimately rotate the pulley 1060 and correspondingly rotate the inner platform 1054 relative to the outer table 1030 (about the longitudinally aligned central axis of the central shaft 1050) when the electric motor is powered. The electric motor 1068 is mounted to a motor bracket 1076 that extends from the bottom side of the outer table 1030 below one of the fork-shaped attachments 1042. As will be discussed in more detail below, the IMU 1002 is calibrated in part by rotating the internal platform 1054 relative to the external stage 1030, which rotation thus rotates the IMU relative to a longitudinal central axis that is perpendicular to the central vertical axis. It will be appreciated by those skilled in the art that a third axis of rotation may be introduced to rotate the IMU about an axis that is perpendicular to both the longitudinal central axis and the longitudinal vertical axis. An exemplary calibration procedure for calibrating one or more IMUs 1002 using the calibration tool 1000 will be described below.

[0354] In an exemplary form, the IMU 1002 is preferably calibrated near the location where it will ultimately be used in surgical navigation. This may be in an operating room, more specifically, near a patient bed where the patient is located or lying. The calibration of the IMU is location specific, so that calibrating the IMU at a location far from the target application may result in a meaningful difference in the magnetic field at the calibration location and the area of ​​use (i.e., the surgical area). Therefore, the IMU 1002 is preferably calibrated near the area of ​​use.

[0355] Using the new calibration tool 1000, each IMU 1002 is mounted to one of the upstanding protrusions 1058 of the internal platform 1054. For example, each IMU 1002 is mounted to a housing having a contoured perimeter that outlines an open bottom. The contoured perimeter of the IMU 1002 housing is configured to outline the perimeter of the upstanding protrusions 1058 so that the IMU housing can be snap-fitted onto the corresponding upstanding protrusions to maintain the engagement of the IMU housing with the internal platform 1054 during the calibration process. For example, the IMU housing can have an oval, triangular, rectangular, or other sided perimeter that engages the corresponding upstanding protrusions 1058. As an exemplary discussion and illustration, the IMU housing has a rectangular opening outlined with a constant vertical cross-section that is slightly larger than the rectangular cross-section of the upstanding protrusions 1058. In an exemplary form, the calibration tool 1000 includes four upstanding protrusions 1058 to allow calibration of four IMUs 1002 at the same time. It should be noted, however, that more or less than four upstanding protrusions 1058 may be included as part of the internal platform 1054 in order to provide calibration for one or more IMUs simultaneously.

[0356] The purpose of the calibration process is to establish a zero point for the accelerometer (i.e., meaning that at a stationary position, the accelerometer provides data consistent with zero acceleration), and to plot the local magnetic field and normalize the output of the magnetometer to account for the directional variance and amount of distortion of the detected magnetic field. To calibrate the accelerometers of the IMU 1002, the internal platform 1054 remains stationary relative to the external table 1030, which also remains stationary relative to the stationary base 1006. The internal platform 1054 is placed in a first fixed stationary position relative to the external table 1030, and multiple readings are obtained from all accelerometers. The internal table is then moved to a second fixed stationary position relative to the external table 1030, and a second set of multiple readings are obtained from all accelerometers. The outputs from the accelerometers at the multiple fixed positions are recorded on an accelerometer-specific basis and used to establish zero acceleration readings for the applicable accelerometers. In addition to establishing a zero point for the accelerometer, the calibration process also plots the local magnetic field and normalizes the output of the magnetometer to account for the directional variance and amount of distortion of the detected magnetic field.

[0357] To map the local magnetic field for each magnetometer (assuming that multiple magnetometers for each IMU 1002 are located at different locations), in addition to rotating the outer stage 1030 about the drive axis 1020 and about the central vertical axis relative to the stationary base 1006, the inner platform 1054 is rotated about the central axis 1050 and about the central axis relative to the outer stage 1030. The output data from each magnetometer is recorded as the inner platform 1054 is rotated about two axes that are perpendicular to each other. The repositioning of each magnetometer about the two perpendicular axes produces a point cloud or map of the three-dimensional local magnetic field sensed by each magnetometer. Figure (calibration Figure 1-3 ) depicts exemplary local magnetic fields plotted from isometric front and top views based on data received from a magnetometer rotating on two axes simultaneously. As reflected in the local magnetic field map, the local map is embodied as an ellipsoid. This ellipsoid shape is the result of the local magnetic field distortion caused by the presence of iron or magnetic materials, which is commonly referred to as hard iron and soft iron distortion. Examples of soft iron distortions are materials with low magnetic permeability such as carbon steel, stainless steel, etc. Hard iron distortions are caused by materials such as permanent magnets.

[0358] It is inferred that if there is no local magnetic field distortion, the local magnetic field map will be spherical. Therefore, the calibration process effectively collects enough data points to describe the local magnetic field at different orientations through manual operation of the calibration tool 1000 or the IMU. The calibration algorithm calculates correction factors to map the distorted elliptical local magnetic field into a uniform spherical field.

[0359] refer to Fig.184 , after calibration is complete, the local magnetic field is detected using multiple magnetometers placed in different positions relative to each other as part of the IMU 1002. In the absence of any magnetic field distortion, each magnetometer should provide data indicating exactly the same direction, such as the North Pole. However, distortions in the local magnetic field, such as the presence of iron or magnetic materials (such as surgical instruments), cause the magnetometers to provide different data for the direction of the North Pole. In other words, if the output from the magnetometers does not consistently reflect the North Pole, distortion has occurred, and the IMU 1002 may temporarily be unable to use the magnetometer data for tracking algorithms. It can also warn the user that distortion has been detected.

[0360] refer to Fig.185 and 186, an exemplary surgical tool receiving an IMU 1002 includes an electrical switch pattern or grid unique to each instrument. More specifically, each surgical tool includes a protrusion whose top surface is primarily flat, except for one or more cylindrical cavities. In an exemplary form, each IMU 1002 includes a housing defining a bottom opening, the opening being configured to receive the protrusion of the surgical tool. There are four switches in this bottom opening, each of which includes a partially cylindrical button so that when the button is pressed, the switch is closed and a corresponding signal is sent to the IMU 1002 processor. In contrast, when the button is not pressed, the switch remains open, and no corresponding signal of the switch being closed is sent to the IMU 1002 processor. In this way, the processor determines which switches are open and which switches are closed, and uses this information to identify which surgical tool the IMU 1002 is mounted to.

[0361] As part of identifying the surgical tool, 0 to 4 switches may be depressed, depending on the topology of the top surface of the protrusion. As illustrated, the protrusion of the surgical tool is received in the bottom opening of the IMU 1002 housing such that the top surface of the protrusion is pushed adjacent to the switches. It should be noted that the protrusion and the bottom opening in the IMU 1002 housing are configured such that the protrusion is received within the bottom opening in only a single rotational orientation, thereby limiting the chance of misalignment between the protrusion and the switch, which otherwise could result in misidentification of the surgical tool.

[0362] Specifically, if Fig.185As shown in , the calibration adapter surgical tool includes a single cylindrical cavity located near the right front corner of the protrusion (opposite the cut-off corner) to provide a unique configuration. Therefore, when the protrusion of the calibration adapter surgical tool is received within the bottom opening of the IMU 1002 housing, only the single switch in the 2&2 switch grid closest to the right front corner of the IMU 1002 housing is activated, which informs the IMU 1002 processor that the IMU 1002 is installed to the calibration adapter surgical tool. In contrast, in a second unique configuration, the patient anatomy mapping (PAM) registration tool adapter surgical tool includes two cylindrical cavities located near the right front and right rear corners of the protrusion. Therefore, the protrusion of the PAM adapter surgical tool is received within the bottom opening of the IMU 1002 housing, and only the two switches in the 2&2 switch grid closest to the right side of the IMU 1002 housing are activated, which informs the IMU 1002 processor that the IMU 1002 is installed to the PAM adapter surgical tool. In addition, the reamer adapter surgical tool includes two cylindrical cavities located near the front side of the protrusion (i.e., near the left front and right front corners). Therefore, when the protrusion of the reamer adapter surgical tool is received in the bottom opening of the IMU 1002 housing, only the two switches in the 2&2 switch grid closest to the front side of the IMU 1002 housing are activated, which informs the IMU 1002 processor that the IMU 1002 is installed to the reamer adapter surgical tool. Finally, the impactor adapter surgical tool includes three cylindrical cavities located near the front and right sides of the protrusion (i.e., near the left front and right front corners and the right rear corner). Therefore, when the protrusion of the impactor adapter surgical tool is received in the bottom opening of the IMU 1002 housing, only the three switches in the 2&2 switch grid closest to the front and right sides of the IMU 1002 housing are activated, which informs the IMU 1002 processor that the IMU 1002 is installed to the impactor adapter surgical tool. Those skilled in the art will appreciate that variations may be provided by providing multiple switches or electrical contacts as components of the IMU 1002 that interface with multiple protrusions, cavities, or electrical contacts associated with a surgical tool in order to specifically identify the surgical tool to which the IMU 1002 is mounted.

[0363] Identifying the surgical tool to which the IMU 1002 is mounted is important for accurate surgical navigation. Specifically, a surgical navigation system in accordance with the present disclosure has a software package that has been pre-installed with a CAD model or surface model of each surgical tool to which the IMU 1002 may be mounted. In doing so, the software package knows the relative dimensions of each surgical tool, such as but not limited to the length in the X direction, the width in the Y direction, and the height in the Z direction, and how these dimensions change along the length, width, and height of the surgical tool. Therefore, when the IMU 1002 is mounted in a known position of a surgical tool, the position and orientation information (via a gyroscope, accelerometer, and magnetometer) from the IMU 1002 can be converted into the position and orientation information of the surgical tool. Therefore, by tracking the IMU 1002 in 3D space, the software package can track the surgical tool to which the IMU 1002 is mounted in 3D space and relay the position and orientation to a user, such as a surgeon or a surgeon's assistant.

[0364] In an exemplary form, the software package includes a visual display that effectively displays each surgical tool as a 3D model. When IMU 1002 is mounted to a surgical tool, the IMU 1002 processor sends data to the software package, which allows the software package to identify which surgical tool the IMU 1002 is mounted to. After making this identification, the software package displays a 3D model of a surgical tool mounted to the IMU 1002 in an orientation consistent with the orientation information derived from the IMU. In addition to providing orientation information in real time by operating the 3D virtual model of the surgical tool, the software package also provides real-time data about the position of the surgical tool by using a second reference IMU 1002 mounted to a reference object (i.e., the patient's bone). But before the software package can provide meaningful position information, it is necessary to align the IMU 1002 (IMU#1 mounted to the surgical tool and IMU#2 mounted to the reference object (i.e., bone)) relative to each other.

[0365] In the case of total hip replacement surgery, in an exemplary form, such as Figures 103-110As shown in , by engaging the patient's anatomical structure with a predetermined orientation, the template surgical plan is regenerated using the registration tool. When each practical IMU 1002 is mounted to its registration tool (one for the femur and the second for the pelvis), the registration tool is mounted to the relevant bone with a predetermined orientation (only one orientation that accurately matches the patient's anatomical structure to the IMU "zero point"). In order to perform this registration, the second reference IMU is accurately mounted to the bone in question (one IMU is mounted to the pelvis and the second IMU is mounted to the femur). In other words, a practical IMU is mounted to the acetabulum registration tool, and the second reference IMU is accurately mounted to the pelvis. In the case of the femur, a practical IMU is mounted to the femur registration tool, and the second reference IMU is accurately mounted to the femur. As part of the registration process, the software of the computer uses the output from the two IMUs (practical and reference) to calculate the "zero" position of the practical IMU when the registration tool is finally stationary and located in its unique position and orientation. The IMU 1002 can then be removed from the associated registration tool and mounted to the surgical tool (reamer, saw, implant placement guide, etc.) in a predetermined manner to ensure the correct orientation and placement of the surgical tool. The IMU 1002 can be successively mounted to and removed from each surgical tool until the surgical operation is completed.

[0366] In this exemplary embodiment, acetabular registration tool comprises elongated rod, and it has the unique protrusion that is shaped to fit in patient's acetabular cup only in single orientation (comprising rotational position and angular position).The proximal end of registration tool comprises IMU 1002 registration sleeve to receive IMU 1002, so that when IMU 1002 is locked in sleeve, IMU 1002 is rigidly fixed with respect to registration tool and unique protrusion.Consistent with registration tool, second reference IMU 1002 is rigidly fixed to known pelvis of position.When the unique protrusion of described registration tool is correctly oriented in patient's acetabular cup (and the IMU 1002 that is locked in registration sleeve and the IMU 1002 that is installed to pelvis are activated), the orientation (it is set when described unique protrusion is received in acetabular cup in only single correct orientation) of the IMU 1002 that is locked to registration sleeve with respect to planned implant cup orientation is known. The operator indicates to the software system that the IMUs are in the correct position, and the software records the position of each IMU. The registration tool (with the IMU 1002 locked in the sleeve) is removed from the anatomy, and the IMU 1002 is then removed from the registration sleeve in preparation for mounting the IMU 1002 to the surgical tool.

[0367] For example, an IMU 1002 previously mounted to an acetabular registration tool is removed from the tool and mounted to a surgical tool at a known location. In an exemplary form, the IMU 1002 (previously mounted to the acetabular registration tool) is rigidly fixed to the acetabular cup reamer in a known orientation relative to the reaming direction, so that the orientation of the acetabular cup reamer relative to the pelvis is known and dynamically updated by two IMUs (the IMU 1002 mounted to the acetabular cup reamer and the IMU 1002 mounted to the pelvis).

[0368] The software program provides the surgeon with a graphical user interface that displays a virtual model of the patient's pelvis and the surgical tool in question, in this case the acetabular cup reamer (the virtual model of the patient's pelvis has been completed in accordance with the virtual template making steps, and for the specific acetabular cup reamer and other surgical tools that can be used, the virtual model of the acetabular cup reamer or other surgical tools has been previously loaded into the system), and updates the orientation of the pelvis and surgical tools in real time through the graphical user interface to provide position and orientation information to the surgeon. Instead of using a graphical user interface, the system can include a surgical device with an indicator light that indicates to the surgeon whether the reamer is correctly oriented, and if not correctly oriented, the direction to which the reamer needs to be repositioned to correctly orient the reamer to be consistent with the preoperative plan. After the surface resurfacing is completed using the acetabular cup reamer, the IMU 1002 is removed from the acetabular cup reamer and rigidly fixed to the inserter in an orientation known relative to the acetabular cup inserter. The acetabular cup inserter is then used to place the acetabular cup implant, while the IMU continues to provide acceleration feedback, which the software uses to calculate the position to provide real-time feedback about the position of the pelvis relative to the acetabular cup inserter. In terms of drilling in the pelvis before or after the acetabular cup is placed, the IMU 1002 previously mounted to the alignment tool can be rigidly fixed to the surgical drill to ensure the correct orientation of the drill relative to the pelvis. Similar alignment tools and complete sets of IMUs can be used together with the software system to assist in the placement of the femoral stem assembly.

[0369] In an exemplary embodiment, the femoral registration tool includes an elongated rod having a distal form shaped to partially fit on the femoral neck of the patient in only one orientation (including rotational position and angular position). The proximal end of the registration tool includes an IMU 1002 registration sleeve to receive the IMU 1002, so that when the IMU 1002 is locked in the sleeve, the IMU 1002 is rigidly fixed relative to the registration tool and the distal form. In line with the registration tool, a second reference IMU 1002 is rigidly fixed to the femur of a known position. When the distal form of the registration tool is correctly oriented relative to the femoral neck of the patient (and the IMU 1002 locked in the registration sleeve and the IMU 1002 mounted to the femur are activated), the orientation of the IMU 1002 locked to the registration sleeve relative to the femoral orientation (which is set when the distal form is received on the femoral neck in only a single correct orientation) is known. The operator indicates to the software system that the IMUs are in the correct position, and the software records the position of each IMU. The registration tool (with the IMU 1002 locked in the sleeve) is removed from the anatomy, and the IMU 1002 is then removed from the registration sleeve in preparation for mounting the IMU 1002 to the surgical tool.

[0370] For example, an IMU 1002 previously mounted to a femur registration tool is removed from the tool and mounted to another surgical tool whose position is known. In an exemplary form, the IMU 1002 (previously mounted to the femur registration tool) is rigidly fixed to a surgical saw in a known position, such that the movement of the IMU 1002 is correspondingly translated into the known movement of the surgical saw. Considering that another IMU 1002 is fixedly mounted to the femur in a known position, the IMUs work together to provide the software system with dynamically updated information (via acceleration data) regarding the position changes of both the femur and the surgical saw.

[0371] The software program provides the surgeon with a graphical user interface that displays a virtual model of the patient's femur and the surgical tool in question, in this case a surgical saw (the virtual model of the patient's femur has been completed in accordance with the virtual template preparation steps, and for the specific surgical saw and other surgical tools that can be used, the virtual model of the surgical saw or other surgical tools has been previously loaded into the system), and updates the orientation of the femur and surgical tools in real time through the graphical user interface to provide position and orientation information to the surgeon. Instead of using a graphical user interface, the system may include a surgical device with an indicator light that indicates to the surgeon whether the surgical saw is correctly oriented, and if not correctly oriented, the direction in which the surgical saw needs to be repositioned in order to correctly orient the surgical saw to make the bone cut consistent with the preoperative plan. After the necessary bone cut is made, the IMU 1002 is removed from the surgical saw and rigidly fixed to the reamer (to properly expand the intramedullary canal), and then mounted to the femoral stem inserter in a known orientation relative to the inserter orientation. A femoral stem inserter is then used to place a femoral stem implant in the expanded intramedullary canal while the IMU continues to provide acceleration feedback, which the software uses to calculate the position of the femur and femoral stem inserter in real time and display this position data to the surgeon via a graphical user interface.

[0372] In an exemplary form, in the context of total shoulder replacement surgery, such as Fig.187 and 188 As shown in , the template surgical plan is regenerated using the registration tool by engaging the patient's anatomy in a predetermined orientation. When each practical IMU 1002 is mounted to its registration tool (one for the humerus and the second for the scapula), the registration tool is mounted to the relevant bone in a predetermined orientation (only one orientation that accurately matches the patient's anatomy to the IMU "zero point"). In order to perform this registration, the second reference IMU is accurately mounted to the bone in question (one IMU is mounted to the humerus and the second IMU is mounted to the scapula). In other words, a practical IMU is mounted to the humerus registration tool, and the second reference IMU is accurately mounted to the humerus. In the case of the scapula, a practical IMU is mounted to the scapula registration tool, and the second reference IMU is accurately mounted to the scapula. As part of the registration process, the computer's software uses the output from the two IMUs (practical and reference) to calculate the "zero" position of the practical IMU when the registration tool is finally stationary and located in its unique position and orientation. The IMU 1002 can then be removed from the associated registration tool and mounted to the surgical tool (reamer, saw, implant placement guide, etc.) in a predetermined manner to ensure the correct orientation and placement of the surgical tool. The IMU 1002 can be successively mounted to and removed from each surgical tool until the surgical operation is completed.

[0373] In this exemplary embodiment, Fig.188 As shown in, the scapula registration tool includes an elongated rod having a unique protrusion that is shaped to fit in the patient's scapular glenoid only in a single orientation (including rotational position and angular position). The proximal end of the registration tool includes an IMU 1002 registration sleeve to receive the IMU 1002, so that when the IMU 1002 is locked in the sleeve, the IMU 1002 is rigidly fixed relative to the registration tool and the unique protrusion. In accordance with the registration tool, the second reference IMU 1002 is rigidly fixed to the scapula with a known position. When the unique protrusion of the registration tool is correctly oriented in the patient's scapular glenoid (and the IMU 1002 locked in the registration sleeve and the IMU 1002 mounted to the scapula are activated), the orientation of the IMU 1002 locked to the registration sleeve relative to the planned implant scapular glenoid orientation (which is set when the unique protrusion is received in the scapular glenoid in only a single correct orientation) is known. The operator indicates to the software system that the IMUs are in the correct position, and the software records the position of each IMU. The registration tool (with the IMU 1002 locked in the sleeve) is removed from the anatomy, and the IMU 1002 is then removed from the registration sleeve in preparation for mounting the utility IMU 1002 to other surgical tools.

[0374] For example, an IMU 1002 previously mounted to a scapula registration tool is removed from the tool and mounted to a surgical tool at a known location. In an exemplary form, the IMU 1002 (previously mounted to the scapula registration tool) is rigidly fixed to the glenoid reamer in a known orientation relative to the reaming direction, such that the orientation of the glenoid reamer relative to the scapula is known and dynamically updated by two IMUs (the IMU 1002 mounted to the glenoid reamer and the IMU 1002 mounted to the scapula).

[0375] The software program provides the surgeon with a graphical user interface that displays a virtual model of the patient's scapula and a virtual model of the surgical tool in question, in this case a glenoid reamer (the virtual model of the patient's scapula has been completed in accordance with the virtual templating steps, and for the specific glenoid reamer and other surgical tools that can be used, the virtual model of the glenoid reamer or other surgical tools has been previously loaded into the system), and updates the orientation of the scapula and surgical tools in real time through the graphical user interface to provide position and orientation information to the surgeon. Instead of using a graphical user interface, the system may include a surgical device with an indicator light that indicates to the surgeon whether the reamer is correctly oriented and, if not, the orientation to which the reamer needs to be repositioned in order to correctly orient the reamer to be consistent with the preoperative plan. After the resurfacing is completed using the glenoid reamer, the practical IMU 1002 is removed from the glenoid reamer and rigidly fixed to the inserter in an orientation known relative to the glenoid inserter. The glenoid implant is then placed using the glenoid inserter while the IMU continues to provide acceleration feedback that the software uses to calculate position to provide real-time feedback about the position of the scapula relative to the glenoid inserter. In the case of drilling a hole in the scapula before or after glenoid placement, the practical IMU 1002 previously mounted to the alignment tool can be rigidly fixed to the surgical drill to ensure the correct orientation of the drill relative to the scapula. Similar alignment tools and sets of IMUs can be used with the software system to assist in the placement of the humeral stem assembly.

[0376] In an exemplary embodiment, the humeral registration tool includes an elongated rod having a distal form shaped to partially fit on the patient's humeral neck in only one orientation (including rotational position and angular position). The proximal end of the registration tool includes an IMU 1002 registration sleeve to receive the IMU 1002, so that when the IMU 1002 is locked in the sleeve, the IMU 1002 is rigidly fixed relative to the registration tool and the distal form. In accordance with the registration tool, a second reference IMU 1002 is rigidly fixed to the humerus of known position. When the registration tool is correctly oriented relative to the humeral neck (and the IMU 1002 locked in the registration sleeve and the IMU 1002 mounted to the humerus are activated), the orientation of the IMU 1002 locked to the registration sleeve relative to the humeral orientation (which is set when the distal form is received on the humeral neck in only a single correct orientation) is known. The operator indicates to the software system that the IMU is in the correct position, and the software records the position of each IMU. The registration tool (with the IMU 1002 locked in the sleeve) is removed from the anatomy, and the utility IMU 1002 is then removed from the registration sleeve in preparation for mounting the IMU 1002 to other surgical tools.

[0377] For example, an IMU 1002 previously mounted to a humerus registration tool is removed from the tool and mounted to another surgical tool whose position is known. In an exemplary form, the IMU 1002 (previously mounted to the humerus registration tool) is rigidly fixed to a surgical saw in a known position, such that the movement of the IMU 1002 is correspondingly translated into the known movement of the surgical saw. Considering that the reference IMU 1002 is fixedly mounted to the humerus in a known position, the IMUs work together to provide the software system with dynamically updated information (via acceleration data) regarding the position changes of both the humerus and the surgical saw.

[0378] The software program provides the surgeon with a graphical user interface that displays a virtual model of the patient's humerus and a virtual model of the surgical tool in question, in this case a surgical saw (the virtual model of the patient's humerus has been completed in accordance with the virtual template preparation steps, and for the specific surgical saw and other surgical tools that can be used, the virtual model of the surgical saw or other surgical tools has been previously loaded into the system), and updates the orientation of the humerus and surgical tools in real time through the graphical user interface to provide position and orientation information to the surgeon. Instead of using a graphical user interface, the system may include a surgical device with an indicator light that indicates to the surgeon whether the surgical saw is properly oriented, and if not properly oriented, the direction in which the surgical saw needs to be repositioned in order to properly orient the surgical saw to make the bone cut consistent with the preoperative plan. After the necessary bone cut is made, the practical IMU 1002 is removed from the surgical saw and rigidly fixed to the reamer (to properly expand the humeral medullary canal), and then mounted to the humeral stem inserter in a known orientation relative to the inserter orientation. The humeral stem inserter is then used to place the humeral stem implant in the expanded medullary canal while the IMU continues to provide acceleration feedback, which the software uses to calculate the position of the humerus and humeral stem inserter in real time and display this position data to the surgeon via a graphical user interface.

[0379] In an exemplary form, in the case of a reverse shoulder replacement surgery, such as Fig.187As shown in Figures 1 and 188, the template surgical plan is regenerated using the registration tool by engaging the patient's anatomy in a predetermined orientation. When each practical IMU 1002 is mounted to its registration tool (one for the humerus and the second for the scapula), the registration tool is mounted to the relevant bone in a predetermined orientation (only one orientation that accurately matches the patient's anatomy to the IMU "zero point"). In order to perform this registration, a second reference IMU is accurately mounted to the bone in question (one IMU is mounted to the humerus and the second IMU is mounted to the scapula). In other words, one practical IMU is mounted to the humerus registration tool and the second reference IMU is accurately mounted to the humerus. In the case of the scapula, one practical IMU is mounted to the scapula registration tool and the second reference IMU is accurately mounted to the scapula. As part of the registration process, the computer's software uses the output from the two IMUs (practical and reference) to calculate the "zero" position of the practical IMU when the registration tool is finally stationary and located in its unique position and orientation. The IMU 1002 can then be removed from the associated registration tool and mounted in a predetermined manner to the surgical tool (reamer, saw, inserter, drill guide, drill, etc.) to ensure proper orientation and placement of the surgical tool. The IMU 1002 can be sequentially mounted and removed from each surgical tool until the surgical operation is completed.

[0380] In this exemplary embodiment, Fig.188 As shown in, the scapula registration tool includes an elongated rod having a unique protrusion that is shaped to fit in the patient's scapular glenoid only in a single orientation (including rotational position and angular position). The proximal end of the registration tool includes an IMU 1002 registration sleeve to receive the IMU 1002, so that when the IMU 1002 is locked in the sleeve, the IMU 1002 is rigidly fixed relative to the registration tool and the unique protrusion. In accordance with the registration tool, the second reference IMU 1002 is rigidly fixed to the scapula with a known position. When the unique protrusion of the registration tool is correctly oriented in the patient's scapular glenoid (and the IMU 1002 locked in the registration sleeve and the IMU 1002 mounted to the scapula are activated), the orientation of the IMU 1002 locked to the registration sleeve relative to the planned implant scapular glenoid orientation (which is set when the unique protrusion is received in the scapular glenoid in only a single correct orientation) is known. The operator indicates to the software system that the IMUs are in the correct position, and the software records the position of each IMU. The registration tool (with the IMU 1002 locked in the sleeve) is removed from the anatomy, and the IMU 1002 is then removed from the registration sleeve in preparation for mounting the utility IMU 1002 to other surgical tools.

[0381] For example, an IMU 1002 previously mounted to a scapula registration tool is removed from the tool and mounted to a surgical tool at a known location. In an exemplary form, the IMU 1002 (previously mounted to the scapula registration tool) is rigidly fixed to the glenoid reamer in a known orientation relative to the reaming direction, such that the orientation of the glenoid reamer relative to the scapula is known and dynamically updated by two IMUs (the IMU 1002 mounted to the glenoid reamer and the IMU 1002 mounted to the scapula).

[0382] The software program provides the surgeon with a graphical user interface that displays a virtual model of the patient's scapula and a virtual model of the surgical tool in question, in this case a glenoid reamer (the virtual model of the patient's scapula has been completed in accordance with the virtual templating steps, and for the specific glenoid reamer and other surgical tools that can be used, the virtual model of the glenoid reamer or other surgical tools has been previously loaded into the system), and updates the orientation of the scapula and surgical tools in real time through the graphical user interface to provide position and orientation information to the surgeon. Instead of using a graphical user interface, the system may include a surgical device with an indicator light that indicates to the surgeon whether the reamer is correctly oriented and, if not, the direction in which the reamer needs to be repositioned to correctly orient the reamer to be consistent with the preoperative plan. After the resurfacing is completed using the glenoid reamer, the practical IMU 1002 is removed from the glenoid reamer and rigidly fixed to the drill plate in a known orientation and position. The drill plate is then used to drill a hole in the scapula while the IMU continues to provide acceleration feedback, which the software uses to calculate position to provide real-time feedback about the position of the scapula relative to the drill plate, and then positions the glenoid baseplate and installs the glenoid assembly ball. Although not required, the utility IMU 1002 can be rigidly fixed to the surgical drill to ensure the correct orientation of the drill relative to the drill plate when drilling through the drill plate. Similar alignment tools and set IMUs can be used with the software system to assist in the placement of the humeral stem assembly.

[0383] In one exemplary embodiment, the humeral registration tool comprises an elongated rod having a distal form shaped to partially fit over the patient's humeral neck in only a single orientation (including rotational and angular positions). The proximal end of the registration tool comprises an IMU 1002 registration sleeve to receive the IMU 1002, so that when the IMU 1002 is locked in the sleeve, the IMU 1002 is rigidly fixed relative to the registration tool and the distal form. In accordance with the registration tool, a second reference IMU 1002 is rigidly fixed to the humerus at a known position. When the registration tool is correctly oriented relative to the humeral neck (and the IMU 1002 locked in the registration sleeve and the IMU 1002 mounted to the humerus are activated), the orientation of the IMU 1002 locked to the registration sleeve relative to the humeral orientation (which is set when the distal form is received on the humeral neck in only a single correct orientation) is known. The operator indicates to the software system that the IMUs are in the correct position and are stationary, and the software records the position of each IMU as the "zero point" of the utility IMU. The registration tool (with the IMU 1002 locked in the sleeve) is removed from the anatomy, and the utility IMU 1002 is then removed from the registration sleeve in preparation for mounting the IMU 1002 to other surgical tools.

[0384] For example, an IMU 1002 previously mounted to a humeral registration tool is removed from the tool and mounted to another surgical tool whose position is known. In an exemplary form, the IMU 1002 (previously mounted to the humeral registration tool) is rigidly fixed to a humeral resection anvil in a known position so that the movement of the IMU 1002 is correspondingly converted into the known movement of the resection anvil. Considering that the reference IMU 1002 is fixedly mounted to the humerus in a known position, the IMUs work together to provide the software system with dynamically updated information (via acceleration data) about the position changes of both the humerus and the resection anvil.

[0385] The software program provides a graphical user interface for the surgeon, which displays a virtual model of the patient's humerus and the surgical tool in question, in this case a virtual model of the humeral resection anvil (the virtual model of the patient's humerus has been completed according to the virtual template making steps, and for the specific resection anvil and other surgical tools that can be used, the virtual model of the resection anvil or other surgical tools has been previously loaded into the system), and updates the orientation of the humerus and the surgical tools in real time through the graphical user interface, providing position and orientation information to the surgeon. Without using a graphical user interface, the system may include a surgical device with an indicator light, which indicates to the surgeon whether the resection anvil is correctly oriented, and if it is not correctly oriented, the resection anvil needs to be repositioned to which direction to correctly orient the resection anvil to make a bone cut consistent with the preoperative plan. In addition or alternatively, a practical IMU 1002 can be mounted to a drill plate for drilling one or more holes each with a reference pin inserted therein. In this case, if the surgical resection anvil is correctly positioned and oriented using one or more reference pins, the resection anvil may not need to be accompanied by an IMU. In any case, after the necessary bone cuts are made, the utility IMU 1002 is removed from the surgical tool and rigidly fixed to the reamer (to properly expand the humeral medullary canal), and then mounted to the humeral stem inserter in a known orientation relative to the inserter. The humeral stem inserter is then used to place the humeral stem implant in the expanded medullary canal while the IMU continues to provide acceleration feedback, which the software uses to calculate the position of the humerus and humeral stem inserter in real time and display this position data to the surgeon via a graphical user interface.

[0386] In addition to component placement, IMUs mounted to the pelvis and femur can be used to track component rotation to test for potential tampering of the components to prevent post-operative complications and improve overall patient satisfaction.

[0387] Based on the above disclosure of using IMU 1002, the following is an exemplary discussion of mathematical models and algorithms for generating three-dimensional position data from the gyroscope, accelerometer and magnetometer of each IMU. In an exemplary form, each IMU processor is programmed to calculate the position change of IMU 1002 based on the input from the gyroscope, accelerometer and magnetometer of the IMU using the Sequential Monte Carlo method (SMC) with the von Mises-Fisher density algorithm. The IMU data stream consists of 1 set of gyroscope data (G1) on the three axes of X, Y, and Z, 3 sets of accelerometer data (A1-A3) on the three axes of X, Y, and Z, and 3 sets of magnetometer data (M1-M3) on the three axes of X, Y, and Z. The orientation tracking of IMU 1002 can be achieved using 1 set of data from each sensor (i.e., G1, A1, M1).

[0388] Using G1, A1, and M1 as examples, and assuming that all sensor raw data has been transformed and processed:

[0389] At time and state = 1:

[0390] 1) As represented by Algorithm 1 identified below, the algorithm first generates a set of N particles around a central position with a predetermined von Mises-Fisher density dispersion coefficient. Each particle represents an orientation around the X, Y, and Z axes in quaternion form. In other words, the particles contain a set of independent and identically distributed random variables drawn from the same probability density space. In orientation tracking applications, the particles are statistically constrained variability of the observed orientations. It should be noted, however, that the exact statistic (dispersion coefficient) does not have to be "known" because the algorithm optimizes its properties as it collects more samples. It is preferable to use a higher variability as an initial guess and allow the algorithm to refine it.

[0391]

[0392] After receiving the first data set from G1, A1 and M1, an estimate of the current orientation of the IMU is calculated. This is achieved by first knowing the tilt measured from A1. As shown in steps 2 and 3 of Algorithm 2 identified below, in order to mathematically correct (de-rotate) the magnetometer readings, the tilt information is required. Subsequently, the A1 and M1 data are used to estimate the initial orientation of the IMU through Algorithm 2 based on the Gauss-Newton optimization method. The goal of Algorithm 2 is to iteratively determine the orientation (q obv ) such that the estimated tilt and heading components are identical to the readings from A1 and M1 with an acceptable margin of error. It should be noted that although Algorithm 2 requires input from a previous state, since there is no previous state at time = 1, any input can be used. The reason why accelerometers and magnetometers cannot be used alone to track orientation is due to limitations in how accelerometers measure tilt. For example, it is possible that in several specific orientations, the output of tilt may be the same despite the IMU assuming different orientations due to the nature of the quadrants of triangular geometry. Therefore, in order to keep track of which quadrant the IMU is in, a gyroscope is necessary.

[0393]

[0394] Next, the group of N particles (q νMF ) so that their mean is centered about the orientation estimates from A1 and M1:

[0395]

[0396] Then, all particles are estimated forward in time based on G1 using the following equation:

[0397]

[0398] where ω is the angular velocity measured at time t, and Δt is the sampling duration.

[0399] In other words, if G1 indicates an angular velocity around the X-axis, all particles will rotate around the X-axis based on Newton's equations of motion.

[0400] The orientation in the current state is expected by using the algorithm 3 identified as follows to estimate the particle value (q est,i (t+1)) is averaged to obtain. Since quaternions are four-dimensional vectors, the averaging is performed in a different way. Algorithm 3 iteratively interpolates two quaternions from a particle group until only one remains.

[0401]

[0402] At time and state = 2, a second data set is received. Using the same method as described in paragraph

[0380] (Algorithm 2), the latest orientation estimate is calculated and then compared with all particle estimates from previous states (q est,i The error / residual between each particle and the current orientation estimate is used to weight the accuracy of the particle (i.e. particles closer to the estimate receive higher weights than particles further away) using the following equation:

[0403] δ res，i =2cos(q res，i (t)·q0)

[0404]

[0405] δ res,i is the residual 3D angular difference between the particle and the current observation.

[0406] w i is the weight of the particle.

[0407] Next, the quality of the particles is evaluated to eliminate and resample particles with very low weights. This can be done using a deterministic, residual, or auxiliary resampling schedule. As the algorithm favors particles that are closer to the observed data, the particle group will begin to lose diversity over time. The particles will become highly concentrated and no longer carry any statistical significance. At that time, a small portion of the particles is replaced to increase diversity. This is done by first evaluating the current dispersion coefficient of the particles. If the dispersion coefficient indicates a high concentration, a new set of particles is generated at an intermediate position based on a predetermined dispersion coefficient to replace a portion of the current particles. The new particles are rotated from the intermediate position to the current orientation expected value. This is summarized in the equation below:

[0408]

[0409]

[0410] Furthermore, since the algorithm of this SMC method is time-dependent, delays in the received signal or a temporary loss of contact with the IMU data stream can adversely affect the estimate. If the contact with the IMU data stream is not closely monitored, the particle group can diverge and make the filter unstable. The algorithm of this SMC method tracks the properties of the particle group after each iteration to prevent excessive divergence.

[0411] Finally, the particle is estimated forward in time based on the new data from G1 and the current orientation state is recalculated. The above process and algorithm are reused each time new data is received from G1, A1 and M1.

[0412] Production of trauma protection plate

[0413] refer to Figure 112-125 , an exemplary process and system for producing bone plates (i.e., trauma plates) in a predetermined population are described. Those skilled in the art recognize that bones are able to undergo regeneration to repair themselves after a fracture. Depending on the severity and location of the fracture, the trauma plates of the prior art used often need to be bent or otherwise modified in the operating room to conform to the irregular bone shape and achieve maximum contact between the bone fragments. However, excessive bending reduces the service life of the trauma plate, which may cause plate failure and / or loosening of the trauma plate screw fixation. The present process and system provide a more accurate trauma plate shape to reduce or eliminate the necessity to contour the plate during surgery, thereby increasing the service life of the plate and increasing the time before any loosening of the plate screw fixation occurs.

[0414] The above exemplary explanation of producing a trauma plate is applicable to any and all bones to which a trauma plate may be applied. For the sake of brevity, the exemplary explanation describes a system and process for producing a trauma plate for the humerus. However, it should be understood that the process and system are equally applicable to the manufacture of other bones of the body and corresponding trauma plates, and are in no way limited to humeral trauma plates.

[0415] As part of an exemplary process and system for producing a trauma plate, a skeletal atlas is produced and / or used for the bones in question. By way of explanation, the bones in question include the humerus. Those skilled in the art are familiar with atlases and how to construct atlases in the case of one or more bones. Therefore, for the sake of further brevity, a detailed discussion of constructing a skeletal atlas is omitted. However, what may be unique to the skeletal atlas of the exemplary system and process is the classification of the humerus within the skeletal atlas based on gender, age, race, deformation and / or partial construction. In this way, one or more trauma plates can be mass-customized for one or more of the above-mentioned categories, wherein the one or more categories establish specific bone populations.

[0416] In exemplary form, the skeletal statistical atlas comprises anatomical data that can take various forms. For example, the skeletal statistical atlas can include two-dimensional or three-dimensional images and information about the skeletal parameters from which measurements can be obtained. Exemplary atlas input data can take the form of X-ray images, CT scan images, MRI images, laser scanning images, ultrasonic images, segmented bones, physical measurement data and any other information from which a skeletal model can be produced. The software that accesses the statistical atlas data is used to build a three-dimensional skeletal model (or accesses a three-dimensional skeletal model that has been produced and preserved as a part of the statistical atlas), and the software is run to produce a three-dimensional average skeletal model or a template skeletal model from the model.

[0417] Using the template bone model, the software can automatically assign or allow manual assignment of points on the outer surface of the template bone model. As an explanation, in the case of an average humeral model, the software user establishes an overall boundary shape for the final trauma protection plate by roughly outlining the shape of the trauma protection plate on the outer surface of the humeral model. The overall boundary shape of the trauma protection plate can also be obtained by assigning a series of points corresponding to the outer boundary on the outer surface of the humeral model by the user. Once the outer boundary or boundary point is established, the software can automatically assign or allow manual assignment of points within the established boundary on the outer surface of the humeral model. For example, the software provides a fill percentage operation, on which the user can assign the percentage assigned by a series of points within the boundary of the trauma protection plate, each of which corresponds to a unique position on the outside of the humeral model. In addition, the software provides a manual point assignment feature, on which the user can assign one or more points within the boundary on the outer surface of the humeral model. It should be noted that in the case of using manual point assignment, the user does not have to establish a boundary as a preliminary step for assigning points on the outside of the humeral model. Instead, when the manual assignment of points is completed, the boundary is established by the outermost point assigned.

[0418] After assigning points on the outer surface of the template bone model, the located points are propagated to the entire bone population in question. Specifically, the located points are automatically applied to each three-dimensional bone model in a given population by the software through the point correspondence of the statistical atlas. For example, a given bone population can be gender and race specific to include humeri from Caucasian women. Using the propagated points of each bone model of the population, the software uses a three-dimensional filling process to fill the gaps between the points within the boundary, generating a three-dimensional rendering of the trauma protection plate for each bone. Subsequently, the software calculates the longitudinal midline of the three-dimensional rendering of each trauma protection plate through a refinement process.

[0419] The centerline of each three-dimensional wound protection plate rendering contains a three-dimensional centerline with various curvatures along its length. The software extracts the three-dimensional centerline and uses least squares fitting to determine the preferred number of curvature radii that synergistically and optimally approximate the main curvature of the three-dimensional centerline. In the case of the humerus, three curvature radii have been determined to accurately approximate the centerline curvature. However, this number may vary with the boundaries of the bone population and the wound protection plate. Other features may also be included here, such as the cross-sectional curvature at one or more positions along the length of the plate, the position of muscles, nerves and other soft tissues that need to be avoided, or any other features related to determining the size or shape of the plate. For example, the three curvature radii used for the centerline represent the bend in the wound protection plate in the proximal humerus, the transition between the humeral shaft and the humeral head, and the curvature of the humeral shaft. Record each radius of curvature, and apply a four-dimensional feature vector to the radius of curvature data to cluster the radius into a group that best fits the population. In an exemplary form, the cluster data may indicate that multiple wound protection plates are required in order to correctly fit the population. Once the radius of curvature data is clustered, the dimensions of the wound protection plate can be finally determined.

[0420] After the features relevant to the plate design have been extracted, the software determines the optimal number of clusters to fit the population. It must be noted that in some cases two or more clusters provide local minima, e.g. Fig.120 As outlined in . In order to determine the most suitable choice that provides acceptable error tolerance and reasonable number of plates in each family, the software generates a three-dimensional surface model of the plates in each cluster. Then, by placing those plates on the colony and calculating the mismatch between the plate and the bone surface, automatic evaluation is performed. The results of this analysis allow the software to pick the most suitable number of plates that will be used for this particular colony. The final plate model is then parameterized, and the screw position is placed on each plate in a manner that avoids muscle and soft tissue positions and maximizes fixation. The width of the screw is determined by a cross-sectional analysis of the bone at each screw height in the entire colony.

[0421] This process and method of the humerus was validated using a cadaveric study. Specifically, CT scans of cadaveric humeri were obtained from Caucasian females. These CT scans were used by the software to generate separate three-dimensional models for each humerus. It should be noted that neither the CT scans nor the three-dimensional models used during this validation study were part of the statistical atlas and related populations used to generate humeral trauma protection plates. Therefore, the CT scans or three-dimensional models contain new data and models for validating the designed humeral trauma protection plates. After the three-dimensional validation model is generated, each model is classified into a specific cluster (the cluster is derived from designing humeral trauma protection plates from the design population). Based on the cluster to which the validation model is classified, the humeral trauma protection plate designed for the cluster is fitted to a suitable validation three-dimensional humeral bone model, and the measurements showing any intervals between the outer surface of the validation three-dimensional humeral bone model and the bottom surface of the humeral trauma protection plate are calculated. Fig.124 A distance plot of a trauma plate fitted to a validation three-dimensional humeral bone model is depicted, showing the areas of greatest distance between the bone and the trauma plate. As can be seen, the majority of the trauma plate has minimal separation from the bone, while areas of less conformance only show separation in the range of 0.06 to 0.09 centimeters. Thus, the conclusions of this cadaveric study determined that the trauma plate designed using the above system in accordance with the above exemplary process has an exceptional contour match, which, when applied intraoperatively, avoids the surgeon's practice of having to bend or manually reshape the plate.

[0422] refer to Figures 131-138 In another exemplary case of this process, a trauma shield is generated for a clavicle. Here, a statistical atlas is generated from a large number of clavicles that adequately captures the variability within, for example, a Caucasian population. It should be noted that the statistical atlas can include clavicles from a large number of ethnicities, from a large number of patient ages, and from a variety of different geographic regions. The exemplary disclosure happens to be in the context of a Caucasian population data set, although those skilled in the art will appreciate that the described systems and methods are not limited to a Caucasian population statistical atlas. Fig.132 The general clavicle anatomy is illustrated.

[0423] In an exemplary form, the statistical atlas of clavicles also defines locations associated with muscle attachment sites for each clavicle, such as Fig.134 In addition, the Fig.138 ) and at the muscle attachment sites and clavicular waist (see Fig.137) to extract cross-sectional profiles. The maximum and minimum dimensions of each cross-sectional profile were calculated. In addition, the asymmetry of the entire three-dimensional surface was examined by analyzing the size and orientation differences between homologous points across all clavicle surfaces in the dataset. The results confirmed existing research on clavicle asymmetry, namely that the left clavicle is longer than the right clavicle, but the right clavicle is thicker than the left clavicle. However, there were differences in the pattern of asymmetry between males and females, such as Fig.139 as shown in .

[0424] In addition, if Fig.133 As shown in the figure, the midline of the clavicle does not follow a symmetrical "S" shape as is the case with existing clavicle trauma guard designs. Therefore, the present disclosure confirms that current clavicle trauma guards fail to mimic the anatomical curvature of the clavicle. Fig.135 and 136 In the male clavicle, there was significant asymmetry in all dimensions and in the contours of muscle and ligament attachment sites (p < .05), whereas asymmetry in the female was more variable. However, in both sexes, asymmetry was evident in the region of the posterior midshaft where there were no muscle attachments.

[0425] The clavicle features extracted across the statistical atlas are clustered (according to the clustering method previously described in this application, which is incorporated herein by reference) to determine different similarity groupings (i.e., populations), from which each different group is associated with a specific clavicle trauma guard plate to best fit the population. In addition, bolt fixation locations and lengths are determined for each trauma guard plate population to optimally avoid soft tissue (muscle attachments) and prevent additional fractures or plate loosening as a result of bolts being too long or too short. Using the process, several clavicle trauma guard plate families corresponding to large-scale customized clavicle trauma guard plates are designed, as shown in Figures 140-149.

[0426] Generation of patient-specific trauma shields

[0427] refer to Fig.126 , the process of patient-specific wound shields is illustrated to include various components. Among these components are preoperative surgical planning, the generation of a patient-specific wound shield with a predetermined contour, intraoperative guidance for placing and fastening the patient-specific wound shield, and optional postoperative evaluation of the patient-specific wound shield. A more detailed discussion of these components and the exemplary processes and structures involved in each component are discussed in turn below.

[0428] refer to Figures 126-130, illustrating an exemplary process flow of a preoperative surgical planning component. An initial input of anatomical data is obtained for the anatomical structure in question. For exemplary purposes only, the clavicle is described as a broken or deformed anatomical structure, and the clavicle trauma plate is described as a patient-specific trauma plate. The anatomical data is input into a software package configured to select or generate a patient-specific clavicle trauma plate, wherein the anatomical data includes a two-dimensional (2D) image or a three-dimensional (3D) surface representation of the clavicle, which can, for example, take the form of a surface model or a point cloud. In the case of using 2D images, these 2D images are used to construct a 3D virtual surface representation of the broken clavicle. Those skilled in the art are familiar with using 2D images of anatomical structures to construct 3D surface representations. Therefore, a detailed explanation of this process is omitted for simplicity. For example, the input anatomical data may include one or more of an X-ray image, a computed tomography (CT) scan, a magnetic resonance image (MRI), or any other imaging data from which a 3D surface representation of the tissue in question can be generated. The output from this anatomical data input is a 3D virtual surface representation of the fractured clavicle components.

[0429] The 3D virtual surface representation of the broken clavicle components is then evaluated to identify the location and shape of the fracture, or in the case where the bone components are completely broken and separated, the location and shape of the bone components relative to each other.

[0430] In the case where the skeletal components are completely broken and separated, the method and related software perform a skeletal reduction process, which can allow the 3D virtual surface representation of the broken clavicle to be manually repositioned to build a patchwork clavicle. In this case, the user repositions and redirects the 3D virtual surface representation of the broken clavicle to produce a 3D patchwork clavicle model similar to the clavicle assembled from the components comprising the 3D virtual surface representation. Alternatively, the method and related software can optionally use the 3D template model of the clavicle to provide automatic repositioning and reconstruction of the 3D virtual surface representation of the broken clavicle to build a patchwork clavicle model. More specifically, the software first detects one or more fracture sites from the 3D virtual surface representation of each broken bone component (i.e., the edge of the fracture) comprising the 3D virtual surface representation, and extracts contours from each fracture site. The software then compares the extracted contours with the contours of the 3D template clavicle model, so as to match these contours in pairs for each fracture site and locate the matching skeletal components / pieces. The matching components / pieces are then grouped together. After the matched components / pieces are grouped, the software matches the grouped pieces to the 3D template clavicle model to identify the correct positions of all skeletal components / pieces relative to the 3D template clavicle model. The matched components / pieces are then restored to a 3D patchwork clavicle model similar to the 3D template clavicle model, which is used by the software to construct a 3D reconstructed clavicle model as discussed later.

[0431] Reference again Figure 7 and 127 , after restoration, the 3D patchwork clavicle is used to identify the anatomical model (e.g., a complete skeletal model) in the statistical atlas that most closely resembles the 3D patchwork clavicle model of the patient in question. This step is Figure 3 is depicted as finding the closest bone in the atlas. In order to first identify the skeletal model in the statistical atlas that is most closely similar to the 3D patchwork clavicle model, the 3D patchwork clavicle model is compared with the skeletal model in the statistical atlas using one or more similarity metrics. The result of the initial similarity metric is a skeletal model selected from the statistical atlas as an "initial guess" for the subsequent registration step. The registration step uses the selected atlas skeletal model (i.e., the initial guess skeletal model) to register the 3D patchwork clavicle model so that the output is a patient-specific reconstructed skeletal model aligned with the atlas skeletal model. After the registration step, the shape parameters of the aligned "initial guess" are optimized to make the shape match the 3D patchwork clavicle model.

[0432] The shape parameters, in this case from a statistical atlas, are optimized so that the error between the reconstructed patient-specific bone model and the 3D patchwork clavicle model is minimized using non-fracture regions. Changing the shape parameter values ​​allows for the representation of different anatomical shapes. This process is repeated until convergence of the reconstructed shape is achieved (possibly measured as the relative surface change between iterations or the maximum number of iterations allowed).

[0433] A relaxation step is performed to deform the optimized skeleton to best match the 3D patchwork clavicle model. Consistent with the exemplary case, the lost anatomical structure from the 3D patchwork clavicle model output from the convergence step is applied to the deformed 3D clavicle model, thereby generating a patient-specific 3D model of the patient's reconstructed clavicle. More specifically, the surface points on the 3D patchwork clavicle model are directly relaxed (i.e., deformed) onto the patient-specific 3D clavicle model so that the reconstructed shape best matches the patient-specific shape. The output of this step is a fully reconstructed patient-specific 3D clavicle model that represents what the normal / complete anatomical structure of the patient's clavicle should be.

[0434] After the full anatomical structure is rebuilt, the system software starts the planned reduction order process. In this planned reduction order process, the software allows to manually or automatically determine which clavicle components (i.e., the broken clavicle pieces) will be reassembled and installed with each other and in what order. In doing so, the software records in the memory the 3D model of the clavicle gradually assembled from the skeletal components. Therefore, assuming that the clavicle is broken into 6 components, the software will record the first 3D model of the assembly of the first and second broken bone components to be assembled, followed by the second 3D model of the assembly of the first, second and third broken bone components to be assembled, and so on, until the assembly position and orientation reflecting all 6 broken bone components are obtained, thereby being similar to the final 3D model of the 3D patchwork clavicle model.

[0435] Using the reduction order determination, the software allows for manual or automatic selection of one of a plurality of clavicle trauma plate templates using the 3D patchwork clavicle. More specifically, the clavicle trauma plate template comprises a series of 3D virtual surface representations of clavicle trauma plates that have been generically modeled to match size and shape parameters associated with a given population obtained from a statistical skeletal atlas. In other words, the statistical skeletal atlas includes surface models of a plurality of normal full anatomical clavicles that have been classified on the basis of one or more of size, race, age, gender, and any other markers indicative of bone shape. An exemplary discussion of the procedure for obtaining a template bone plate has been previously described with respect to Figure 112-125In automatic selection mode, the software compares the dimensions and contours of multiple clavicle trauma plate templates to the 3D patchwork clavicle to discern which templates most closely conform to the 3D patchwork clavicle (ie, contour and shape similarity relative to the bony anatomy).

[0436] Using the clavicle trauma plate template that most closely conforms to the 3D patchwork clavicle, the software allows for manual or automatic identification of the location of the fixation site through the trauma plate, as well as determining the direction and length of the fixation device (e.g., surgical bolt) to be used. In the automatic fixation site identification mode, the software takes into account the muscle and attachment location and the nerve location to avoid placing any fixation holes in the nerve pathway or muscle attachment site. In addition, the software allows for manual or automatic selection of fixation fasteners for use with the trauma plate. In this way, the software can automatically select fasteners taking into account the size and shape of the clavicle fracture assembly, the location and orientation of the fastener holes extending through the trauma plate, and the geometry of the fasteners (e.g., bolts), so as to improve the fixation strength and try to avoid unnecessary damage to the integrity of the clavicle.

[0437] After selecting the clavicle trauma plate template, the location of the fixation holes, and the fixation fasteners, the software performs virtual plate placement. This includes placing the clavicle trauma plate template on the 3D patchwork clavicle and manually or automatically deforming the clavicle trauma plate template to match the external surface contour of the 3D patchwork clavicle, thereby generating a virtual 3D patient-specific clavicle trauma plate with certain size, length, and contour dimensions. The software records the patient-specific clavicle trauma plate dimensions and converts these virtual dimensions into machine code, which allows the generation of a physical patient-specific clavicle trauma plate that can be quickly manufactured.

[0438] Using the patient-specific clavicle trauma plate dimensions, the software also receives anatomical data regarding the placement and location of the patient's soft tissues, blood vessels, and nerves within the region of the fractured clavicle to construct a cutting plan. The cutting plan is pre-operative and suggests a surgical approach to making one or more incisions that increases accessibility to the fractured clavicle components and at the same time reduces the invasiveness of the surgical procedure, thereby potentially reducing recovery time and associated post-operative trauma. Fig.134 A 3D patchwork clavicle with surface coloring is shown, indicating the location of muscle attachment to the patient's clavicle. Thus, the drawn circles extending longitudinally along the 3D patchwork clavicle correspond to the locations of the fixation fasteners, which are oriented to locations that are substantially free of muscle attachments.

[0439] After constructing the cutting plan, the surgeon checks the plan to make any modifications before approving the plan. After approving the cutting plan, the plan can be output to the surgical knowledge system during surgery. Similarly, the cutting plan can be used to construct an actual clavicle model before surgery to estimate the shape of the reconstructed clavicle components that are installed to each other to simulate the patient's normal clavicle. This actual clavicle model can then be used to test the fit of the clavicle trauma guard plate, and any contour modifications can be made by the surgeon through bending that may be desirable before surgery. Alternatively, the actual clavicle model can include a clavicle component in a loose form so that one or more trauma guards must be installed to it in order to keep the clavicle component together, allowing the surgeon to test the fit of the trauma guard plate ex vivo and also make any modifications to the trauma guard plate ex vivo.

[0440] refer to Fig.128 and 129 , an exemplary patient-specific clavicle trauma plate can be placed intraoperatively using fluoroscopy. Although the exemplary techniques are described with respect to attaching a patient-specific clavicle trauma plate to a patient's clavicle or clavicle component, it should be understood that the exemplary process is equally applicable to attaching a non-patient-specific trauma plate to a clavicle, and more generally to attaching any trauma plate to any bone or broken bone component.

[0441] Fig.128 A process flow diagram is depicted depicting the various different steps involved as part of a trauma plate placement system for intraoperative placement of patient-specific trauma plates using fluoroscopy, which includes utilizing pre-planned data and placing fiducial markers to establish patient position registration. More specifically, the pre-planned data is loaded into a software package for the trauma plate placement system and may include the patient's bone geometry and tissue geometry, the location of each trauma plate, the type and location of fixation devices used to secure the trauma plate to the bone or skeletal component in question, and any other relevant information related to the surgical site and technique. Fiducial markers for fluoroscopy include, but are not limited to, optical, electromagnetic, IMU markers (although in Fig.128Referenced in the process flow are optical markers that are placed at known locations relative to anatomical landmarks on the patient). Using the fiducial markers and the patient's known anatomical locations and dimensions, the trauma plate placement system aligns the patient relative to the pre-operative coordinate system. The fiducial markers are then tracked in space to provide the surgeon with feedback from the trauma plate placement system indicating the location of one or more incisions relative to a fixed patient reference frame consistent with the pre-operative plan. Exemplary feedback systems that may be used as part of the trauma plate placement system include, but are not limited to, a visual display projected onto the patient surface outlining the location and length of each incision.

[0442] In the case of a broken clavicle in which the clavicle comprises separate bone components, the trauma plate placement system is also capable of visually displaying identification marks on multiple clavicle components to indicate the assembly order of the bone components. In an exemplary form, the visual display includes visible colored numbers displayed on each bone component. The colored numbers change color as the bone components are oriented and positioned relative to each other. In an exemplary form, the first bone component is identified by a display number "1" projected onto the outer surface. Depending on the orientation and position of the bone, the display number "1" can be red, yellow or green. Red numbers indicate that the orientation and position are incorrect. After moving, if the surgeon is moving the bone component in the correct direction to achieve placement consistent with the preoperative plan, the mark turns yellow. After continuing to move, when the correct position is reached, the number turns green. Repeat this repositioning process for each clavicle component.

[0443] In order to provide the surgeon with this intuitive feedback on the position and orientation of the broken bone components, the trauma plate placement system uses fluoroscopy to track the bone components in 3D space to distinguish whether the position and orientation of the bones are consistent with the preoperative plan. Before the bone component tracking, the bone components are aligned using preoperative data so that the surgeon is provided with real-time updated information about the correct position and orientation of the bone components through the display of the projection. When each bone fragment is tracked and finally installed on the clavicle trauma plate, the system verifies the progress of the trauma plate placement using fluoroscopy images to confirm the orientation and position of the plate and the orientation and position of the fixing device (e.g., bolt) and the bone component. Finally, when the bone components are coupled to each other by one or more clavicle trauma plates, the system displays the final mark, which indicates to the surgeon that the operation meets the purpose of the preoperative plan and can be ended.

[0444] Fig.130A process flow diagram depicting the various steps involved as part of a wound plate placement system for intraoperative placement of patient-specific wound plates using ultrasound instead of fluoroscopy. Fig.128 Explanation and Fig.130 The explanation of the present invention is the same as that of the present invention, which is incorporated herein by reference, except that the system uses ultrasound instead of fluoroscopy to track bone components, trauma plates and fixation devices. Therefore, redundant explanations are omitted for brevity.

[0445] Development of a trauma shield placement guide

[0446] refer to Fig.150 , an exemplary process and system for producing a patient-specific trauma plate placement guide are described. Those skilled in the art recognize that bones can break at one or more locations, producing bone fragments that are separated from each other. As part of a reconstructive surgery to repair the bone, one or more trauma plates are used to clamp these fragments in a fixed orientation. The reconstructive surgeon attempts to use innate knowledge rather than patient-specific anatomical facts to put the bone back together. Therefore, to the extent that the patient's skeletal anatomy differs from normal structure, the bone fragments are extremely distorted, or the number of bone fragments is large, the surgeon will resort to using prior art trauma plates and matching the bone fragments to the shape of the plate rather than the other way around. The process and system improve upon prior art trauma plates by producing a trauma plate placement guide and customized trauma plates to match the trauma plate to the bone to replicate the original bone shape and orientation.

[0447] The exemplary system begins by receiving input data representing a fractured anatomical structure. For purposes of explanation only, the fractured anatomical structure comprises a human skull. It should be noted that the above process and system are equally applicable to other anatomical structures / bones, including but not limited to bones in arms, legs, and torsos. In an exemplary form, the anatomical structure data input may take the form of an X-ray image, a CT scan, an MRI, or any other imaging data that may represent the size and shape of a bone.

[0448] The input anatomical data is used to construct a three-dimensional virtual model of the fractured anatomical structure. For example, the input anatomical data includes a computed tomography scan of a fractured skull, which is processed by software to segment the scan and generate a three-dimensional model. Those skilled in the art are familiar with how to use computed tomography scans to construct a three-dimensional virtual model. Therefore, for the sake of further brevity, a detailed description of this aspect of the process is omitted.

[0449] After generating a three-dimensional virtual model of a fractured skull, the software compares the three-dimensional virtual model of the skull with data from a statistical atlas to determine the area of ​​the skull fracture in the three-dimensional virtual model. Specifically, the software uses features extracted from the surface model of the input anatomical structure (e.g., surface roughness, curvature, shape index, curvature, neighborhood connectivity) to extract the area of ​​the fracture site. The approximate contours of those fracture sites are then extracted and matched together to find matching fracture sites. The fracture fragments are also matched to the atlas to indicate the best position to place the matching fracture site in order to reconstruct the normal anatomical structure.

[0450] After the software produces the reconstructed three-dimensional virtual model of the skull of fracture, the support can be manually and / or automatically placed on the outside of the reconstructed three-dimensional virtual skull model. The automatic placement of the support is the result of program-controlled logic, to maximize the stability of bone fragments while minimizing the number of supports. When used in this article, the term support and its plural form refer to any support for making bone fragments stable relative to each other. In some cases, when utilizing the manual support placement feature, the practical experience of the surgeon or other professional users can supplement or replace the logic. In any case, a series of supports are programmed in the software, which allows the software or software users to select different supports for different applications. Meanwhile, the length of the support can be manually or automatically manipulated on the basis of the dimensions of fracture and bone fragments.

[0451] After the supports are assigned and placed on the reconstructed three-dimensional virtual skull model, the dimensions and contours of each support are recorded by the software. This record includes the information necessary for manufacturing each support, or at least includes information that helps to allow a surgeon or other professional to obtain existing supports and make them conform to the placement guide. In the case of molding existing supports, the software extracts the contours of the reconstructed three-dimensional virtual skull model to generate computer-aided design (CAD) instructions for generating one or more solid models of the three-dimensional skull model of the instructions to be reconstructed. These CAD instructions are sent to a rapid prototyping machine, which generates the solid models of the three-dimensional skull model of the one or more instructions to be reconstructed. By recreating the correct anatomical surface as a solid model, each support can be applied to the solid model at the target position and manually conformed before being implanted and fastened to the patient's skull.

[0452] Based on the position and length of any support, the software also extracts the contour of the reconstructed three-dimensional virtual skull model to generate contour data for one or more patient-specific support placement guides. Specifically, a placement guide can be generated for each support. In this way, the placement guide includes a surface contour that matches the contour of the patient's skull in a single direction. If the position of the support on the virtual model of the reconstructed skull is known, and the contour of the adjacent outer surface of the skull is also known, the software merges the two to generate a virtual patient-specific placement guide. The virtual guide is output to a rapid prototyping machine in the form of CAD instructions for manufacturing.

[0453] In this exemplary embodiment, the patient-specific placement guide manufactured includes an elongated handle that is configured to be grasped by the surgeon. A large C-shaped contour plate extends from the end of the elongated handle. The underside of the contour plate is concave to match the convex topology of the skull where the support should be placed. Although not required, the end (or another part) of the contour plate can be fastened to the support, or the contour plate can simply provide a working window in which the support is aligned with the skull and ultimately fastened to the skull. After the support is attached to the skull, the contour plate can be removed.

[0454] Custom cutting and placement guides, plates

[0455] refer to Fig.151 The reconstruction of deformed, broken or partial anatomical structures is a complex problem faced by health care providers. Abnormal anatomical structures may be the result of birth conditions, tumors, diseases, personal injuries. As part of providing treatment for a variety of patients, health care providers may find that it is advantageous to reconstruct or construct anatomical structures to facilitate the treatment of a variety of conditions, which may include but are not limited to bone fractures / fractures, bone degeneration, bone surgical implant revisions, bone surgical initial implants, and diseases.

[0456] The present disclosure provides systems and methods for bone and tissue reconstruction using bone grafts. To perform such reconstruction, the system and related methods use images of the patient's current anatomical structure to construct two virtual 3D models: (a) a first 3D model representing the current abnormal anatomical structure; and (2) a second 3D model representing the patient's reconstructed anatomical structure. Reference must be made to the detailed explanation of using patient images (X-ray images, CT scans, MRI images, etc.) to obtain virtual models of the patient's abnormal anatomical structure and reconstructed anatomical structure in the previous "Full Anatomy Reconstruction" section. The system and method are built on the basis of the system described in the "Full Anatomy Reconstruction" section to utilize two 3D virtual models in combination with constructing a 3D virtual model of one or more bones (i.e., donor bones) from which a bone graft can be obtained. As will be described in more detail below, the 3D virtual models of the patient's reconstructed and abnormal anatomical structure are analyzed to generate a 3D virtual model of the bone graft required for reconstruction. This 3D virtual graft model is compared with the 3D virtual model of the donor bone to access one or more sites on the donor bone where the bone graft can be cut. After the resection location is determined, a cutting guide and a graft placement guide are designed and manufactured for collecting and securing the grafted bone to the reconstruction site.

[0457] As an exemplary explanation, the system and method will be described in the context of facial reconstruction, where the donor bone comprises a fibula. One skilled in the art will recognize that the system and method are applicable to any reconstructive surgical procedure utilizing one or more bone grafts. Furthermore, while facial reconstruction and the fibula as a bone donor are discussed, one skilled in the art will appreciate that the exemplary system and method can be used with donor bones other than the fibula.

[0458] As a prelude to discussing the exemplary systems and methods for reconstructive surgical planning and surgical procedures using bone grafts, it is assumed that the patient's abnormal anatomy has been imaged and virtual 3D models of the patient's abnormal and reconstructed anatomy have been generated according to the process described in the previous "Full Anatomy Reconstruction" section. Therefore, for the sake of brevity, a detailed discussion of utilizing patient images to generate virtual 3D models of both the patient's abnormal and reconstructed anatomy has been omitted.

[0459] After the virtual 3D models of the patient's abnormal and reconstructed anatomical structures have been generated, the software compares the anatomical structures and highlights the areas of difference. Specifically, the areas shared between the virtual 3D models represent bones that will be retained, while the areas of difference indicate one or more sites for reconstruction. The software extracts those areas that are not shared from the virtual 3D model of the patient's reconstructed anatomical structure and separates these areas as separate 3D virtual models of the target bone graft. The surgeon or other preoperative planner can observe the virtual 3D bone graft model and use his judgment on one or more bones that can best cut the bone graft.

[0460] Regardless of the logic used for the possible bones initially selected as transplant candidates, the bones in question are imaged using conventional means (X-ray, CT, MRI, etc.). Using the process described in the previous "Full Anatomy Reconstruction" section, each imaged bone is segmented and a virtual 3D model of the imaged bone is generated. The 3D donor bone model is compared with the virtual 3D bone graft model to separate the common areas. Specifically, the software compares the surface contours of the 3D donor bone model with the surface contours of the virtual 3D bone graft model to identify common or similar curvature areas. Assuming there are no common or similar areas, the process can be restarted by analyzing another possible donor bone. On the contrary, if there are one or more areas that are common or have similar curvatures in the donor bone, these areas are highlighted on the 3D donor bone model. Specifically, the highlighted area simulates the shape of the virtual 3D bone graft model. If the common area is judged to be suitable for cutting a bone graft, the software virtually cuts the bone graft as a virtual 3D model and applies the bone graft (which has a contour specific / unique to the donor bone) to the virtual 3D model of the patient's abnormal anatomy to verify the potential fit and any areas of the patient's abnormal anatomy that may need to be cut as part of the reconstruction. In the event that the application of the virtual 3D model of the cut bone to the virtual 3D model of the patient's abnormal anatomy produces a less than satisfactory reconstruction, the process can be restarted at the bone selection point, or restarted to cut a different area of ​​the bone. However, assuming that the application of the virtual 3D model of the cut bone to the virtual 3D model of the patient's abnormal anatomy produces a suitable fit, the system moves forward to designing a fixture to facilitate cutting the bone graft and installing the bone graft to the patient's residual bone.

[0461] In this exemplary embodiment, the system generates and outputs the machine code necessary for a rapid prototyping machine, CNC machine, or similar device to manufacture a bone graft cutting guide and a bone graft placement guide. To generate the output necessary to manufacture the bone graft cutting guide and the bone graft placement guide, the system uses a virtual 3D model of the resected bone for the virtual 3D model of the patient's abnormal anatomy.

[0462] Specifically, the virtual 3D model of the bone cut off defines the boundary of the virtual 3D cutting guide. In addition, in this exemplary case, it is intended to cut off a portion of the fibula to provide a bone graft. In order to ensure that the suitable portion of the fibula is cut off, the virtual 3D cutting guide includes a window, and the cutting device (saw, cutting drill, etc.) moves back and forth in the window to produce a bone graft that the profile is suitable for. The virtual 3D cutting guide not only needs to be shaped to produce a suitable bone graft profile, but also needs to be shaped to ensure that the placement of the cutting guide on the patient's donor bone is special. More specifically, the placement of the cutting guide on the donor bone needs to ensure that the bone cut off includes the correct profile shape and also shows the correct profile. In this way, the bottom surface of the virtual 3D cutting guide is designed to be the "negative shape" of the surface of the donor bone where the cutting guide will be installed. Exemplary installation techniques for fixing the cutting guide to the donor bone include but are not limited to screws, dowels and pins. To accommodate one or more of these or other mounting techniques, the virtual 3D cutting guide is also designed to include one or more through holes in addition to the window in which the surgical cutter moves back and forth. After the design of the virtual 3D cutting guide is completed, the system generates and outputs the machine code necessary for a rapid prototyping machine, CNC machine or similar device to manufacture the bone graft cutting guide, and subsequently manufactures the real cutting guide.

[0463] In addition to the cutting guide, the software also designs one or more bone graft placement guides. The bone graft placement guide is patient-specific and conforms to the patient's anatomical structure (both the donor bone and the residual bone to which the donor bone is fixed) to ensure the correct placement of the bone graft relative to the residual bone. In an exemplary form, a bone graft placement guide for mandibular reconstruction surgery is constructed. In order to design the bone graft placement guide, the software utilizes a virtual 3D model of the cut bone and applies it to the virtual 3D model of the patient's abnormal anatomical structure to build a hybrid model. Using this hybrid model, identify the joints where the bone graft is connected to the adjacent residual bone (and there is hope to connect through bone growth). At these joints, depending on various factors such as the surgeon's preference, the system identifies the bone graft plate position and one or more guides for each plate, so that the plate is correctly placed and fixed to the bone graft and residual bone.

[0464] Customized Trauma Plate Templating and Placement Guide

[0465] refer to Fig.152 , an exemplary system and method for wound plate template making is illustrated in the form of a flow chart. The system and method include a computer and associated software that perform calculations to determine the best fit within a set of template wound plates so as to reduce future shape changes that may be necessary to fit the wound plate to match the patient's bone geometry. In an exemplary form, the system includes constructing a 3D model of the patient's broken bones as a uniform skeleton, and then forming a template wound plate for the 3D model to finalize the shape of the wound plate prior to implantation. In this way, the final wound plate shape is patient-specific and allows for a closer fit to the patient's anatomy, eliminating ambiguity in the placement of the wound plate, and reducing surgical time. The system can be easily deployed in an everyday clinical environment or a surgeon's office.

[0466] Reference again Fig.152 The initial input to the system is any of a variety of medical images depicting a broken bone. For example, these medical images may be one or more of X-ray, ultrasound, CT, and MRI. The image of the broken bone is analyzed by the operator to select the broken bone from a plurality of possible programmed bones. Using the bone selection, the software uses the medical image data to form a 3D model of the broken bone assembly (as described above for Fig.127 and its related description, which are incorporated herein by reference). These 3D skeletal models are then restored (i.e., reassembled to form a patchwork skeleton, which orients and positions the 3D skeletal model as if the skeleton were unified and not broken) using the skeletal data from the statistical atlas to form a 3D patchwork skeletal model. Similarly, the skeletal data from the statistical atlas is used in combination with the 3D patchwork skeletal model to deform the 3D patchwork skeletal model on a complete, unbroken skeletal model, producing a complete 3D skeletal model (unbroken) of the patient's skeleton in question, which is referred to as a reconstructed skeletal model.

[0467] The reconstructed bone model is analyzed by software to extract the longitudinal curves (i.e., midline curves) along the main dimension, and the software also extracts the cross-sectional curves obtained perpendicular to the main dimension to extract the trauma shield design parameters. From these design parameters, the software calculates which of the multiple template trauma shields is closest to the design parameters. These design parameters may include the length of the trauma shield, the longitudinal curvature of the trauma shield, the transverse curvature perpendicular to the longitudinal curvature, the transverse length, and the fixing position of the bone fastener to minimize interference with the muscle attachment site and the nerve position, and at the same time ensure the correct installation and retention of the trauma shield to the broken bone.

[0468] The skeleton model of described reconstruction is also used to produce entity 3D skeleton model.In exemplary form, described software is programmed to output virtual reconstruction skeleton model as machine code, thereby allows to make the prototype of 3D skeleton model quickly with additive method or subtractive method.For the purpose of this disclosure, additive method comprises 3D printing, wherein from starting blank canvas, model is produced by adding material to form the discrete layer or thin slice of skeleton model, and described layer or thin slice once forms final skeleton model after being stacked on each other by printing subsequent layer.On the contrary, subtractive method comprises starting from solid material block, and uses machine code (for example CNC code) to machine away material to obtain solid skeleton model.Those skilled in the art will appreciate that any one of multiple methods can be used to make entity skeleton model.Depending on selected method, software is programmed to convert 3D virtual model into machine code, so that rapid prototyping and construction of 3D skeleton model.

[0469] After the 3D bone model is constructed, a template trauma plate can be constructed, machined or selected based on the software's selection of a trauma plate that is most closely shaped to conform to the patient's broken bone. Once completed, the template trauma plate is fitted to the 3D bone model and further refined by manually bending the trauma plate to conform to the 3D bone model. After sufficient compliance is achieved between the trauma plate and the bone model, the trauma plate can be considered patient-specific and ready for implantation in the patient after sterilization.

[0470] Patient-specific hip cage templating and placement guide

[0471] refer to Fig.153, illustrates in flowchart form an exemplary system and method for hip cage template making and placement guide. The system and method include a computer and associated software that performs calculations to determine the best fit within a set of template hip cages in order to reduce future shape changes that may be necessary to fit the fit to match the patient's bone geometry. In an exemplary form, the system includes constructing a 3D model of the patient's hip (as a unified bone if fractured or degenerated), and then forming a template hip cage against the 3D model to finalize the shape of the hip cage prior to implantation. In this way, the final hip cage shape and attachment sites are patient-specific and allow for a closer fit to the patient's anatomy, eliminating ambiguity in the placement of the hip cage, and reducing surgical time. The system can be easily deployed in a daily clinical setting or a surgeon's office.

[0472] Reference again Fig.153 The initial input to the system is any of a variety of medical images depicting the patient's hip (all or part of the pelvis). For example, these medical images can be one or more of X-ray, ultrasound, CT, and MRI. The image of the hip bone is used by the software to construct a 3D virtual skeletal model of the patient's hip (as described above for Figure 1 and 7 and its related description, which are incorporated herein by reference). The 3D bone model is then automatically landmarked by software.

[0473] The software uses input from a statistical atlas (e.g., areas that may contain specific landmarks) and local geometric analysis to calculate anatomical landmarks for the 3D skeletal model by comparison with those hip bone models in the statistical atlas. This calculation is specific to each landmark. For example, the approximate shape of the area is known, and the position of the landmark being searched for relative to the local shape features is known. For example, locating the upper edge of the anterior labrum groove point of the acetabulum is achieved by refining the search based on the approximate position of the upper edge of the anterior labrum groove point in the statistical atlas. This process is repeated for each landmark in question.

[0474] After the anatomical landmarks are automatically calculated for the 3D skeletal model, the skeletal model is analyzed by the software to calculate which of the multiple template hip cages most closely fits the anatomical landmarks. In addition to calculating which of the multiple hip cages most closely fits the anatomical landmarks of the patient's hip, the software also calculates the position at which the hip cage will be installed to the patient's anatomy. Again, reference is made to the relevant discussion incorporated herein by reference. Fig. 20 and 21 , the software effectively determines the position where the hip cage will be mounted to the patient's anatomy and generates a virtual 3D guide that can be used to output machine code sufficient to construct a physical 3D placement guide for the revised hip cage.

[0475] The skeletal model of the patient's hip is also used to produce a solid 3D skeletal model. In an exemplary form, the software is programmed to output the virtual 3D skeletal model as a machine code, thereby allowing the prototype of the solid 3D skeletal model to be quickly manufactured with an additive method or a subtractive method. For the purposes of this disclosure, the additive method includes 3D printing, wherein a model is produced from a starting blank canvas by adding material to form a discrete layer or slice of a skeletal model, and the layer or slice, once stacked on each other by printing subsequent layers, forms a final skeletal model. On the contrary, the subtractive method includes starting from a solid material block, and using a machine code (e.g., CNC code) to machine away the material to obtain a solid skeletal model. It will be appreciated by those skilled in the art that any of a variety of methods can be used to manufacture a solid skeletal model. Depending on the selected method, the software is programmed to convert the 3D virtual model into a machine code for the rapid prototyping and construction of a 3D skeletal model.

[0476] After the 3D bone model is constructed, a hip cage can be constructed, machined or selected based on the software's selection of the hip cage that is most closely shaped to conform to the patient's hip. Once completed, the template hip cage is fitted to the 3D bone model and further refined by manually bending the hip cage to conform to the 3D bone model. After sufficient compliance is achieved between the hip cage and the bone model, the hip cage can be considered patient-specific and is ready for implantation in the patient after sterilization.

[0477] IMU kinematic tracking

[0478] refer to Fig.154 , depicts an exemplary system and method for kinematic tracking of bones and soft tissues using an IMU utilizing a computer and associated software. For example, such kinematic tracking can provide useful information about patient kinematics for preoperative surgical planning. For purposes of exemplary explanation, the present system and method will be described in the context of tracking skeletal motion and obtaining the resulting soft tissue motion from a 3D virtual model that integrates bones and soft tissues. Those skilled in the art will recognize that the present system and method are applicable to any bone, soft tissue, or kinematic tracking attempt. In addition, although the kinematic tracking of bones and soft tissues is discussed in the context of the knee joint or spine, those skilled in the art will appreciate that the exemplary system and method are applicable to joints other than the knee joint and bones other than vertebrae.

[0479] As a preface to discussing exemplary systems and methods for skeletal and soft tissue kinematic tracking, it is assumed that the patient's anatomy (to be tracked) has been imaged (including but not limited to X-ray, CT, MRI, and ultrasound), and a virtual 3D model of the patient's anatomy has been generated in accordance with the methods described in the previous "Full Anatomy Reconstruction" section, which is incorporated herein by reference. Therefore, for increased simplicity, a detailed discussion of generating a virtual 3D model of the patient's anatomy using patient images has been omitted.

[0480] If images of soft tissue (e.g., ligaments, tendons, etc.) can be obtained based on the imaging method, these images are also included and segmented by the software when the bones are segmented to form a virtual 3D model of the patient's anatomical structure. If soft tissue images cannot be obtain...

Claims

1. A method for tracking a surgical instrument in three-dimensional space, comprising: understand the relative sizes of multiple surgical instruments; Knowing a position where a first inertial measurement unit (IMU) is mounted to at least one surgical instrument of the plurality of surgical instruments without requiring registration, the first inertial measurement unit (IMU) comprising an accelerometer, a gyroscope, and a magnetometer; Knowing to which of the plurality of surgical instruments the first inertial measurement unit (IMU) is mounted; In response to the repositioning of the first inertial measurement unit (IMU), calibrating a magnetometer in the first inertial measurement unit (IMU); Tracking the first IMU in the three-dimensional space to determine changes in position and orientation of the first IMU while the first IMU is mounted to at least one of the plurality of surgical instruments, including estimating a current orientation of the first IMU using tilt and heading components that match data from the accelerometer and the magnetometer; as well as Orientation and position information of the at least one surgical instrument mounted with the first inertial measurement unit (IMU) is relayed to a user.

2. The method according to claim 1, wherein: The orientation and position information of the relay is depicted on the visual display.

3. The method according to claim 2, wherein: The visual display depicts a three-dimensional model of the at least one surgical instrument mounted with the first inertial measurement unit (IMU) in addition to the virtual model of the patient's anatomy.

4. The method according to claim 3, wherein: The visual display is updated in real time to depict changes in position of the at least one surgical instrument relative to the virtual model of the patient anatomy.

5. The method according to claim 4, wherein: The patient anatomy includes at least one of a pelvis, a femur, a scapula, a humerus, and a tibia.

6. The method according to claim 1, wherein: Relaying the orientation and position information of the surgical instrument includes providing information related to the at least one surgical instrument on which the first inertial measurement unit (IMU) is mounted to an indicator.

7. The method according to claim 6, wherein: The indicator includes a plurality of lights that indicate at least one of whether the at least one surgical instrument is oriented according to preoperative planning and in what direction the at least one surgical instrument needs to be oriented according to preoperative planning.

8. The method according to claim 1, wherein: The plurality of surgical instruments include at least one of a surgical reamer, an acetabular cup inserter, a surgical drill, a surgical saw, a femoral stem inserter, and an implant placement guide.

9. The method according to claim 1, further comprising: Tracking a second inertial measurement unit IMU mounted to the reference object.

10. The method according to claim 9, wherein: The reference object is a bone including at least one of a pelvis, a femur, a humerus, a scapula, and a tibia.

11. The method according to claim 9, further comprising: The first inertial measurement unit (IMU) and the second inertial measurement unit (IMU) are registered with respect to each other.

12. The method according to claim 1, further comprising: placing a patient-specific registration tool against the patient's anatomy in only one orientation and position that precisely matches the patient's anatomy, wherein the patient-specific registration tool comprises the first inertial measurement unit (IMU) mounted to the patient-specific registration tool in a known position and orientation; collecting data from the first inertial measurement unit (IMU) while the patient-specific registration tool is stationary relative to the patient anatomy; collecting data from a second inertial measurement unit (IMU) rigidly mounted to the patient's anatomy; Retrieving the first inertial measurement unit (IMU) from the patient-specific registration tool; and The first inertial measurement unit (IMU) is mounted to at least one of the plurality of surgical instruments.

13. The method according to claim 1, wherein: The gyroscope is configured to generate a first set of gyroscope data, and the accelerometer includes a plurality of accelerometers and is configured to generate a plurality of sets of acceleration data.

14. The method according to claim 13, wherein: The first set of gyroscope data describes 3 vertical axes.

15. The method according to claim 13, wherein: The plurality of sets of acceleration data describe three vertical axes, wherein each set of the plurality of sets of acceleration data separately describes one of the three vertical axes.

16. The method according to claim 13, wherein: The first inertial measurement unit (IMU) includes a plurality of magnetometers configured to generate a plurality of sets of magnetic field data.

17. The method according to claim 16, wherein: The multiple sets of magnetic field data illustrate three vertical axes, wherein each set of the multiple sets of magnetic field data separately illustrates one of the three vertical axes.

18. The method according to claim 1, wherein: The first inertial measurement unit IMU is configured to generate a first set of gyroscope data for X, Y, and Z axes, a first set of acceleration data for X, Y, and Z axes, and a first set of magnetic field data for X, Y, and Z axes. The first set of gyroscope data, the first set of acceleration data, and the first set of magnetic field data are used to generate position data.

19. The method according to claim 18, further comprising: As part of generating the position data, a first set of N particles having a predetermined von Mises-Fisher density dispersion coefficient about a mid-position are generated for the current orientation using the first set of gyroscope data, the first set of acceleration data, and the first set of magnetic field data, wherein each particle of the first set of N particles represents an orientation about the X, Y, and Z axes in quaternion form.

20. The method according to claim 19, wherein: The first group of N particles includes a group of independent and identically distributed random variables extracted from the same probability density space.

21. The method according to claim 19, wherein: The first set of N particles is a statistically constrained variation of the observed orientations.

22. The method of claim 19, further comprising rotating the first set of N particles in the intermediate position to center on an average using the estimated current orientation.

23. The method of claim 22, further comprising estimating the first set of N particles following in time using the first set of gyroscope data.

24. The method according to claim 23, further comprising: The first set of N particles of the follow-up estimation are averaged by iteratively interpolating two quaternions from the first set of N particles of the follow-up estimation until only one quaternion remains.

25. The method according to claim 23, wherein: The gyroscope, accelerometer and magnetometer are configured to generate a second set of gyroscope data for the X, Y, and Z axes, a second set of acceleration data for the X, Y, and Z axes, and a second set of magnetic field data for the X, Y, and Z axes. The second set of gyroscope data, the second set of acceleration data, and the second set of magnetic field data are used to generate position data.

26. The method according to claim 25, further comprising: As part of generating the position data, a second set of N particles having a predetermined von Mises-Fisher density dispersion coefficient about a mid-position are generated for a second orientation using the second set of gyroscope data, the second set of acceleration data, and the second set of magnetic field data, wherein each particle in the second set of N particles represents an orientation about the X, Y, and Z axes in quaternion form.

27. The method according to claim 26, further comprising: A current orientation of the first inertial measurement unit (IMU) is compared to a previous orientation.

28. The method according to claim 27, wherein: The act of comparing includes comparing the second set of N particles from the second state with the first set of N particles from the first state resulting from following in time to estimate the first set of N particles.

29. The method according to claim 28, wherein: The comparing action includes a weighted accuracy that more heavily weights the second set of N particles that are closest to the first set of N particles resulting from the follow-up estimating action.

30. The method of claim 29, wherein: The comparing action includes eliminating certain low-weight particles from the second set of N particles.

31. The method of claim 27, further comprising: The generating and comparing actions are repeated to compare the currently observed orientation with the orientation calculated based on the orientation prediction.

32. A surgical navigation device, comprising: A software package configured to track a surgical instrument in three-dimensional space by performing the following steps: understand the relative sizes of multiple surgical instruments; Knowing a position where a first inertial measurement unit (IMU) is mounted to at least one surgical instrument of the plurality of surgical instruments without requiring registration, the first inertial measurement unit (IMU) comprising an accelerometer, a gyroscope, and a magnetometer; Knowing to which of the plurality of surgical instruments the first inertial measurement unit (IMU) is mounted; In response to the repositioning of the first inertial measurement unit (IMU), calibrating a magnetometer in the first inertial measurement unit (IMU); Tracking the first IMU in three-dimensional space to determine changes in position and orientation of the first IMU while the first IMU is mounted to at least one of the plurality of surgical instruments, including estimating a current orientation of the first IMU using tilt and heading components that match data from the accelerometer and the magnetometer; as well as Orientation and position information of the at least one surgical instrument mounted with the first inertial measurement unit (IMU) is relayed to a user.

33. The surgical navigation device according to claim 32, further comprising: A visual display depicting the orientation and position information of the relay.

34. The surgical navigation device according to claim 33, wherein: The visual display is configured to depict a three-dimensional model of the at least one surgical instrument mounted with the first inertial measurement unit (IMU) in addition to the virtual model of the patient's anatomy.

35. The surgical navigation device according to claim 34, wherein: The visual display is configured to be updated in real time to depict changes in position of the at least one surgical instrument relative to the virtual model of the patient anatomy.

36. The surgical navigation device according to claim 35, wherein: The patient anatomy includes at least one of a pelvis, a femur, a scapula, a humerus, and a tibia.

37. The surgical navigation device according to claim 32, wherein: Relaying the orientation and position information of the surgical instrument includes providing information related to the at least one surgical instrument on which the first inertial measurement unit (IMU) is mounted to an indicator.

38. The surgical navigation device according to claim 37, wherein: The indicator includes a plurality of lights that indicate at least one of whether the at least one surgical instrument is oriented according to preoperative planning and in what direction the at least one surgical instrument needs to be oriented according to preoperative planning.

39. The surgical navigation device according to claim 32, wherein: The plurality of surgical instruments include at least one of a surgical reamer, an acetabular cup inserter, a surgical drill, a surgical saw, a femoral stem inserter, and an implant placement guide.

40. The surgical navigation device according to claim 32, wherein: The software package is configured to track a second inertial measurement unit (IMU) mounted to a reference object.

41. The surgical navigation device according to claim 40, wherein: The reference object is a bone including at least one of a pelvis, a femur, a humerus, a scapula, and a tibia.

42. The surgical navigation device according to claim 40, wherein: The software package is configured to register the first inertial measurement unit (IMU) and the second inertial measurement unit (IMU) with respect to each other.

43. The surgical navigation device according to claim 32, wherein: The package is configured as: collecting data from the first inertial measurement unit (IMU) while a patient-specific registration tool is stationary relative to the patient's anatomy, wherein the patient-specific registration tool is configured to be placed on the patient's anatomy only in one orientation and position that precisely matches the patient's anatomy, and wherein the patient-specific registration tool comprises the first inertial measurement unit (IMU) mounted to the patient-specific registration tool in a known position and orientation; collecting data from a second inertial measurement unit (IMU) rigidly mounted to the patient's anatomy; and Data is collected from the first inertial measurement unit (IMU) after the first inertial measurement unit (IMU) has been removed from the registration tool and mounted to at least one of the plurality of surgical instruments.

44. The surgical navigation device according to claim 32, wherein: The gyroscope is configured to generate a first set of gyroscope data, and the accelerometer includes a plurality of accelerometers and is configured to generate a plurality of sets of acceleration data.

45. The surgical navigation device according to claim 44, wherein: The first set of gyroscope data describes 3 vertical axes.

46. ​​The surgical navigation device according to claim 44, wherein: The plurality of sets of acceleration data describe three vertical axes, wherein each set of the plurality of sets of acceleration data separately describes one of the three vertical axes.

47. The surgical navigation device according to claim 44, wherein: The first inertial measurement unit (IMU) includes a plurality of magnetometers configured to generate a plurality of sets of magnetic field data.

48. The surgical navigation device according to claim 47, wherein: The multiple sets of magnetic field data illustrate three vertical axes, wherein each set of the multiple sets of magnetic field data separately illustrates one of the three vertical axes.

49. The surgical navigation device according to claim 32, wherein: The first inertial measurement unit IMU is configured to generate a first set of gyroscope data for X, Y, and Z axes, a first set of acceleration data for X, Y, and Z axes, and a first set of magnetic field data for X, Y, and Z axes. The first set of gyroscope data, the first set of acceleration data, and the first set of magnetic field data are used to generate position data.

50. The surgical navigation device according to claim 49, wherein: The software package is configured to generate, as part of generating the position data, a first set of N particles having a predetermined von Mises-Fisher density dispersion coefficient about a mid-position for the current orientation using the first set of gyroscope data, the first set of acceleration data, and the first set of magnetic field data, wherein each particle in the first set of N particles represents an orientation about the X, Y, and Z axes in quaternion form.

51. The surgical navigation device according to claim 50, wherein: The first group of N particles includes a group of independent and identically distributed random variables extracted from the same probability density space.

52. The surgical navigation device according to claim 50, wherein: The first set of N particles is a statistically constrained variation of the observed orientations.

53. The surgical navigation device according to claim 50, wherein: The software package is configured to estimate the current orientation of the first inertial measurement unit (IMU) using the first set of acceleration data and the first set of magnetic field data.

54. The surgical navigation device according to claim 50, wherein: The software package is configured to rotate the first set of N particles in the intermediate position to center on a mean value using the estimated current orientation.

55. The surgical navigation device according to claim 54, wherein: The software package is configured to estimate the first set of N particles following in time using the first set of gyroscope data.

56. The surgical navigation device according to claim 55, wherein: The software package is configured to average the first set of N particles of the follow-up estimation by iteratively interpolating two quaternions from the first set of N particles of the follow-up estimation until only one quaternion remains.

57. The surgical navigation device according to claim 55, wherein: The gyroscope, accelerometer and magnetometer are configured to generate a second set of gyroscope data for the X, Y, and Z axes, a second set of acceleration data for the X, Y, and Z axes, and a second set of magnetic field data for the X, Y, and Z axes. The second set of gyroscope data, the second set of acceleration data, and the second set of magnetic field data are used to generate position data.

58. The surgical navigation device according to claim 57, wherein: The software package is configured to, as part of generating the position data, use the second set of gyroscope data, the second set of acceleration data, and the second set of magnetic field data to generate a second set of N particles having a predetermined von Mises-Fisher density dispersion coefficient about an intermediate position for a future orientation, wherein each particle in the second set of N particles represents an orientation about the X, Y, and Z axes in quaternion form.

59. The surgical navigation device according to claim 58, wherein: The software package is configured to compare a future orientation of the first inertial measurement unit (IMU) with the current orientation.

60. The surgical navigation device according to claim 59, wherein: The software package is configured to compare a future orientation of the first inertial measurement unit (IMU) with the current orientation by comparing the second set of N particles from the second state with the first set of N particles from the first state resulting from following in time to estimate the first set of N particles.

61. The surgical navigation device according to claim 60, wherein: The software package is configured to compare a future orientation of the first inertial measurement unit (IMU) to the current orientation using a weighted accuracy that more heavily weights the second group of N particles that are closest to the first group of N particles resulting from a follow-up estimated action.

62. The surgical navigation device according to claim 61, wherein: The software package is configured to compare a future orientation of the first inertial measurement unit (IMU) with the current orientation using elimination of certain low weight particles from the second set of N particles.

63. The surgical navigation device according to claim 59, wherein: The software package is configured to compare the currently observed orientation with the orientation calculated on the basis of the orientation prediction.

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