Approach for correspondence-free registration in arthroplasty

Abstract

Systems and methods relating to pre-operative bone models are disclosed herein. A method for registering a pre-operative bone model, the method comprises receiving, by a processor, the pre-operative bone model; receiving a statistical shape model of the bone surface; and fitting the statistical shape model to the pre-operative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model, wherein regions are defined on the statistical shape model; presenting the user with a visual representation of the pre-operative bone model, the visual representation including an indication of the regions; receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to methods, systems, and apparatuses related to registration of an image-based model to an intraoperative tracking space to guide implant planning without one-to-one correspondence between reference marks on the model to landmarks on the bone surface. The disclosed techniques may apply to surgical interventions involving a variety of joints. BACKGROUND

[0002] During an arthroplasty procedure performed using image-based navigation and / or robotics, a pre-segmented bone surface model is available from pre-operative imaging and planning. The model can be acquired by segmenting imaging (e.g., CT or MRI) taken before the procedure.

[0003] During the procedure, important information about the bone that assists in implant planning can be derived from the pre-segmented bone surface by rigidly registering the pre-segmented bone surface into an intraoperative tracking space where locational data is collected.

[0004] In prior systems, it was often necessary to input reference markings that have a one-to-one correspondence with each locational datum. The need to determine the corresponding locations of reference markings on the boney anatomy, as well as the variability of collecting singular locational data, limited the precision of the resulting registration.

[0005] However, the pre-segmented bone surface model may not exactly match the actual bone surface (e.g., due to trauma, disease progression, intra-operative removal of boney growths, etc.). Thus, non-rigid surface-to-point-cloud registration, wherein the pre-segmented bone surface is modified to fit intra-operative locational data, may produce a more accurate representation of the bone.

[0006] As such, it may be advantageous to utilize both rigid and non-rigid registration in an arthroplasty procedure. SUMMARY

[0007] In some embodiments, a method for determining parameters of an implant includes receiving, by a processor, a pre-operative bone model; receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model; modifying, by the processor, the registered model based on the locational data to generate a modified model; and determining, by the processor, parameters of an implant based on the registered model and the modified model.

[0008] In some embodiments, the pre-operative bone model is pre-segmented.

[0009] In some embodiments, receiving the pre-operative bone model includes receiving imagery from at least one of a CT scan or an MRI scan.

[0010] In some embodiments, the method includes receiving a statistical shape model of the bone surface and fitting the statistical shape model to the pre-operative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0011] In some embodiments, receiving locational data comprises receiving locational data from a point probe.

[0012] In some embodiments, registering the pre-operative bone model is based on finding the closest points on the pre-operative bone model to a subset of the locational data.

[0013] In some embodiments, the registration is perturbed and repeated with varying subsets of the locational data.

[0014] In some embodiments, registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

[0015] In some embodiments, the method includes determining the distance of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

[0016] In some embodiments, the method includes assessing a quality of the registration using the distance of the locational data to regions on the pre-operative bone model.

[0017] In some embodiments, the parameters of the implant include a type and a size of the implant.

[0018] In some embodiments, the parameters of the implant include a position and an orientation of the implant with respect to the pre-operative bone model.

[0019] In some embodiments, the parameters of the implant are adjusted with respect to the registered model and the modified bone model.

[0020] In some embodiments, the method includes determining, by the processor, a resection based on the parameters of the implant and robotically assisting, by the processor, a resection tool to perform the resection.

[0021] In some embodiments, a system for determining parameters of an implant includes a processor and a non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium including one or more programing instructions. The one or more programming instructions, when executed, can cause the processor to receive a pre-operative bone model; receive locational data on a bone surface; register the pre-operative bone model based on the locational data to generate a registered model; modify the registered model based on the locational data to generate a modified model; and determine parameters of an implant based on the registered model and the modified model.

[0022] In some embodiments, the pre-operative bone model is pre-segmented.

[0023] In some embodiments, the one or more programming instructions that cause the processor to receive the pre-operative bone model further cause the processor to receive imagery from at least one of a CT scan or an MRI scan.

[0024] In some embodiments, the one or more programming instructions further cause the processor to receive a statistical shape model of the bone surface and fit the statistical shape model to the pre-operative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0025] In some embodiments, the system includes a point probe and the one or more programming instructions that cause the processor to receive locational data further cause the processor to receive locational data from the point probe.

[0026] In some embodiments, the parameters of the implant comprise a type and a size of the implant.

[0027] In some embodiments, the parameters of the implant comprise a position and an orientation of the implant.

[0028] In some embodiments, the one or more programming instructions further cause the processor to determine a resection based on the parameters of the implant and robotically assist a resection tool to perform the resection. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and form a part of the specification, illustrate the embodiments of the invention and together with the written description serve to explain the principles, characteristics, and features of the invention. In the drawings:

[0030] FIG. 1 depicts an operating theatre including an illustrative computer-assisted surgical system (CASS) in accordance with an embodiment.

[0031] FIG. 2A depicts illustrative control instructions that a surgical computer provides to other components of a CASS in accordance with an embodiment.

[0032] FIG. 2B depicts illustrative control instructions that components of a CASS provide to a surgical computer in accordance with an embodiment.

[0033] FIG. 2C depicts an illustrative implementation in which a surgical computer is connected to a surgical data server via a network in accordance with an embodiment.

[0034] FIG. 3 depicts a flow diagram for a method of registration in accordance with an embodiment.

[0035] FIGS. 4A-4B illustrate sample points in example critical regions on the femur in a posterior view and an anterior view, respectively, in accordance with an embodiment.

[0036] FIG. 5 illustrates sample points in example critical regions on the tibia in accordance with an embodiment.

[0037] FIGS. 6A-6B illustrate sample points in example critical regions on the femur in a medial view and a lateral view, respectively, in accordance with an embodiment.

[0038] FIGS. 7A-7B illustrate sample points in example critical regions on the tibia in a medial view and a lateral view, respectively, in accordance with an embodiment.

[0039] FIG. 8 illustrates an example modified model overlaid with a pre-segmented model in accordance with an embodiment.

[0040] FIG. 9 illustrates a block diagram of an exemplary data processing system in which embodiments are implemented.

[0041] FIG. 10 illustrates a method for registering a pre-operative bone model in accordance with an embodiment.

[0042] FIG. 11 shows a method 1100 for identifying regions of a pre-operative bone model in accordance with an embodiment..

[0043] FIGS. 12A-12B show methods for registering a pre-operative bone model in accordance with embodiments.

[0044] FIG. 13 illustrates a method of assessing locational data in accordance with an embodiment.

[0045] FIGS. 14A-14B show methods for registering a pre-operative bone model in accordance with embodiments.

[0046] FIG. 15 illustrates a method for registering a pre-operative bone model in accordance with an embodiment.

[0047] FIG. 16 illustrates a method of obtaining a registered bone model and modifying the registered bone model. DETAILED DESCRIPTION

[0048] This disclosure is not limited to the particular systems, devices and methods described, as these may vary. The terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope.

[0049] As used in this document, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. Nothing in this disclosure is to be construed as an admission that the embodiments described in this disclosure are not entitled to antedate such disclosure by virtue of prior invention. As used in this document, the term “comprising” means “including, but not limited to.” Definitions

[0050] For the purposes of this disclosure, the term “implant” is used to refer to a prosthetic device or structure manufactured to replace or enhance a biological structure. For example, in a total hip replacement procedure a prosthetic acetabular cup (implant) is used to replace or enhance a patients worn or damaged acetabulum. While the term “implant” is generally considered to denote a man-made structure (as contrasted with a transplant), for the purposes of this specification an implant can include a biological tissue or material transplanted to replace or enhance a biological structure.

[0051] For the purposes of this disclosure, the term “real-time” is used to refer to calculations or operations performed on-the-fly as events occur or input is received by the operable system. However, the use of the term “real-time” is not intended to preclude operations that cause some latency between input and response, so long as the latency is an unintended consequence induced by the performance characteristics of the machine.

[0052] For the purposes of this disclosure, the terms “distract,” “distracting,” or “distraction” are used to refer to displacement of a first point with respect to a second point. For example, the first point and the second point may correspond to surfaces of a joint. In some embodiments herein, a joint may be distracted, i.e., portions of the joint may be separated and / or moved with respect to one another to place the joint under tension. In some embodiments, a first portion of the joint be a surface of a scapula and a second portion of the joint may be a surface of a humerus such that separation occurs between the bones of the joint. In additional embodiments, a first portion of the joint may be a first portion of a humeral implant component or a humeral trial implant and a second portion of the joint may be a second portion of the humeral implant component or the humeral trial implant that is movable with respect to the first portion (e.g., a humeral component and a spacer). Accordingly, separation may occur between the portions of the humeral implant component or the humeral trial implant (i.e., intra-implant separation). Throughout the disclosure herein, the described embodiments may be collectively referred to as distraction of the joint.

[0053] Although much of this disclosure refers to surgeons or other medical professionals by specific job title or role, nothing in this disclosure is intended to be limited to a specific job title or function. Surgeons or medical professionals can include any doctor, nurse, medical professional, or technician. Any of these terms or j ob titles can be used interchangeably with the user of the systems disclosed herein unless otherwise explicitly demarcated. For example, a reference to a surgeon also could apply, in some embodiments to a technician or nurse.

[0054] The systems, methods, and devices disclosed herein are particularly well adapted for surgical procedures that utilize surgical navigation systems, such as the CORI® surgical navigation system. CORI is a registered trademark of SMITH & NEPHEW, INC. of Memphis, TN. CASS Ecosystem Overview

[0055] FIG. 1 provides an illustration of an example computer-assisted surgical system (CASS) 100, according to some embodiments. As described in further detail in the sections that follow, the CASS uses computers, robotics, and imaging technology to aid surgeons in performing orthopedic surgery procedures such as total knee arthroplasty (TKA), unicondylar knee arthroplasty (UKA) , or total hip arthroplasty (THA). For example, surgical navigation systems can aid surgeons in locating patient anatomical structures, guiding surgical instruments, and implanting medical devices with a high degree of accuracy. Surgical navigation systems such as the CASS 100 often employ various forms of computing technology to perform a wide variety of standard and minimally invasive surgical procedures and techniques. Moreover, these systems allow surgeons to more accurately plan, track and navigate the placement of instruments and implants relative to the body of a patient, as well as utilize pre-operative and intra-operative body imaging in the aforementioned placement of instruments and implants.

[0056] An Effector Platform 105 positions surgical tools relative to a patient during surgery. The exact components of the Effector Platform 105 will vary, depending on the embodiment employed. For example, for a knee surgery, the Effector Platform 105 may include an End Effector 105B that holds surgical tools or instruments during their use. The End Effector 105B may be a handheld device or instrument used by the surgeon (e.g., a CORI® hand piece or a cutting guide or jig) or, alternatively, the End Effector 105B can include a device or instrument held or positioned by a robotic arm 105 A. While one robotic arm 105 A is illustrated in FIG. 1, in some embodiments there may be multiple devices. As examples, there may be one robotic arm 105 A on each side of an operating table T or two devices on one side of the table T. The robotic arm 105 A may be mounted directly to the table T, be located next to the table T on a floor platform (not shown), mounted on a floor-to-ceiling pole, or mounted on a wall or ceiling of an operating room. The floor platform may be fixed or moveable. In one particular embodiment, the robotic arm 105A is mounted on a floor-to-ceiling pole located between the patient's legs or feet. In some embodiments, the End Effector 105B may include a suture holder or a stapler to assist in closing wounds. Further, in the case of two robotic arms 105 A, the surgical computer 150 can drive the robotic arms 105 A to work together to suture the wound at closure. Alternatively, the surgical computer 150 can drive one or more robotic arms 105A to staple the wound at closure.

[0057] The Effector Platform 105 can include a Limb Positioner 105C for positioning the patient's limbs during surgery. One example of a Limb Positioner 105C is the SMITH AND NEPHEW SPIDER2 system. The Limb Positioner 105C may be operated manually by the surgeon or alternatively change limb positions based on instructions received from the Surgical Computer 150 (described below). While one Limb Positioner 105C is illustrated in FIG. 1, in some embodiments there may be multiple devices. As examples, there may be one Limb Positioner 105C on each side of the operating table T or two devices on one side of the table T. The Limb Positioner 105C may be mounted directly to the table T, be located next to the table T on a floor platform (not shown), mounted on a pole, or mounted on a wall or ceiling of an operating room. In some embodiments, the Limb Positioner 105C can be used in non-conventional ways, such as a retractor or specific bone holder. The Limb Positioner 105C may include, as examples, an ankle boot, a soft tissue clamp, a bone clamp, or a soft-tissue retractor spoon, such as a hooked, curved, or angled blade. In some embodiments, the Limb Positioner 105C may include a suture holder to assist in closing wounds.

[0058] The Effector Platform 105 may include tools, such as a screwdriver, light or laser, to indicate an axis or plane, bubble level, pin driver, pin puller, plane checker, pointer, finger, or some combination thereof.

[0059] Resection Equipment 110 (not shown in FIG. 1) performs bone or tissue resection using, for example, mechanical, ultrasonic, or laser techniques. Examples of Resection Equipment 110 include drilling devices, burring devices, oscillatory sawing devices, vibratory impaction devices, reamers, ultrasonic bone cutting devices, radio frequency ablation devices, reciprocating devices (such as a rasp or broach), and laser ablation systems. In some embodiments, the Resection Equipment 110 is held and operated by the surgeon during surgery. In other embodiments, the Effector Platform 105 may be used to hold the Resection Equipment 110 during use.

[0060] The Effector Platform 105 also can include a cutting guide or jig 105D that is used to guide saws or drills used to resect tissue during surgery. Such cutting guides 105D can be formed integrally as part of the Effector Platform 105 or robotic arm 105 A or cutting guides can be separate structures that can be matingly and / or removably attached to the Effector Platform 105 or robotic arm 105A. The Effector Platform 105 or robotic arm 105A can be controlled by the CASS 100 to position a cutting guide or jig 105D adjacent to the patient's anatomy in accordance with a pre-operatively or intraoperatively developed surgical plan such that the cutting guide or jig will produce a precise bone cut in accordance with the surgical plan.

[0061] The Tracking System 115 uses one or more sensors to collect real-time position data that locates the patient's anatomy and surgical instruments. For example, for TKA procedures, the Tracking System may provide a location and orientation of the End Effector 105B during the procedure. In addition to positional data, data from the Tracking System 115 also can be used to infer velocity / acceleration of anatomy / instrumentation, which can be used for tool control. In some embodiments, the Tracking System 115 may use a tracker array attached to the End Effector 105B to determine the location and orientation of the End Effector 105B. The position of the End Effector 105B may be inferred based on the position and orientation of the Tracking System 115 and a known relationship in three-dimensional space between the Tracking System 115 and the End Effector 105B. Various types of tracking systems may be used in various embodiments of the present invention including, without limitation, Infrared (IR) tracking systems, electromagnetic (EM) tracking systems, video or image based tracking systems, and ultrasound registration and tracking systems. Using the data provided by the tracking system 115, the surgical computer 150 can detect objects and prevent collision. For example, the surgical computer 150 can prevent the robotic arm 105A and / or the End Effector 105B from colliding with soft tissue.

[0062] Any suitable tracking system can be used for tracking surgical objects and patient anatomy in the surgical theatre. For example, a combination of IR and visible light cameras can be used in an array. Various illumination sources, such as an IR LED light source, can illuminate the scene allowing three-dimensional imaging to occur. In some embodiments, this can include stereoscopic, tri-scopic, quad-scopic, etc. imaging. In addition to the camera array, which in some embodiments is affixed to a cart, additional cameras can be placed throughout the surgical theatre. For example, handheld tools or headsets worn by operators / surgeons can include imaging capability that communicates images back to a central processor to correlate those images with images captured by the camera array. This can give a more robust image of the environment for modeling using multiple perspectives. Furthermore, some imaging devices may be of suitable resolution or have a suitable perspective on the scene to pick up information stored in quick response (QR) codes or barcodes. This can be helpful in identifying specific objects not manually registered with the system. In some embodiments, the camera may be mounted on the robotic arm 105 A.

[0063] In some embodiments, specific objects can be manually registered by a surgeon with the system preoperatively or intraoperatively. For example, by interacting with a user interface, a surgeon may identify the starting location for a tool or a bone structure. By tracking fiducial marks associated with that tool or bone structure, or by using other conventional image tracking modalities, a processor may track that tool or bone as it moves through the environment in a three-dimensional model.

[0064] In some embodiments, certain markers, such as fiducial marks that identify individuals, important tools, or bones in the theater may include passive or active identifiers that can be picked up by a camera or camera array associated with the tracking system. For example, an IR LED can flash a pattern that conveys a unique identifier to the source of that pattern, providing a dynamic identification mark. Similarly, one- or two-dimensional optical codes (barcode, QR code, etc.) can be affixed to objects in the theater to provide passive identification that can occur based on image analysis. If these codes are placed asymmetrically on an object, they also can be used to determine an orientation of an object by comparing the location of the identifier with the extents of an object in an image. For example, a QR code may be placed in a corner of a tool tray, allowing the orientation and identity of that tray to be tracked. Other tracking modalities are explained throughout. For example, in some embodiments, augmented reality (AR) headsets can be worn by surgeons and other staff to provide additional camera angles and tracking capabilities. In this case, the infrared / time of flight sensor data, which is predominantly used for hand / gesture detection, can build correspondence between the AR headset and the tracking system of the robotic system using sensor fusion techniques. This can be used to calculate a calibration matrix that relates the optical camera coordinate frame to the fixed holographic world frame.

[0065] In addition to optical tracking, certain features of objects can be tracked by registering physical properties of the object and associating them with objects that can be tracked, such as fiducial marks fixed to a tool or bone. For example, a surgeon may perform a manual registration process whereby a tracked tool and a tracked bone can be manipulated relative to one another. By impinging the tip of the tool against the surface of the bone, a threedimensional surface can be mapped for that bone that is associated with a position and orientation relative to the frame of reference of that fiducial mark. By optically tracking the position and orientation (pose) of the fiducial mark associated with that bone, a model of that surface can be tracked with an environment through extrapolation.

[0066] The registration process that registers the CASS 100 to the relevant anatomy of the patient also can involve the use of anatomical landmarks, such as landmarks on a bone or cartilage. For example, the CASS 100 can include a 3D model of the relevant bone or joint and the surgeon can intraoperatively collect data regarding the location of bony landmarks on the patient's actual bone using a probe that is connected to the CASS. Bony landmarks can include, for example, the medial malleolus and lateral malleolus, the ends of the proximal femur and distal tibia, and the center of the hip joint. The CASS 100 can compare and register the location data of bony landmarks collected by the surgeon with the probe with the location data of the same landmarks in the 3D model. Alternatively, the CASS 100 can construct a 3D model of the bone or joint without pre-operative image data by using location data of bony landmarks and the bone surface that are collected by the surgeon using a CASS probe or other means. The registration process also can include determining various axes of a joint. For example, for a TKA the surgeon can use the CASS 100 to determine the anatomical and mechanical axes of the femur and tibia. The surgeon and the CASS 100 can identify the center of the hip joint by moving the patient's leg in a spiral direction (i.e., circumduction) so the CASS can determine where the center of the hip joint is located.

[0067] A Tissue Navigation System 120 (not shown in FIG. 1) provides the surgeon with intraoperative, real-time visualization for the patient's bone, cartilage, muscle, nervous, and / or vascular tissues surrounding the surgical area. Examples of systems that may be employed for tissue navigation include fluorescent imaging systems and ultrasound systems.

[0068] The Display 125 provides graphical user interfaces (GUIs) that display images collected by the Tissue Navigation System 120 as well other information relevant to the surgery. For example, in one embodiment, the Display 125 overlays image information collected from various modalities (e.g., CT, MRI, X-ray, fluorescent, ultrasound, etc.) collected pre-operatively or intra-operatively to give the surgeon various views of the patient's anatomy as well as real-time conditions. The Display 125 may include, for example, one or more computer monitors. As an alternative or supplement to the Display 125, one or more members of the surgical staff may wear an Augmented Reality (AR) Head Mounted Device (HMD). For example, in FIG. 1 the Surgeon Illis wearing an AR HMD 155 that may, for example, overlay pre-operative image data on the patient or provide surgical planning suggestions. In one embodiment, a tracker array-mounted surgical tool could be detected by both the IR camera and an AR headset (HMD) using sensor fusion techniques without the need for any "intermediate" calibration rigs. This near-depth, time-of-flight sensing camera located in the HMD could be used for hand / gesture detection. The headset's sensor API can be used to expose IR and depth image data and carryout image processing using, for example, C++ with OpenCV. This approach allows the relationship between the CASS and the virtual coordinate frame to be determined and the headset sensor data (i.e., IR in combination with depth images) to isolate the CASS tracker arrays. The image processing system on the HMD can locate the surgical tool in a fixed holographic world frame and the CASS IR camera can locate the surgical tool relative to its camera coordinate frame. This relationship can be used to calculate a calibration matrix that relates the CASS IR camera coordinate frame to the fixed holographic world frame. This means that if a calibration matrix has previously been calculated, the surgical tool no longer needs to be visible to the AR headset. However, a recalculation may be necessary if the CASS camera is accidentally moved in the workflow. Various example uses of the AR HMD 155 in surgical procedures are detailed in the sections that follow.

[0069] Surgical Computer 150 provides control instructions to various components of the CASS 100, collects data from those components, and provides general processing for various data needed during surgery. In some embodiments, the Surgical Computer 150 is a general-purpose computer. In other embodiments, the Surgical Computer 150 may be a parallel computing platform that uses multiple central processing units (CPUs) or graphics processing units (GPU) to perform processing. In some embodiments, the Surgical Computer 150 is connected to a remote server over one or more computer networks (e.g., the Internet). The remote server can be used, for example, for storage of data or execution of computationally intensive processing tasks.

[0070] Various techniques generally known in the art can be used for connecting the Surgical Computer 150 to the other components of the CASS 100. Moreover, the computers can connect to the Surgical Computer 150 using a mix of technologies. For example, the End Effector 105B may connect to the Surgical Computer 150 over a wired (i.e., serial) connection. The Tracking System 115, Tissue Navigation System 120, and Display 125 can similarly be connected to the Surgical Computer 150 using wired connections. Alternatively, the Tracking System 115, Tissue Navigation System 120, and Display 125 may connect to the Surgical Computer 150 using wireless technologies such as, without limitation, Wi-Fi, Bluetooth, Near Field Communication (NFC), or ZigBee. Robotic Arm

[0071] In some embodiments, the CASS 100 includes a robotic arm 105A that serves as an interface to stabilize and hold a variety of instruments used during the surgical procedure. For example, in the context of a hip surgery, these instruments may include, without limitation, retractors, a sagittal or reciprocating saw, the reamer handle, the cup impactor, the broach handle, and the stem inserter. The robotic arm 105 A may have multiple degrees of freedom (like a Spider device) and have the ability to be locked in place (e.g., by a press of a button, voice activation, a surgeon removing a hand from the robotic arm, or other method).

[0072] In some embodiments, movement of the robotic arm 105 A may be effectuated by use of a control panel built into the robotic arm system. For example, a display screen may include one or more input sources, such as physical buttons or a user interface having one or more icons, that direct movement of the robotic arm 105 A. The surgeon or other healthcare professional may engage with the one or more input sources to position the robotic arm 105 A when performing a surgical procedure.

[0073] A tool or an end effector 105B attached or integrated into a robotic arm 105 A may include, without limitation, a burring device, a scalpel, a cutting device, a retractor, a joint tensioning device, or the like. In embodiments in which an end effector 105B is used, the end effector may be positioned at the end of the robotic arm 105 A such that any motor control operations are performed within the robotic arm system. In embodiments in which a tool is used, the tool may be secured at a distal end of the robotic arm 105A, but motor control operation may reside within the tool itself.

[0074] The robotic arm 105A may be motorized internally to both stabilize the robotic arm, thereby preventing it from falling and hitting the patient, surgical table, surgical staff, etc., and to allow the surgeon to move the robotic arm without having to fully support its weight. While the surgeon is moving the robotic arm 105 A, the robotic arm may provide some resistance to prevent the robotic arm from moving too fast or having too many degrees of freedom active at once. The position and the lock status of the robotic arm 105 A may be tracked, for example, by a controller or the Surgical Computer 150.

[0075] In some embodiments, the robotic arm 105 A can be moved by hand (e.g., by the surgeon) or with internal motors into its ideal position and orientation for the task being performed. In some embodiments, the robotic arm 105A may be enabled to operate in a "free" mode that allows the surgeon to position the arm into a desired position without being restricted. While in the free mode, the position and orientation of the robotic arm 105 A may still be tracked as described above. In one embodiment, certain degrees of freedom can be selectively released upon input from user (e.g., surgeon) during specified portions of the surgical plan tracked by the Surgical Computer 150. Designs in which a robotic arm 105A is internally powered through hydraulics or motors or provides resistance to external manual motion through similar means can be described as powered robotic arms, while arms that are manually manipulated without power feedback, but which may be manually or automatically locked in place, may be described as passive robotic arms.

[0076] A robotic arm 105 A or end effector 105B can include a trigger or other means to control the power of a saw or drill. Engagement of the trigger or other means by the surgeon can cause the robotic arm 105 A or end effector 105B to transition from a motorized alignment mode to a mode where the saw or drill is engaged and powered on. Additionally, the CASS 100 can include a foot pedal (not shown) that causes the system to perform certain functions when activated. For example, the surgeon can activate the foot pedal to instruct the CASS 100 to place the robotic arm 105 A or end effector 105B in an automatic mode that brings the robotic arm or end effector into the proper position with respect to the patient's anatomy in order to perform the necessary resections. The CASS 100 also can place the robotic arm 105A or end effector 105B in a collaborative mode that allows the surgeon to manually manipulate and position the robotic arm or end effector into a particular location. The collaborative mode can be configured to allow the surgeon to move the robotic arm 105 A or end effector 105B medially or laterally, while restricting movement in other directions. As discussed, the robotic arm 105 A or end effector 105B can include a cutting device (saw, drill, and burr) or a cutting guide or jig 105D that will guide a cutting device. In other embodiments, movement of the robotic arm 105A or robotically controlled end effector 105B can be controlled entirely by the CASS 100 without any, or with only minimal, assistance or input from a surgeon or other medical professional. In still other embodiments, the movement of the robotic arm 105 A or robotically controlled end effector 105B can be controlled remotely by a surgeon or other medical professional using a control mechanism separate from the robotic arm or robotically controlled end effector device, for example using a joystick or interactive monitor or display control device.

[0077] A robotic arm 105 A may be used for holding the retractor. For example, in one embodiment, the robotic arm 105 A may be moved into the desired position by the surgeon. At that point, the robotic arm 105 A may lock into place. In some embodiments, the robotic arm 105 A is provided with data regarding the patient's position, such that if the patient moves, the robotic arm can adjust the retractor position accordingly. In some embodiments, multiple robotic arms may be used, thereby allowing multiple retractors to be held or for more than one activity to be performed simultaneously (e.g., retractor holding & reaming).

[0078] The robotic arm 105A may also be used to help stabilize the surgeon's hand while making a femoral neck cut. In this application, control of the robotic arm 105 A may impose certain restrictions to prevent soft tissue damage from occurring. For example, in one embodiment, the Surgical Computer 150 tracks the position of the robotic arm 105A as it operates. If the tracked location approaches an area where tissue damage is predicted, a command may be sent to the robotic arm 105A causing it to stop. Alternatively, where the robotic arm 105A is automatically controlled by the Surgical Computer 150, the Surgical Computer may ensure that the robotic arm is not provided with any instructions that cause it to enter areas where soft tissue damage is likely to occur. The Surgical Computer 150 may impose certain restrictions on the surgeon to prevent the surgeon from reaming too far into the medial wall of the acetabulum or reaming at an incorrect angle or orientation.

[0079] In some embodiments, the robotic arm 105A may be used to hold a cup impactor at a desired angle or orientation during cup impaction. When the final position has been achieved, the robotic arm 105 A may prevent any further seating to prevent damage to the pelvis.

[0080] The surgeon may use the robotic arm 105A to position the broach handle at the desired position and allow the surgeon to impact the broach into the femoral canal at the desired orientation. In some embodiments, once the Surgical Computer 150 receives feedback that the broach is fully seated, the robotic arm 105 A may restrict the handle to prevent further advancement of the broach.

[0081] The robotic arm 105A may also be used for resurfacing applications. For example, the robotic arm 105A may stabilize the surgeon while using traditional instrumentation and provide certain restrictions or limitations to allow for proper placement of implant components (e.g., guide wire placement, chamfer cutter, sleeve cutter, plan cutter, etc.). Where only a burr is employed, the robotic arm 105A may stabilize the surgeon's handpiece and may impose restrictions on the handpiece to prevent the surgeon from removing unintended bone in contravention of the surgical plan.

[0082] The robotic arm 105 A may be a passive arm. As an example, the robotic arm 105A may be a CIRQ robot arm available from Brainlab AG. CIRQ is a registered trademark of Brainlab AG, Olof-Palme-Str. 9 81829, Munchen, FED REP of GERMANY. In one particular embodiment, the robotic arm 105A is an intelligent holding arm as disclosed in U.S. Patent Application No. 15 / 525,585 to Krinninger et al., U.S. Patent Application No. 15 / 561,042 to Nowatschin et al., U.S. Patent Application No. 15 / 561,048 to Nowatschin et al., and U.S. Patent No. 10,342,636 to Nowatschin et al., the entire contents of each of which is herein incorporated by reference. Surgical Procedure Data Generation and Collection

[0083] The various services that are provided by medical professionals to treat a clinical condition are collectively referred to as an "episode of care." For a particular surgical intervention, the episode of care can include three phases: pre-operative, intra-operative, and post-operative. During each phase, data is collected or generated that can be used to analyze the episode of care in order to understand various features of the procedure and identify patterns that may be used, for example, in training models to make decisions with minimal human intervention. The data collected over the episode of care may be stored at the Surgical Computer 150 or the Surgical Data Server 180 as a complete dataset. Thus, for each episode of care, a dataset exists that comprises all of the data collectively pre-operatively about the patient, all of the data collected or stored by the CASS 100 intra-operatively, and any postoperative data provided by the patient or by a healthcare professional monitoring the patient.

[0084] As explained in further detail, the data collected during the episode of care may be used to enhance performance of the surgical procedure or to provide a holistic understanding of the surgical procedure and the patient outcomes. For example, in some embodiments, the data collected over the episode of care may be used to generate a surgical plan. In one embodiment, a high-level, pre-operative plan is refined intra-operatively as data is collected during surgery. In this way, the surgical plan can be viewed as dynamically changing in real-time or near real-time as new data is collected by the components of the CASS 100. In other embodiments, pre-operative images or other input data may be used to develop a robust plan preoperatively that is simply executed during surgery. In this case, the data collected by the CASS 100 during surgery may be used to make recommendations that ensure that the surgeon stays within the pre-operative surgical plan. For example, if the surgeon is unsure how to achieve a certain prescribed cut or implant alignment, the Surgical Computer 150 can be queried for a recommendation. In still other embodiments, the pre-operative and intra-operative planning approaches can be combined such that a robust pre-operative plan can be dynamically modified, as necessary or desired, during the surgical procedure. In some embodiments, a biomechanics-based model of patient anatomy contributes simulation data to be considered by the CASS 100 in developing preoperative, intraoperative, and post-operative / rehabilitation procedures to optimize implant performance outcomes for the patient.

[0085] Aside from changing the surgical procedure itself, the data gathered during the episode of care may be used as an input to other procedures ancillary to the surgery. For example, in some embodiments, implants can be designed using episode of care data. Example data-driven techniques for designing, sizing, and fitting implants are described in U.S. Patent No. 10,064,686, filed August 15, 2011, and entitled "Systems and Methods for Optimizing Parameters for Orthopaedic Procedures"; U.S. Patent No. 10,102,309, filed July 20, 2012 and entitled "Systems and Methods for Optimizing Fit of an Implant to Anatomy"; and U.S. Patent No. 8,078,440, filed September 19, 2008 and entitled "Operatively Tuning Implants for Increased Performance," the entire contents of each of which are hereby incorporated by reference into this patent application.

[0086] Furthermore, the data can be used for educational, training, or research purposes. For example, using the network-based approach described below in FIG. 2C, other doctors or students can remotely view surgeries in interfaces that allow them to selectively view data as it is collected from the various components of the CASS 100. After the surgical procedure, similar interfaces may be used to "playback" a surgery for training or other educational purposes, or to identify the source of any issues or complications with the procedure.

[0087] Data acquired during the pre-operative phase generally includes all information collected or generated prior to the surgery. Thus, for example, information about the patient may be acquired from a patient intake form or electronic medical record (EMR). Examples of patient information that may be collected include, without limitation, patient demographics, diagnoses, medical histories, progress notes, vital signs, medical history information, allergies, and lab results. The pre-operative data may also include images related to the anatomical area of interest. These images may be captured, for example, using Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray, ultrasound, or any other modality known in the art. The pre-operative data may also comprise quality of life data captured from the patient. For example, in one embodiment, pre-surgery patients use a mobile application ("app") to answer questionnaires regarding their current quality of life. In some embodiments, preoperative data used by the CASS 100 includes demographic, anthropometric, cultural, or other specific traits about a patient that can coincide with activity levels and specific patient activities to customize the surgical plan to the patient. For example, certain cultures or demographics may be more likely to use a toilet that requires squatting on a daily basis.

[0088] FIGS. 2A and 2B provide examples of data that may be acquired during the intra-operative phase of an episode of care. These examples are based on the various components of the CASS 100 described above with reference to FIG. 1; however, it should be understood that other types of data may be used based on the types of equipment used during surgery and their use.

[0089] FIG. 2A shows examples of some of the control instructions that the Surgical Computer 150 provides to other components of the CASS 100, according to some embodiments. Note that the example of FIG. 2A assumes that the components of the Effector Platform 105 are each controlled directly by the Surgical Computer 150. In embodiments where a component is manually controlled by the Surgeon 111, instructions may be provided on the Display 125 or AR HMD 155 instructing the Surgeon 111 how to move the component.

[0090] The various components included in the Effector Platform 105 are controlled by the Surgical Computer 150 providing position commands that instruct the component where to move within a coordinate system. In some embodiments, the Surgical Computer 150 provides the Effector Platform 105 with instructions defining how to react when a component of the Effector Platform 105 deviates from a surgical plan. These commands are referenced in FIG. 2A as "haptic" commands. For example, the End Effector 105B may provide a force to resist movement outside of an area where resection is planned. Other commands that may be used by the Effector Platform 105 include vibration and audio cues.

[0091] In some embodiments, the end effectors 105B of the robotic arm 105 A are operatively coupled with cutting guide 105D. In response to an anatomical model of the surgical scene, the robotic arm 105 A can move the end effectors 105B and the cutting guide 105D into position to match the location of the femoral or tibial cut to be performed in accordance with the surgical plan. This can reduce the likelihood of error, allowing the vision system and a processor utilizing that vision system to implement the surgical plan to place a cutting guide 105D at the precise location and orientation relative to the tibia or femur to align a cutting slot of the cutting guide with the cut to be performed according to the surgical plan. Then, a surgeon can use any suitable tool, such as an oscillating or rotating saw or drill to perform the cut (or drill a hole) with perfect placement and orientation because the tool is mechanically limited by the features of the cutting guide 105D. In some embodiments, the cutting guide 105D may include one or more pin holes that are used by a surgeon to drill and screw or pin the cutting guide into place before performing a resection of the patient tissue using the cutting guide. This can free the robotic arm 105A or ensure that the cutting guide 105D is fully affixed without moving relative to the bone to be resected. For example, this procedure can be used to make the first distal cut of the femur during a total knee arthroplasty. In some embodiments, where the arthroplasty is a hip arthroplasty, cutting guide 105D can be fixed to the femoral head or the acetabulum for the respective hip arthroplasty resection. It should be understood that any arthroplasty that utilizes precise cuts can use the robotic arm 105 A and / or cutting guide 105D in this manner.

[0092] The Resection Equipment 110 is provided with a variety of commands to perform bone or tissue operations. As with the Effector Platform 105, position information may be provided to the Resection Equipment 110 to specify where it should be located when performing resection. Other commands provided to the Resection Equipment 110 may be dependent on the type of resection equipment. For example, for a mechanical or ultrasonic resection tool, the commands may specify the speed and frequency of the tool. For Radiofrequency Ablation (RFA) and other laser ablation tools, the commands may specify intensity and pulse duration.

[0093] Some components of the CASS 100 do not need to be directly controlled by the Surgical Computer 150; rather, the Surgical Computer 150 only needs to activate the component, which then executes software locally specifying the manner in which to collect data and provide it to the Surgical Computer 150. In the example of FIG. 2A, there are two components that are operated in this manner: the Tracking System 115 and the Tissue Navigation System 120.

[0094] The Surgical Computer 150 provides the Display 125 with any visualization that is needed by the Surgeon 111 during surgery. For monitors, the Surgical Computer 150 may provide instructions for displaying images, GUIs, etc. using techniques known in the art. The display 125 can include various portions of the workflow of a surgical plan. During the registration process, for example, the display 125 can show a preoperatively constructed 3D bone model and depict the locations of the probe as the surgeon uses the probe to collect locations of anatomical landmarks on the patient. The display 125 can include information about the surgical target area. For example, in connection with a TKA, the display 125 can depict the mechanical and anatomical axes of the femur and tibia. The display 125 can depict varus and valgus angles for the knee joint based on a surgical plan, and the CASS 100 can depict how such angles will be affected if contemplated revisions to the surgical plan are made. Accordingly, the display 125 is an interactive interface that can dynamically update and display how changes to the surgical plan would impact the procedure and the final position and orientation of implants installed on bone.

[0095] As the workflow progresses to preparation of bone cuts or resections, the display 125 can depict the planned or recommended bone cuts before any cuts are performed. The surgeon 111 can manipulate the image display to provide different anatomical perspectives of the target area and can have the option to alter or revise the planned bone cuts based on intraoperative evaluation of the patient. The display 125 can depict how the chosen implants would be installed on the bone if the planned bone cuts are performed. If the surgeon 111 choses to change the previously planned bone cuts, the display 125 can depict how the revised bone cuts would change the position and orientation of the implant when installed on the bone.

[0096] The display 125 can provide the surgeon 111 with a variety of data and information about the patient, the planned surgical intervention, and the implants. Various patient-specific information can be displayed, including real-time data concerning the patient's health such as heart rate, blood pressure, etc. The display 125 also can include information about the anatomy of the surgical target region including the location of landmarks, the current state of the anatomy (e.g., whether any resections have been made, the depth and angles of planned and executed bone cuts), and future states of the anatomy as the surgical plan progresses. The display 125 also can provide or depict additional information about the surgical target region. For a TKA, the display 125 can provide information about the gaps (e.g., gap balancing) between the femur and tibia and how such gaps will change if the planned surgical plan is carried out. For a TKA, the display 125 can provide additional relevant information about the knee joint such as data about the joint's tension (e.g., ligament laxity) and information concerning rotation and alignment of the joint. The display 125 can depict how the planned implants' locations and positions will affect the patient as the knee joint is flexed. The display 125 can depict how the use of different implants or the use of different sizes of the same implant will affect the surgical plan and preview how such implants will be positioned on the bone. The CASS 100 can provide such information for each of the planned bone resections in a TKA or THA. In a TKA, the CASS 100 can provide robotic control for one or more of the planned bone resections. For example, the CASS 100 can provide robotic control only for the initial distal femur cut, and the surgeon 111 can manually perform other resections (anterior, posterior and chamfer cuts) using conventional means, such as a 4-in-l cutting guide or jig 105D.

[0097] The display 125 can employ different colors to inform the surgeon of the status of the surgical plan. For example, un-resected bone can be displayed in a first color, resected bone can be displayed in a second color, and planned resections can be displayed in a third color. Implants can be superimposed onto the bone in the display 125, and implant colors can change or correspond to different types or sizes of implants.

[0098] The information and options depicted on the display 125 can vary depending on the type of surgical procedure being performed. Further, the surgeon 111 can request or select a particular surgical workflow display that matches or is consistent with his or her surgical plan preferences. For example, for a surgeon 111 who typically performs the tibial cuts before the femoral cuts in a TKA, the display 125 and associated workflow can be adapted to take this preference into account. The surgeon 111 also can preselect that certain steps be included or deleted from the standard surgical workflow display. For example, if a surgeon 111 uses resection measurements to finalize an implant plan but does not analyze ligament gap balancing when finalizing the implant plan, the surgical workflow display can be organized into modules, and the surgeon can select which modules to display and the order in which the modules are provided based on the surgeon's preferences or the circumstances of a particular surgery. Modules directed to ligament and gap balancing, for example, can include pre- and post-resection ligament / gap balancing, and the surgeon 111 can select which modules to include in their default surgical plan workflow depending on whether they perform such ligament and gap balancing before or after (or both) bone resections are performed.

[0099] For more specialized display equipment, such as AR HMDs, the Surgical Computer 150 may provide images, text, etc. using the data format supported by the equipment. For example, if the Display 125 is a holography device such as the Microsoft HoloLens™ or Magic Leap One™, the Surgical Computer 150 may use the HoloLens Application Program Interface (API) to send commands specifying the position and content of holograms displayed in the field of view of the Surgeon 111.

[0100] In some embodiments, one or more surgical planning models may be incorporated into the CASS 100 and used in the development of the surgical plans provided to the surgeon 111. The term "surgical planning model" refers to software that simulates the biomechanics performance of anatomy under various scenarios to determine the optimal way to perform cutting and other surgical activities. For example, for knee replacement surgeries, the surgical planning model can measure parameters for functional activities, such as deep knee bends, gait, etc., and select cut locations on the knee to optimize implant placement. One example of a surgical planning model is the LIFEMOD™ simulation software from SMITH AND NEPHEW, INC. In some embodiments, the Surgical Computer 150 includes computing architecture that allows full execution of the surgical planning model during surgery (e.g., a GPU-based parallel processing environment). In other embodiments, the Surgical Computer 150 may be connected over a network to a remote computer that allows such execution, such as a Surgical Data Server 180 (see FIG. 2C). As an alternative to full execution of the surgical planning model, in some embodiments, a set of transfer functions are derived that simplify the mathematical operations captured by the model into one or more predictor equations. Then, rather than execute the full simulation during surgery, the predictor equations are used. Further details on the use of transfer functions are described in WIPO Publication No. 2020 / 037308, filed August 19, 2019, entitled "Patient Specific Surgical Method and System," the entirety of which is incorporated herein by reference.

[0101] FIG. 2B shows examples of some of the types of data that can be provided to the Surgical Computer 150 from the various components of the CASS 100. In some embodiments, the components may stream data to the Surgical Computer 150 in real-time or near real-time during surgery. In other embodiments, the components may queue data and send it to the Surgical Computer 150 at set intervals (e.g., every second). Data may be communicated using any format known in the art. Thus, in some embodiments, the components all transmit data to the Surgical Computer 150 in a common format. In other embodiments, each component may use a different data format, and the Surgical Computer 150 is configured with one or more software applications that enable translation of the data.

[0102] In general, the Surgical Computer 150 may serve as the central point where CASS data is collected. The exact content of the data will vary depending on the source. For example, each component of the Effector Platform 105 provides a measured position to the Surgical Computer 150. Thus, by comparing the measured position to a position originally specified by the Surgical Computer 150 (see FIG. 2B), the Surgical Computer can identify deviations that take place during surgery.

[0103] The Resection Equipment 110 can send various types of data to the Surgical Computer 150 depending on the type of equipment used. Example data types that may be sent include the measured torque, audio signatures, and measured displacement values. Similarly, the Tracking Technology 115 can provide different types of data depending on the tracking methodology employed. Example tracking data types include position values for tracked items (e.g., anatomy, tools, etc.), ultrasound images, and surface or landmark collection points or axes. The Tissue Navigation System 120 provides the Surgical Computer 150 with anatomic locations, shapes, etc. as the system operates.

[0104] Although the Display 125 generally is used for outputting data for presentation to the user, it may also provide data to the Surgical Computer 150. For example, for embodiments where a monitor is used as part of the Display 125, the Surgeon 111 may interact with a GUI to provide inputs which are sent to the Surgical Computer 150 for further processing. For AR applications, the measured position and displacement of the HMD may be sent to the Surgical Computer 150 so that it can update the presented view as needed.

[0105] During the post-operative phase of the episode of care, various types of data can be collected to quantify the overall improvement or deterioration in the patient's condition as a result of the surgery. The data can take the form of, for example, self-reported information reported by patients via questionnaires. For example, in the context of a knee replacement surgery, functional status can be measured with an Oxford Knee Score questionnaire, and the post-operative quality of life can be measured with a EQ5D-5L questionnaire. Other examples in the context of a hip replacement surgery may include the Oxford Hip Score, Harris Hip Score, and WOMAC (Western Ontario and McMaster Universities Osteoarthritis index). Such questionnaires can be administered, for example, by a healthcare professional directly in a clinical setting or using a mobile app that allows the patient to respond to questions directly. In some embodiments, the patient may be outfitted with one or more wearable devices that collect data relevant to the surgery. For example, following a knee surgery, the patient may be outfitted with a knee brace that includes sensors that monitor knee positioning, flexibility, etc. This information can be collected and transferred to the patient's mobile device for review by the surgeon to evaluate the outcome of the surgery and address any issues. In some embodiments, one or more cameras can capture and record the motion of a patient's body segments during specified activities postoperatively. This motion capture can be compared to a biomechanics model to better understand the functionality of the patient's joints and better predict progress in recovery and identify any possible revisions that may be needed.

[0106] The post-operative stage of the episode of care can continue over the entire life of a patient. For example, in some embodiments, the Surgical Computer 150 or other components comprising the CASS 100 can continue to receive and collect data relevant to a surgical procedure after the procedure has been performed. This data may include, for example, images, answers to questions, "normal" patient data (e.g., blood type, blood pressure, conditions, medications, etc.), biometric data (e.g., gait, etc.), and objective and subjective data about specific issues (e.g., knee or hip joint pain). This data may be explicitly provided to the Surgical Computer 150 or other CASS component by the patient or the patient's physician(s). Alternatively, or additionally, the Surgical Computer 150 or other CASS component can monitor the patient's EMR and retrieve relevant information as it becomes available. This longitudinal view of the patient's recovery allows the Surgical Computer 150 or other CASS component to provide a more objective analysis of the patient's outcome to measure and track success or lack of success for a given procedure. For example, a condition experienced by a patient long after the surgical procedure can be linked back to the surgery through a regression analysis of various data items collected during the episode of care. This analysis can be further enhanced by performing the analysis on groups of patients that had similar procedures and / or have similar anatomies.

[0107] In some embodiments, data is collected at a central location to provide for easier analysis and use. Data can be manually collected from various CASS components in some instances. For example, a portable storage device (e.g., USB stick) can be attached to the Surgical Computer 150 into order to retrieve data collected during surgery. The data can then be transferred, for example, via a desktop computer to the centralized storage. Alternatively, in some embodiments, the Surgical Computer 150 is connected directly to the centralized storage via a Network 175 as shown in FIG. 2C.

[0108] FIG. 2C illustrates a "cloud-based" implementation in which the Surgical Computer 150 is connected to a Surgical Data Server 180 via a Network 175. This Network 175 may be, for example, a private intranet or the Internet. In addition to the data from the Surgical Computer 150, other sources can transfer relevant data to the Surgical Data Server 180. The example of FIG. 2C shows three additional data sources: the Patient 160, Healthcare Professional(s) 165, and an EMR Database 170. Thus, the Patient 160 can send pre-operative and post-operative data to the Surgical Data Server 180, for example, using a mobile app. The Healthcare Professional(s) 165 includes the surgeon and his or her staff as well as any other professionals working with Patient 160 (e.g., a personal physician, a rehabilitation specialist, etc.). It should also be noted that the EMR Database 170 may be used for both pre-operative and post-operative data. For example, assuming that the Patient 160 has given adequate permissions, the Surgical Data Server 180 may collect the EMR of the Patient pre-surgery. Then, the Surgical Data Server 180 may continue to monitor the EMR for any updates postsurgery.

[0109] At the Surgical Data Server 180, an Episode of Care Database 185 is used to store the various data collected over a patient's episode of care. The Episode of Care Database 185 may be implemented using any technique known in the art. For example, in some embodiments, a SQL-based database may be used where all of the various data items are structured in a manner that allows them to be readily incorporated in two SQL's collection of rows and columns. However, in other embodiments a No-SQL database may be employed to allow for unstructured data, while providing the ability to rapidly process and respond to queries. As is understood in the art, the term "No-SQL" is used to define a class of data stores that are non-relational in their design. Various types of No-SQL databases may generally be grouped according to their underlying data model. These groupings may include databases that use column-based data models (e.g., Cassandra), document-based data models (e.g., MongoDB), key-value based data models (e.g., Redis), and / or graph-based data models (e.g., Allego). Any type of No-SQL database may be used to implement the various embodiments described herein and, in some embodiments, the different types of databases may support the Episode of Care Database 185.

[0110] Data can be transferred between the various data sources and the Surgical Data Server 180 using any data format and transfer technique known in the art. It should be noted that the architecture shown in FIG. 2C allows transmission from the data source to the Surgical Data Server 180, as well as retrieval of data from the Surgical Data Server 180 by the data sources. For example, as explained in detail below, in some embodiments, the Surgical Computer 150 may use data from past surgeries, machine learning models, etc. to help guide the surgical procedure. [OlH] In some embodiments, the Surgical Computer 150 or the Surgical Data Server 180 may execute a de-identification process to ensure that data stored in the Episode of Care Database 185 meets Health Insurance Portability and Accountability Act (HIPAA) standards or other requirements mandated by law. HIPAA provides a list of certain identifiers that must be removed from data during de-identification. The aforementioned de-identification process can scan for these identifiers in data that is transferred to the Episode of Care Database 185 for storage. For example, in one embodiment, the Surgical Computer 150 executes the deidentification process just prior to initiating transfer of a particular data item or set of data items to the Surgical Data Server 180. In some embodiments, a unique identifier is assigned to data from a particular episode of care to allow for re-identification of the data if necessary.

[0112] Although FIGS. 2A-C discuss data collection in the context of a single episode of care, it should be understood that the general concept can be extended to data collection from multiple episodes of care. For example, surgical data may be collected over an entire episode of care each time a surgery is performed with the CASS 100 and stored at the Surgical Computer 150 or at the Surgical Data Server 180. As explained in further detail below, a robust database of episode of care data allows the generation of optimized values, measurements, distances, or other parameters and other recommendations related to the surgical procedure. In some embodiments, the various datasets are indexed in the database or other storage medium in a manner that allows for rapid retrieval of relevant information during the surgical procedure. For example, in one embodiment, a patient-centric set of indices may be used so that data pertaining to a particular patient or a set of patients similar to a particular patient can be readily extracted. This concept can be similarly applied to surgeons, implant characteristics, CASS component versions, etc.

[0113] Further details of the management of episode of care data are described in U.S. Patent No. 11,532,402, filed April 13, 2020, and entitled "METHODS AND SYSTEMS FOR PROVIDING AN EPISODE OF CARE," the entirety of which is incorporated herein by reference. Using the Point Probe to Acquire High-Resolution of Key Areas during Hip Surgeries

[0114] Use of the point probe is described in U.S. Patent Application No. 14 / 455,742 entitled “Systems and Methods for Planning and Performing Image Free Implant Revision Surgery,” the entirety of which is incorporated herein by reference. Briefly, an optically tracked point probe may be used to map the actual surface of the target bone that needs a new implant. Mapping is performed after removal of the defective or wom-out implant, as well as after removal of any diseased or otherwise unwanted bone. A plurality of points is collected on the bone surfaces by brushing or scraping the entirety of the remaining bone with the tip of the point probe. This is referred to as tracing or “painting” the bone. The collected points are used to create a three-dimensional model or surface map of the bone surfaces in the computerized planning system. The created 3D model of the remaining bone is then used as the basis for planning the procedure and necessary implant sizes. An alternative technique that uses X-rays to determine a 3D model is described in U.S. Patent Application No. 16 / 387,151, filed April 17, 2019 and entitled “Three-Dimensional Selective Bone Matching” and U.S. Patent Application No. 16 / 789,430, filed February 13, 2020 and entitled “Three-Dimensional Selective Bone Matching,” the entirety of each of which is incorporated herein by reference.

[0115] For hip applications, the point probe painting can be used to acquire high resolution data in key areas such as the acetabular rim and acetabular fossa. This can allow a surgeon to obtain a detailed view before beginning to ream. For example, in one embodiment, the point probe may be used to identify the floor (fossa) of the acetabulum. As is well understood in the art, in hip surgeries, it is important to ensure that the floor of the acetabulum is not compromised during reaming so as to avoid destruction of the medial wall. If the medial wall were inadvertently destroyed, the surgery would require the additional step of bone grafting. With this in mind, the information from the point probe can be used to provide operating guidelines to the acetabular reamer during surgical procedures. For example, the acetabular reamer may be configured to provide haptic feedback to the surgeon when he or she reaches the floor or otherwise deviates from the surgical plan. Alternatively, the CASS 100 may automatically stop the reamer when the floor is reached or when the reamer is within a threshold distance.

[0116] As an additional safeguard, the thickness of the area between the acetabulum and the medial wall could be estimated. For example, once the acetabular rim and acetabular fossa has been painted and registered to the pre-operative 3D model, the thickness can readily be estimated by comparing the location of the surface of the acetabulum to the location of the medial wall. Using this knowledge, the CASS 100 may provide alerts or other responses in the event that any surgical activity is predicted to protrude through the acetabular wall while reaming.

[0117] The point probe may also be used to collect high resolution data of common reference points used in orienting the 3D model to the patient. For example, for pelvic plane landmarks like the ASIS and the pubic symphysis, the surgeon may use the point probe to paint the bone to represent a true pelvic plane. Given a more complete view of these landmarks, the registration software has more information to orient the 3D model.

[0118] The point probe may also be used to collect high-resolution data describing the proximal femoral reference point that could be used to increase the accuracy of implant placement. For example, the relationship between the tip of the Greater Trochanter (GT) and the center of the femoral head is commonly used as reference point to align the femoral component during hip arthroplasty. The alignment is highly dependent on proper location of the GT; thus, in some embodiments, the point probe is used to paint the GT to provide a high-resolution view of the area. Similarly, in some embodiments, it may be useful to have a high-resolution view of the Lesser Trochanter (LT). For example, during hip arthroplasty, the Dorr Classification helps to select a stem that will maximize the ability of achieving a press- fit during surgery to prevent micromotion of femoral components post-surgery and ensure optimal bony ingrowth. As is generated understood in the art, the Dorr Classification measures the ratio between the canal width at the LT and the canal width 10 cm below the LT. The accuracy of the classification is highly dependent on the correct location of the relevant anatomy. Thus, it may be advantageous to paint the LT to provide a high-resolution view of the area.

[0119] In some embodiments, the point probe is used to paint the femoral neck to provide high-resolution data that allows the surgeon to better understand where to make the neck cut. The navigation system can then guide the surgeon as they perform the neck cut. For example, as understood in the art, the femoral neck angle is measured by placing one line down the center of the femoral shaft and a second line down the center of the femoral neck. Thus, a high-resolution view of the femoral neck (and possibly the femoral shaft as well) would provide a more accurate calculation of the femoral neck angle.

[0120] High-resolution femoral head neck data also could be used for a navigated resurfacing procedure where the software / hardware aids the surgeon in preparing the proximal femur and placing the femoral component. As is generally understood in the art, during hip resurfacing, the femoral head and neck are not removed; rather, the head is trimmed and capped with a smooth metal covering. In this case, it would be advantageous for the surgeon to paint the femoral head and cap so that an accurate assessment of their respective geometries can be understood and used to guide trimming and placement of the femoral component. Registration of Pre-operative Data to Patient Anatomy using the Point Probe

[0121] As noted above, in some embodiments, a 3D model is developed during the pre-operative stage based on 2D or 3D images of the anatomical area of interest. In such embodiments, registration between the 3D model and the surgical site is performed prior to the surgical procedure. The registered 3D model may be used to track and measure the patient’s anatomy and surgical tools intraoperatively.

[0122] During the surgical procedure, landmarks are acquired to facilitate registration of this pre-operative 3D model to the patient’s anatomy. For knee procedures, these points could comprise the femoral head center, distal femoral axis point, medial and lateral epicondyles, medial and lateral malleolus, proximal tibial mechanical axis point, and tibial A / P direction. For hip procedures these points could comprise the anterior superior iliac spine (ASIS), the pubic symphysis, points along the acetabular rim and within the hemisphere, the greater trochanter (GT), and the lesser trochanter (LT).

[0123] In a revision surgery, the surgeon may paint certain areas that contain anatomical defects to allow for better visualization and navigation of implant insertion. These defects can be identified based on analysis of the pre-operative images. For example, in one embodiment, each pre-operative image is compared to a library of images showing “healthy” anatomy (i.e., without defects). Any significant deviations between the patient’s images and the healthy images can be flagged as a potential defect. Then, during surgery, the surgeon can be warned of the possible defect via a visual alert on the display 125 of the CASS 100. The surgeon can then paint the area to provide further detail regarding the potential defect to the Surgical Computer 150.

[0124] In some embodiments, the surgeon may use a non-contact method for registration of bony anatomy intra-incision. For example, in one embodiment, laser scanning is employed for registration. A laser stripe is projected over the anatomical area of interest and the height variations of the area are detected as changes in the line. Other non-contact optical methods, such as white light interferometry or ultrasound, may alternatively be used for surface height measurement or to register the anatomy. For example, ultrasound technology may be beneficial where there is soft tissue between the registration point and the bone being registered (e.g., ASIS, pubic symphysis in hip surgeries), thereby providing for a more accurate definition of anatomic planes. Correspondence-Free Rigid Registration in Arhtorplasty

[0125] During an image-based arthroplasty procedure, a pre-segmented bone surface that is obtained as the output of pre-planning may be used. The pre-segmented bone surface may be obtained from scans taken before the procedure. During the procedure, the pre-segmented bone surface may be registered with the bone in the intraoperative space. The image-based registered surface may capture certain information, such as the posterior extent of the bone.

[0126] FIG. 3 depicts a flow diagram for a method 300 of combining statistical shape modeling and registration in accordance with an embodiment. The method may include receiving 302 a pre-operative model of the patient’s anatomy. The model may be generated using a variety of imaging modalities (e.g., CT, MRI, X-ray, fluorescent, ultrasound, etc.). In some embodiments, the imaging modality directly produces one or more three-dimensional images. In other embodiments, the imaging modality produces one or more two-dimensional images that can be projected into a three-dimensional space. In certain embodiments, multiple imaging and sensing modalities may be used in combination to generate the model. The resulting three-dimensional model may be formatted as a mesh (e.g., data capturing a collection of vertices, edges, and faces that form a three-dimensional model) and / or as point cloud data. In some embodiments, the model can be pre-segmented. In other embodiments, the model can be segmented (e.g., via deformable models, atlas models, machine learning, etc.).

[0127] In some embodiments, a statistical shape model parameters may be fit to the pre-segmented bone surface to provide a consistent topology for anatomical references on the pre-segmented bone surface.

[0128] In some embodiments, the statistical shape model parameters are fit using data sampled from the pre-operative bone surface.

[0129] In some embodiments, the statistical shape model is further fit using Radial Basis Function (RBF) interpolation to data sampled from the pre-operative bone surface.

[0130] In some embodiments, an anatomic reference frame is determine on the statistical shape model to initialize registration to an intra-operative anatomic reference frame.

[0131] The method 300 may include collecting 304 locational data on the bone surface. A user may be instructed to collect 304 points on the surface of the bone, particularly in regions that are relevant to such measurement. In certain embodiments, locational data is collected 304 using a point probe as described herein. The pre-segmented model may be coarsely registered into a tracker space by rigidly affixing one or more tracking elements to the bone.

[0132] The method 300 may include registering 306 the pre-operative bone model based on the locational data to generate a registered model in an anatomic frame. In some embodiments, the anatomic frame may be configured such that the origin is at the intercondylar notch / eminence ridge for the femur / tibia, the medial-lateral (ML) axis is defined as the x-axis positively pointing patient-left, the anterior-posterior (AP) axis is defined as the y-axis positively pointing anterior, and the mechanical axis of the corresponding anatomy is defined as the z-axis pointing superior. The coordinate frame may be measured intraoperatively as a part of the CASS’s 100 landmarking procedure prior to free collection, using landmarks such as the intercondylar groove, eminence ridge, medial / lateral malleoli, and hip center, where the hip center landmark is calculated as the center of rotation of the femur tracker.

[0133] Upon collecting a threshold number of points, registration 306 may be triggered between the anatomic frame determined in the intra-operative tracking space and a statistical anatomic frame defined by the statistical shape model. In some embodiments, registration may be performed using an Iterative Closet Points (ICP) method. In general, ICP methods determine a set of point-to-point correspondences and then minimize a least-square error between the corresponding point sets (e.g., Hom’s method) to solve for the registration. In point-to-surface ICP, the closest point on the surface to each locational datum is found to determine the point-to-point correspondence.

[0134] In some embodiments, the registration may include repeating point-to-surface ICP after perturbing the result by a small, pseudo-random rotation and translation. In some embodiments, the registration may include repeating ICP and perturbation until the least-square error does not decrease. In some embodiments, the registration may be performed after filtering the points outside of a threshold distance from the surface. In some embodiments, the registration may be repeated after changing the threshold distance from the surface. In some embodiments, the results of the registrations may be multiplied and used as the starting point for the next registration.

[0135] In some embodiments, the parameters (e.g., the outlier detection threshold and maximum translation / rotation perturbation) may be modified from an initial set of parameters to a final set of parameters when repeating the registration. These parameters may be configured to minimize the rotational and translational error in registering the bone model to point clouds. Collected points may easily be translated to the CASS 100 anatomic frame by inversing the coarse transformation.

[0136] The user may be presented with a visual representation of the pre-segmented surface model in the tracker space. In some embodiments, the visual representation can include an indication of regions defined on the statistical shape model for the specific anatomy and / or procedure. The critical regions may be configured to minimize registration error. In some embodiments, the critical regions may be automatically defined by the system.

[0137] In some embodiments, the distance of the locational data from the registered statistical shape model may be used to determine the quality of the registration. In some embodiments, the percentage of regions defined on the statistical shape model which have locational data within a threshold may be determined. In some embodiments, a minimum coverage of these regions defined on the statistical shape model may be required to continue. In some embodiments, statistics concerning the distance of the locational data from regions where resections may be made may be used to inform the collection of locational data.

[0138] FIG. 10 illustrates a method 1000 for registering a pre-operative bone model, according to some examples. The operations of the method 1000 may be performed by a computing device, or a processor, for example. According to the method 1000, a preoperative bone model is received 1005. A statistical shape model is also received 1010, and regions are defined on the statistical shape model. For example, portions of the statistical shape model may be associated with respective predetermined regions, such as anatomical regions that are associated with, or defined with respect to, anatomical features of the bone surface. For example, anatomical features of a femur may include one or more of the medial and lateral distal femur, the medial and lateral posterior femur, the anterior notch, or the intercondylar notch. Anatomical features of a tibia may include one or more of medial and lateral plateaus, or the anterior cortex, for example. The predetermined regions may also be referred to as critical regions herein. The predetermined regions may be defined, or specified, for the specific anatomy, the specific procedure, or both. The statistical shape model may be a point distribution model.

[0139] The statistical shape model is fitted, or mapped, 1015 to the pre-operative bone model. The fitting may be any operation that associates points or elements of the statistical shape model with points or elements of the pre-operative bone model. The fitting may allow points or elements of the pre-operative bone model to be associated with the regions defined in the statistical shape model. For example, where a particular point of the pre-operative bone model is associated with a particular point of the statistical shape model, and the particular point of the statistical shape model is associated with a particular region, the particular point of the pre-operative bone model may be associated with the particular region.

[0140] The pre-operative bone model is placed 1020 in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0141] The user is presented 1025 with a visual representation of the pre-operative bone model. The visual representation may be on a display device communicatively connected with the processor or computing device, for example. The visual representation includes an indication of the regions. For example, the regions may be indicated using color, shading, or the addition of graphical elements, such as dots, etc.

[0142] Locational data of a bone surface is received 1030. This may be intraoperative collection of data describing the surface of the bone, such as by “painting” the bone surface, as described previously. Receiving the locational data may include receiving locational data from a point probe.

[0143] The pre-operative bone surface is registered 1035 on the locational data to generate a registered model 1040 (also referred to as a registered bone model). The registered model 1040 may associate points or elements of the pre-operative bone surface with the locational data, such that the registered model may be aligned with the bone surface in the same reference frame.

[0144] The registered model may be used to determine parameters of an implant for use in a surgical procedure. The parameters may include one or more of a type of the implant, a size of the implant, a position of the implant with respect to the pre-operative bone model, or an orientation of the implant with respect to the pre-operative bone model. In some examples, the parameters of the implant may be adjusted with respect to the registered model. In some examples, the parameters of the implant may be adjusted with respect to the registered model and modified model, as described below. The parameters of the implant may be used to determine a resection. The processor may robotically assist in the resection. The registered model may be used in robotically assisting in a resection.

[0145] In some examples, registering the pre-operative bone model may generate one or both of a registered pre-operative bone model and a registered statistical shape model by respectively registering the pre-operative bone model and the statistical shape model on the locational data.

[0146] In some examples, operations 1005 to 1035 may be performed intra-operatively. In some examples some of the operations of method 1000 may be performed pre-operatively. For example, operations 1005 to 1020 may be performed pre-operatively, and operations 1025 to 1035 may be performed intra-operatively. In some examples operation 1025 may be performed pre-operatively.

[0147] Registering the pre-operative bone model may be based on finding closest points on the pre-operative bone model to a subset of the locational data. The registering may be perturbed and repeated with varying subsets of the locational data.

[0148] FIG. 11 shows a method 1100 for identifying regions of a pre-operative bone model. The operations of the method 1100 may be performed by a computing device, or a processor, for example. A pre-operative bone model is received at 1105, this may be as described in relation to operation 1005 of FIG. 10. A statistical shape model is also received at 1110. Regions are defined on the statistical shape model, as described in relation to operation 1010 of FIG. 10, for example. The statistical shape model is fitted to the preoperative bone model, as described in relation to operation 1015 of FIG. 10.

[0149] Regions on the pre-operative bone model are identified 1120 based on the fitting 1115 and the regions defined on the statistical shape model. For example, points or elements of the pre-operative bone model may be associated with the regions defined in the statistical shape model. Thus, regions on the pre-operative bone model corresponding with the regions of the statistical shape model may be identified.

[0150] As described in relation to FIG. 10, the statistical shape model may include identified anatomical regions, and fitting the statistical shape model to the pre-operative bone model may identify regions of the pre-operative bone model that correspond with the respective identified anatomical regions.

[0151] The fitted statistical shape model, or the pre-operative bone model with identified regions may be used, for example, as described in relation to FIG. 10. In some examples, the fitted statistical shape model, or the pre-operative bone model with identified regions may be used in surgical planning, or for displaying to a user the regions on the preoperative bone model.

[0152] In some examples, the pre-operative bone model and / or the fitted statistical shape model may be used during collection of locational data on a bone surface. For example, locational data of the bone surface may be received, and the pre-operative bone model may be registered based on the locational data corresponding with the predetermined regions to generate a registered model, as described in relation to FIG. 10.

[0153] FIG. 12A shows a method 1200 for registering a pre-operative bone model. The operations of the method 1200 may be performed by a computing device, or a processor, for example. A pre-operative bone model is received at 1205, for example as described in relation to operation 1005 of FIG. 10. A registration process 1210 is performed, the registration process includes providing 1215 a visual representation of the pre-operative bone model, the visual representation including an indication of predetermined regions of the pre operative bone model. In some examples, regions may be indicated in the visual presentation of the pre-operative bone model, e.g. using color, shading, graphical objects, annotations, etc. added to the visual presentation of the pre-operative bone model. The predetermined regions may be the same or similar to the regions described in relation to FIG. 10. In some examples, the regions may be defined on a statistical shape model that is fitted to the pre-operative bone model. In other examples, the regions may be identified according to a predetermined set of rules (e.g., based on curvature of the pre-operative bone surface, etc.) Each region may be associated with a respective area of the pre-operative bone model and / or fitted statistical shape model.

[0154] At 1230 locational data on a bone surface is received, and at 1235 the preoperative bone model is registered based on the locational data to generate a registered model. Operations 1230 and 1235 may be as described in relation to operations 1030 and 1035 ofFIG. 10.

[0155] The locational data is assessed, at 1240, with respect to the predetermined regions to determine predetermined regions for which locational data collection is incomplete.

[0156] At 1245 an indication of the predetermined regions for which locational data collection is incomplete is provided in the visual representation. For example, color, shading, graphical additions, or annotations may be used to differentiate regions for which locational data collection is incomplete from regions for which locational data collection is complete. For example, the regions may be indicated on the visual representation of the pre-operative bone model using a first color prior to locational data collection, and as locational data is collected, corresponding portions of the indicated regions may be changed to a second color, to indicate that the locational data for the respective portions has been completed.

[0157] In some examples, indicating predetermined regions for which locational data collection is incomplete may include presenting an indication of progress of locational data collection for each region, based on the assessment of the locational data. For example, portions of the visual indication of the region may change color when it is determined that locational data associated with the region has sufficient quality, quantity, or density, etc.

[0158] The operations 1215 to 1245 (providing a visual representation 1215, receiving locational data 1230, registering the pre-operative bone model 1235, assessing the locational data 1240, and indicating regions for which locational data collection is incomplete 1245) may be repeated iteratively, until it is determined at that the locational data collection is complete. This is shown in the method 1201 of FIG. 12B, which is an example of method 1200 of FIG. 12 A. Operations 1205 to 1245 of FIG. 12B are as described in relation to the correspondingly numbered operations of FIG. 12A. At 1250 it is determined whether locational data collection is complete, and if not, the registration process 1210 is continued by returning to operation 1215 and receiving further locational data 1230. When it is determined, at operation 1250, that the predetermined coverage has been achieved, the method may continue on from the registration process.

[0159] In some examples, the registering 1235 may be performed after, or during, the assessing 1240 of the locational data. For example, FIG. 13, described below, shows a method in which the assessing 1240 is carried out partially before and partially after the registering 1235.

[0160] The assessment of the locational data can be carried out on a region by region basis. Accordingly, collection of locational data may be directed to regions where data collection is incomplete. Collection may be determined to be complete if collection is determined to be complete in each of the regions. The result of the assessment for each region may be indicated in the visual representation.

[0161] Assessment of the locational data may include assessing a coverage of the regions by collected locational data, may include assessing a quality of the registration, or may include both.

[0162] FIG. 13 illustrates a method 1300 of assessing locational data according to some examples. Locational data is collected or received at 1305, and the coverage of the regions by the location data is assessed at 1310. At 1315 it is determined, based on the assessment 1310 whether a predetermined coverage of the regions has been achieved. If, at 1315, the predetermined coverage has not been achieved, locational data collection continues at 1305. If, at 1315, the predetermined coverage has been achieved, the pre-operative bone model is registered to the locational data at 1320. The quality of the registration is assessed at 1325. At 1330, if the quality of the assessment does not meet a predetermined quality condition the locational data collection may continue at 1305. If, at 1330, the quality of the assessment meets a predetermined quality condition, the locational data collection may be determined to be complete at 1335.

[0163] In some examples, the assessment of locational data may be carried out on a region by region basis. In some examples, the coverage of each region may be assessed at 1315, and the method may proceed to 1320 when all of the regions are assessed to have sufficient coverage. The user may be prompted to collect further locational data in regions that are assed to have insufficient coverage, or insufficient quality. In some examples, a user may be prompted, or instructed, to carry out collection of locational data in a particular region, and when data collection for that region is determined to be complete, the user may be prompted to carry out collection of locational data in another region. In other examples, the user may be permitted to collect locational data from regions freely (e.g., in any order, and changing back and forth between regions).

[0164] Accordingly, it is possible to ensure that the locational data collection is sufficient in the predetermined regions. As described previously, the predetermined regions may be anatomical regions, for example. The predetermined regions may be selected to provide a reliable registration between the pre-operative bone model and the locational data.

[0165] The coverage may be assessed based on a proportion of points in each respective region of the pre-operative bone model that are associated with a point of data in the locational data. For example, the points of the region may correspond with vertices of the pre-operative bone model that are associated with the region, or vertices of a fitted statistical shape model that are associated with the region. The predetermined coverage for a region may be indicative of a threshold coverage that is to be met or exceeded before locational data collection in that region is considered to be complete. The predetermined coverage may be determined for each region. The predetermined coverage may be selected to provide a reliable registration between the pre-operative bone model and the locational data.

[0166] For example, where a region is associated with a plurality of points, such as vertices of a segmented bone model or a fitted statistical shape model, collection of locational data may be determined to be complete for that region if each point associated with the region has at least one point of locational data within a threshold distance. The threshold distance may be a fixed value. In some examples, the collection of locational data may be determined to be complete for that region if each point of a subset (e.g., a predetermined proportion) of the points of the region has at least one point of locational data within a threshold distance.

[0167] Assessing the quality of the registration in a region may be based on differences between the registered pre-operative bone model and the locational data in the region. For example, a distance of the locational data to regions on the pre-operative bone model may be determined. In some examples, the distance may be based on the registered statistical shape model. For example, the distance of the locational data to regions on the pre operative bone model may be based on the distance of the locational data to regions on the fitted statistical shape model, e.g., using the distance of the locational data to regions on the fitted statistical shape model as representative of the locational data to regions on the preoperative bone model. The quality of the registration may be assessed using the distances of the locational data to the regions on the pre-operative bone model.

[0168] In some examples, each difference between a point of the locational data and the pre-operative bone model is a distance between the point and a surface of the preoperative bone model. The subset of the locational data may correspond with points of the locational data that are determined to correspond with the predetermined region, or may correspond with a subset of the points that are determined to correspond with the predetermined region (e.g., the subset may omit points that are determine to be outliers). A correspondence between points of the locational data and predetermined regions (e.g., to associate points of locational data with particular regions) may be determined automatically, for example, based on a coarse registration of the locational data to the pre-operative bone model. The quality of the registration in a predetermined region may be based on a statistical measure of differences between at least a subset of the locational data and the pre-operative bone model in the predetermined region. The quality of the registration in the predetermined region may a statistical measure of differences between at least a subset of the locational data and the pre-operative bone model in the predetermined region. The statistical measure may include a standard deviation, a variance, a statistical mode, a cumulant, or a moment of the differences. Typically, a relatively better registration between the locational data and the preoperative bone model will correspond with a relatively smaller value of a statistical measure that reflects the variability, or spread, of the distances, such as the standard deviation.

[0169] In some examples, the assessment of the quality of the registration in the predetermined region may be based on a proportion of the differences (distances) between at least a subset of the locational data and the pre-operative bone model in the predetermined region that are within a predetermined threshold difference. This measure may provide an indication of the variability of the distances while using fewer computational resources than basing the indication on a measure such as the standard deviation.

[0170] Generally, any suitable metric can be used to assess the quality of the registration in the predetermined region. The metric may be based on the locational data and the pre-operative bone model in the predetermined region.

[0171] In some examples, locational data that is more than a threshold distance from a surface of the pre-operative bone model (or a region of the pre-operative bone model) may be disregarded in one or more operations of the registration process. This reduces the effect of outliers and erroneously collected locational data on the registration process.

[0172] As described previously, the regions may be associated with the preoperative bone surface by fitting a statistical shape model to the pre-operative bone surface.

[0173] FIG. 14A shows a method 1400 for registering a pre-operative bone model. The operations of the method 1400 may be performed by a computing device, or a processor, for example. A pre-operative bone model is received 1405, for example as described in relation to operation 1005 of FIG. 10. A registration process 1410 is performed, the registration process includes receiving 1430 locational data on a bone surface, and registering 1435 the pre-operative bone model based on the locational data to generate a registered model. Operations 1430 and 1435 may be as described in relation to operations 1030 and 1035 of FIG. 10. A predetermined coverage of predetermined regions on the bone by locational data is required at 1440. For example, the predetermined coverage may be required in order to continue. Accordingly, completion of the registration process may require the predetermined coverage. The receiving 1430 and registering 1435 may be repeated iteratively, until it is determined at 1440 that the predetermined coverage has been achieved. This is shown in the method 1401 of FIG. 14B, which corresponds with an example of method 1400 of FIG. 14 A, where requiring 1440 a predetermined coverage of predetermined regions on the bone by location data includes determining, at 1445, whether the predetermined coverage has been achieved, and if not, continuing the registration process 1410 by returning to operation 1430 and receiving further locational data. When it is determined, at operation 1445, that the predetermined coverage has been achieved, the method may continue on from the registration process.

[0174] Accordingly, it is possible to ensure that locational data collection is sufficient in the predetermined regions. As described previously, the predetermined regions may be anatomical regions, for example. The predetermined regions may be selected to provide a reliable registration between the pre-operative bone model and the locational data. The coverage may be assessed based on a proportion of points in each respective region of the pre-operative bone model that are associated with a point of data in the locational data, for example. The predetermined coverage for a region may be indicative of a threshold coverage that is to be met or exceeded before locational data collection in that region is considered to be complete. The predetermined coverage may be determined for each region. The predetermined coverage may be selected to provide a reliable registration between the pre-operative bone model and the locational data.

[0175] In some examples, a quality of the registration of the pre-operative bone model in a region of the pre-operative bone model may be assessed. In some examples, the method 1400 may include determining, based on the assessing, whether to continue from the registration process. In some examples, the method 1400 may include informing the collection of locational data based on the distance of the locational data to the predetermined regions. In some examples, the method 1400 may include presenting a visual representation of the pre-operative bone model or the registered pre-operative bone model, the visual representation including an indication of the regions.

[0176] An indication of progress of locational data collection may be output, based on the determination of the quality of the registering. For example, the indication may be displayed on a display device. In some examples, the pre-operative bone model may be visually presented, and the region of the pre-operative bone model may be indicated in the visual presentation, e.g. using color, shading or graphical objects added to the visual presentation of the pre-operative bone model, etc. Progress of locational data collection may be indicated in the visual presentation. For example, portions of the visual indication of the region may change color when it is determined that locational data associated with the region has sufficient quality, quantity, or density, etc.

[0177] The collection of locational data may be informed based on the coverage of the predetermined regions. For example, a display may present information to a user to indicate coverage of the regions by the locational data. In some examples, a presentation of the pre-operative bone model may include the regions, and indicate on the display of each region the coverage of the respective region.

[0178] Accordingly, the collection of locational data may be directed, improving efficiency of the collection.

[0179] In some embodiments, for a TKA, critical regions of the femur may be located on some combination of the medial and lateral distal femur, the medial and lateral posterior femur, the anterior notch, and the intercondylar notch. FIGS. 4A-4B illustrate sample points in example critical regions on the femur in a posterior view 400 and an anterior view 410, respectively, in accordance with an embodiment. Critical regions of the tibia may be located on some combination of the medial and lateral plateaus and the anterior cortex. FIG. 5 illustrates sample points in example critical regions on the tibia 500 in accordance with an embodiment. With respect to a TKA, on the femur, the posterior regions may constrain varus-valgus movement, the distal regions may constrain internal-external rotation, and the anterior notch along with the intercondylar notch may constrain flexion. On the tibia, the plateaus and anterior cortex together may constrain the posterior slope, while the plateaus together may constrain varus-valgus movement. The extension of the anterior cortex region onto the medial cortical rim may constrain internal-external rotation.

[0180] In other embodiments, for a UKA, critical regions of the femur may be located on some combination of the operative condyle of the distal femur, the operative condyle of the anterior femur, the operative condyle of the posterior femur, the operative condyle of the cortical rim, and the intercondylar notch. FIGS. 6A-6B illustrate sample points in example critical regions on the femur in a medial view 600 and a lateral view 610, respectively, in accordance with an embodiment. Critical regions of the tibia may be located on some combination of the operative side of the plateau, the operative side of the anterior cortex, and the operative side of the cortical rim. FIGS. 7A-7B illustrate sample points in example critical regions on the tibia in a medial view 700 and a lateral view 710, respectively, in accordance with an embodiment. On the femur, the intercondylar notch as well as the cortical rim may constrain the varus-valgus movement, the distal region as well as the intercondylar notch and cortical rim may constrain internal-external rotation, and the anterior and the posterior regions may constrain flexion. On the tibia, the plateau and anterior cortex together may constrain the posterior slope, the plateau and cortical rim may constrain varus-valgus movement, and the cortical rim combined with the anterior cortex (i.e., extending to near the tubercle) may constrain internal-external rotation.

[0181] A person of ordinary skill in the art will understand that additional or alternative critical regions may be identified with varying effects on the accuracy of the registration.

[0182] As a person of ordinary skill in the art will understand, the pre-segmented surface may not exactly match the actual bone (e.g., based on issues in capturing / processing the imagery or changes after the imagery is taken).

[0183] In some embodiments, a non-rigid registration can be incorporated into a surgical workflow by modifying the pre-segmented bone surface based on collected locational information of the actual bone surface (e.g., using a point probe as described herein). The registered pre-segmented model and the modified model may both provide a reference for implant placement and workpiece generation.

[0184] FIG. 15 illustrates a method 1500 for registering a pre-operative bone model. The pre-operative bone model may be registered to locational data of a bone surface, such as locational data that is collected intra-operatively using a point probe. The operations of the method 1500 may be performed by a computing device, or a processor, for example.

[0185] According to the method of FIG. 15, a pre-operative bone model is received at 1505, and locational data is received as 1510. The pre-operative bone model is registered based on the locational data at 1515 to generate a registered model 1040. The registered model is modified, based on the locational data, at 1520 to generate a modified model 1525.

[0186] Accordingly the registered model may be modified to provide an improved correspondence between the model and the locational data. For example, to allow the preoperative bone model to adapt to changes or inaccuracies in the pre-operative bone model when compared with the bone surface (as represented by the locational data).

[0187] The pre-operative bone model, locational data, and registration 1515 may be as described in any of the examples herein.

[0188] In some examples, the registering to generate the registered model may be a first registration, and the modifying the registered model may be a second registration. The second registration may have more degrees of freedom than the first registration. Modifying the registered model may include a non-rigid registration of the pre-operative bone model, based on the locational data. In contrast, the registering 1515 of the pre-operative bone model may include a rigid registration of the pre-operative bone model based on the locational data.

[0189] As described in relation to FIG. 12A and FIG. 12B, for example, the preoperative model may include predetermined regions, and the method 1500 may include assessing the locational data with respect to the predetermined regions to determine predetermined regions for which locational data collection is incomplete. The method may also include iteratively performing, until locational data collection is determined to be complete, the receiving locational data 1510, the registering 1515 the pre-operative bone model, and assessing the locational data.

[0190] The method 1500 may further include determining parameters of an implant based on one or both of the registered model and the modified model. The parameters of the implant may comprise a type and a size of the implant. The parameters of the implant may comprise a position and an orientation of the implant with respect to the pre-operative bone model. The parameters of the implant may be adjusted with respect to the registered model, the modified bone model, or both. In some examples, the method 1500 may further include determining a resection based on the parameters of the implant; and robotically assisting a resection tool to perform the resection.

[0191] The method 1500 may include a determination that locational data collection is complete, for example, as described in relation to any of FIGS. 12A, 12B, 13, 14A or 14B. The modifying the registered model may be performed after locational data collection is determined to be complete. Allowing modification of the model (e.g., using a non-rigid transform) after collection is complete may reduce or avoid over-fitting of the model, which could reducing the reliability of the assessment of whether collection is complete.

[0192] In some embodiments, registration 306 includes applying a Radial Basis Function (RBF) to the pre-segmented model. RBF is defined by a kernel that determines how the pre-segmented model is mapped to a set of points as a function of the distance d between a point on the atlas-generated mesh and an input point (e.g., points collected in the free collection state).

[0193] The method 300 may include modifying 308 the registered pre-operative bone model based on the locational data to generate a modified model. For example, the operative bone may have changed significantly in the time between the imaging and the time of operation. To generate an accurate bone model for planning, it may be beneficial to fit the bone model to the points that were collected on the intraoperative bone surface to generate the modified model. Example reasons for change in the operative bone may include intraoperative osteophyte removal and cartilage degradation from wear on the knee. Thus, after registration, points within a threshold (e.g., 2.5mm) of the surface and points within the surface may be recorded to deform the pre-segmented bone model. The principal constraint for image-to-point surface interpolation may be that the deformations applied to the surface should be local. When no points are near a vertex, no deformation may be applied to that vertex. The constraint may be achieved by using a kernel with compact support and applying only the non-affine portion of the determined RBF transformation. The kernel may be the Wendland C6 kernel, which provides a differentiable polynomial interpolation within the determined support radius.

[0194] During registration 306 of the pre-operative model, it may be desirable to remove collected 304 points beyond a threshold from the pre-operative model surface because these points may introduce error into the registration process.

[0195] At each iteration of RBF, the affine deformation may be applied to the surface, and the non-affine portion of the per-vertex displacement may be found as the difference between the affinely deformed vertex positions and the fully deformed vertex positions. The non-affine portion may be applied to the original surface. Vertices that are deformed by more than a threshold (e.g., 1 micron) may be recorded. Curvature smoothing may be applied to the vertices that were deformed, thereby acting as a low-pass filter on the deformed regions to smooth ridges that may form when a large local deformation is found. FIG. 8 illustrates an example modified model 802 overlaid with a pre-segmented model 804 in accordance with an embodiment.

[0196] FIG. 16 illustrates a method 1600 of obtaining a registered bone model and modifying the registered bone model. Operations of the method 1600 may be performed by a computing device, or a processor, for example.

[0197] Patient imaging data 1605 is received. This may include any data indicative of a bone surface, such as CT or MRI data. The imaging data is segmented at 1610 to produce a segmented bone model 1615 that describes the bone surface. Segmenting may produce a surface from volumetric data, for example.

[0198] A statistical shape model 1620 is received, and the statistical shape model 1620 is fitted to the segmented bone model 1615 at 1625. The statistical shape model 1620 may identify predetermined regions of the bone surface described by the statistical shape model 1620. Fitting the statistical shape model 1620 to the segmented bone model 1615 may produce a patient specific model 1630. The patient specific model 1630 may include the segmented bone model and a fitted statistical shape model, for example. In other examples, the patient specific model 1630 may include the segmented bone model with portions of the bone surface in the segmented bone model 1615 associated with predetermined regions, based on the fitting of the statistical shape model 1620.

[0199] The patient specific model 1630 may be used to identify predetermined regions 1635 relative to the segmented bone model. The predetermined regions 1635 may be regions to be used in registration of the bone model to locational data.

[0200] The patient specific model 1630 and the predetermined regions may be used to generate a visual representation 1640 of the bone surface, including indications of the predetermined regions.

[0201] Locational data 1650 is received though the locational data collection 1645. The collection of locational data may be informed by the visual representation 1640. The bone model (e.g., the segmented bone model 1615, a bone model of the patient specific model 1630, etc.) may be registered to the locational data 1650. The registration may be a region-based registration. The visual representation 1640 is updated, based on the registration 1655 to the locational data 1650. The visual representation 1640 may be updated to indicate the locational data with respect to the bone model. The indications of the predetermined regions and the indication of the locational data may inform a user to portions of the bone surface that lack sufficient locational data and / or portions of the bone surface having sufficient locational data.

[0202] At 1660 it is determined whether the collection of locational data is complete. If the locational data collection is not complete, the method returns to 1645 and further locational data is collected / received. If, at 1660, it is determined that collection of locational data is complete, a registered bone model 1665 is generated, based on the registration of the bone model to the locational data.

[0203] At 1670, the registered bone model is modified based on the locational data to generate modified model 1675.

[0204] The registration at 1655 may be a rigid registration of the bone model to the locational data. In some examples, the modification at 1670 includes a non-rigid registration of the bone model to the locational data.

[0205] Portions of the method 1600 may correspond with other corresponding methods described herein. For example, features relating to obtaining the segmented bone model 1615, fitting the statistical shape model 1620, and obtaining the registered model 1665 may be the same or similar to corresponding operations in the method 1000 of FIG. 10. Features relating to identifying the predetermined regions in the bone model may be the same or similar to corresponding operations of FIG. 11. Features relating to the providing a visual representation and determining whether locational data collection is complete may be the same or similar to corresponding operations of FIGS. 12A, 12B, 13, 14A, or 14B. Features relating to the modification 1670 of the registered bone model 1665 to generate the modified bone model may be as described in relation FIG. 15. Implant Placement is Arthroplasty

[0206] In some embodiments, a matrix describing the placement of the implant with respect to the pre-operative bone model may be provided. In some embodiments, this matrix may be multiplied with the rigid registration matrix to provide the placement of the implant with respect to the intra-operative tracking space. In some embodiments, the implant can then be visualized with respect to the rigidly and non-rigidly (modified) registered pre-segmented model of the bone to adjust the pre-operative parameters (e.g., implant model or size) and / or placement (position and / or orientation relative to the anatomy) of the implant.

[0207] In certain embodiments, the CASS 100 may use the registered pre-segmented model and / or the modified model as best suited for visualization and / or calculations. For example, the CASS 100 may use the pre-segmented model for visualization and calculations relating to the posterior extent of the bone. In further embodiments, additional visualizations and calculations may be based on the modified bone to provide the highest fidelity possible to the calculation. In a further example, determining 310 the parameters and / or placement of an implant can be based on the registered model and the modified model. For example, if osteophytes were removed, the medial / lateral extent of the bone may be very different than original registered model based on pre-operative planning. Thus, the size of the implant may be more accurately adjusted with respect to the modified model. The resulting registration may provide a technical advantage over traditional systems by incorporating the advantages of image-based and image-free solutions. Data Processing Systems for Implementing Embodiments Herein

[0208] FIG. 9 illustrates a block diagram of an exemplary data processing system 900 in which embodiments are implemented. The data processing system 900 is an example of a computer, such as a server or client, in which computer usable code or instructions implementing the process for illustrative embodiments of the present invention are located. In some embodiments, the data processing system 900 may be a server computing device. For example, the data processing system 900 may be implemented in a server or another similar computing device operably connected to a surgical system 100 as described above. The data processing system 900 may be configured to, for example, transmit and receive information related to a patient and / or a related surgical plan with the surgical system 100.

[0209] In the depicted example, the data processing system 900 may employ a hub architecture including a north bridge and memory controller hub (NB / MCH) 901 and south bridge and input / output (VO) controller hub (SB / ICH) 902. A processing unit 903, a main memory 904, and a graphics processor 905 may be connected to the NB / MCH 901. The graphics processor 905 may be connected to the NB / MCH 901 through, for example, an accelerated graphics port (AGP).

[0210] In the depicted example, a network adapter 906 connects to the SB / ICH 902. An audio adapter 907, a keyboard and mouse adapter 908, a modem 909, a read only memory (ROM) 910, a hard disk drive (HDD) 911, an optical drive (e.g., CD or DVD) 912, a universal serial bus (USB) ports and other communication ports 913, and PCI / PCIe devices 914 may connect to the SB / ICH 902 through a bus system 916. The PCI / PCIe devices 914 may include Ethernet adapters, add-in cards, and / or PC cards for notebook computers. The ROM 910 may be, for example, a flash basic input / output system (BIOS). The HDD 911 and the optical drive 912 may use an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. A super VO (SIO) device 915 may be connected to the SB / ICH 902.

[0211] An operating system may run on the processing unit 903. The operating system may coordinate and provide control of various components within the data processing system 900. As a client, the operating system may be a commercially available operating system. An object-oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provide calls to the operating system from the object-oriented programs or applications executing on the data processing system 900. As a server, the data processing system 900 may be an IBM® eServer™ System® running the Advanced Interactive Executive operating system or the Linux operating system. The data processing system 900 may be a symmetric multiprocessor (SMP) system that includes a plurality of processors in the processing unit 903. Alternatively, a single processor system may be employed.

[0212] Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as the HDD 911, and are loaded into the main memory 904 for execution by the processing unit 903. The processes for embodiments described herein may be performed by the processing unit 903 using computer usable program code, which can be located in a memory such as, for example, main memory 904, ROM 910, or in one or more peripheral devices.

[0213] Abus system 916 may comprise one or more busses. The bus system 916 may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit such as the modem 909 or the network adapter 906 may include one or more devices that can be used to transmit and receive data.

[0214] Those of ordinary skill in the art will appreciate that the hardware depicted in FIG. 9 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives may be used in addition to or in place of the hardware depicted. Moreover, the data processing system 900 can take the form of any of a number of different data processing systems, including but not limited to, client computing devices, server computing devices, tablet computers, laptop computers, telephone or other communication devices, personal digital assistants, and the like. Essentially, data processing system 900 can be any known or later developed data processing system without architectural limitation.

[0215] While various illustrative embodiments incorporating the principles of the present teachings have been disclosed, the present teachings are not limited to the disclosed embodiments. Instead, this application is intended to cover any variations, uses, or adaptations of the present teachings and use its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which these teachings pertain.

[0216] In the above detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the present disclosure are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that various features of the present disclosure, as generally described herein, and illustrated in the Figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein.

[0217] The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various features. Many modifications and variations can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0218] With respect to the use of substantially any plural and / or singular terms herein, those having skill in the art can translate from the plural to the singular and / or from the singular to the plural as is appropriate to the context and / or application. The various singular / plural permutations may be expressly set forth herein for sake of clarity.

[0219] It will be understood by those within the art that, in general, terms used herein are generally intended as “open” terms (for example, the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” et cetera). While various compositions, methods, and devices are described in terms of “comprising” various components or steps (interpreted as meaning “including, but not limited to”), the compositions, methods, and devices also can “consist essentially of’ or “consist of’ the various components and steps, and such terminology should be interpreted as defining essentially closed-member groups.

[0220] In addition, even if a specific number is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (for example, the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, et cetera” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (for example, “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, et cetera). In those instances where a convention analogous to “at least one of A, B, or C, et cetera” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (for example, “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, et cetera). It will be further understood by those within the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description, sample embodiments, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

[0221] In addition, where features of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0222] As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, et cetera. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, et cetera. As will also be understood by one skilled in the art all language such as “up to,” “at least,” and the like include the number recited and refer to ranges that can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth.

[0223] The term “about,” as used herein, refers to variations in a numerical quantity that can occur, for example, through measuring or handling procedures in the real world; through inadvertent error in these procedures; through differences in the manufacture, source, or purity of compositions or reagents; and the like. Typically, the term “about” as used herein means greater or lesser than the value or range of values stated by 1 / 10 of the stated values, e.g., ±10%. The term “about” also refers to variations that would be recognized by one skilled in the art as being equivalent so long as such variations do not encompass known values practiced by the prior art. Each value or range of values preceded by the term “about” is also intended to encompass the embodiment of the stated absolute value or range of values. Whether or not modified by the term “about,” quantitative values recited in the present disclosure include equivalents to the recited values, e.g., variations in the numerical quantity of such values that can occur, but would be recognized to be equivalents by a person skilled in the art.

[0224] Various of the above-disclosed and other features and functions, or alternatives thereof, may be combined into many other different systems or applications. Various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art, each of which is also intended to be encompassed by the disclosed embodiments.

[0225] The following clauses describe various embodiments.

[0226] Clause 1 A. A method for determining parameters of an implant, the method comprising: receiving, by a processor, a pre-operative bone model; receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model; modifying, by the processor, the registered model based on the locational data to generate a modified model; and determining, by the processor, parameters of an implant based on the registered model and the modified model.

[0227] Clause 2 A. The method of clause 1 A, wherein the pre-operative bone model is pre-segmented.

[0228] Clause 3A. The method of clause 1 A, wherein receiving the pre-operative bone model comprises receiving imagery from at least one of a CT scan or an MRI scan.

[0229] Clause 4A. The method of clause 1 A, further comprising: receiving a statistical shape model of the bone surface; and fitting the statistical shape model to the preoperative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0230] Clause 5A. The method of clause 1A, wherein receiving locational data comprises receiving locational data from a point probe.

[0231] Clause 6A. The method of clause 1 A, wherein registering the pre-operative bone model is based on finding closest points on the pre-operative bone model to a subset of the locational data.

[0232] Clause 7A. The method of clause 1 A, wherein the registration is perturbed and repeated with varying subsets of the locational data.

[0233] Clause 8A. The method of clause 4A, wherein registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

[0234] Clause 9A. The method of clause 8A, further comprising determining a distance of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

[0235] Clause 10A. The method of clause 9A, further comprising assessing a quality of the registration using the distance of the locational data to regions on the pre-operative bone model.

[0236] Clause 11 A. The method of clause 1 A, wherein the parameters of the implant comprise a type and a size of the implant.

[0237] Clause 12 A. The method of clause 1 A, wherein the parameters of the implant comprise a position and an orientation of the implant with respect to the pre-operative bone model.

[0238] Clause 13A. The method of clause 1 A, wherein the parameters of the implant are adjusted with respect to the registered model and the modified bone model.

[0239] Clause 14A. The method of clause 1A, further comprising: determining, by the processor, a resection based on the parameters of the implant; and robotically assisting, by the processor, a resection tool to perform the resection.

[0240] Clause 15A. A system for determining parameters of an implant, the system comprising: a processor; and a non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the processor to: receive a pre-operative bone model; receive locational data on a bone surface; register the pre-operative bone model based on the locational data to generate a registered model; modify the registered model based on the locational data to generate a modified model; and determine parameters of an implant based on the registered model and the modified model.

[0241] Clause 16A. The system of clause 15 A, wherein the pre-operative bone model is pre-segmented.

[0242] Clause 17A. The system of clause 15A, wherein the one or more programming instructions that cause the processor to receive the pre-operative bone model further cause the processor to receive imagery from at least one of a CT scan or an MRI scan.

[0243] Clause 18A. The system of clause 15A, wherein the one or more programming instructions further cause the processor to: receive a statistical shape model of the bone surface; and fit the statistical shape model to the pre-operative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0244] Clause 19A. The system of clause 15A, further comprising a point probe, wherein the one or more programming instructions that cause the processor to receive locational data further cause the processor to receive locational data from the point probe.

[0245] Clause 20A. The system of clause 15 A, wherein the parameters of the implant comprise a type and a size of the implant.

[0246] Clause 21 A. The system of clause 15 A, wherein the parameters of the implant comprise a position and an orientation of the implant.

[0247] Clause 22A. The system of clause 15A, wherein the one or more programming instructions further cause the processor to: determine a resection based on the parameters of the implant; and robotically assist a resection tool to perform the resection.

[0248] Clause IB. A method for registering a pre-operative bone model, the method comprising: receiving, by a processor, the pre-operative bone model; receiving a statistical shape model of the bone surface; and fitting the statistical shape model to the pre-operative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model, wherein regions are defined on the statistical shape model; presenting the user with a visual representation of the pre-operative bone model, the visual representation including an indication of the regions; receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model.

[0249] Clause 2B. The method of clause IB, wherein registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

[0250] Clause 3B. The method of clause 2B, further comprising determining a distance of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

[0251] Clause 4B. The method of clause 3B, further comprising assessing a quality of the registration using distances of the locational data to the regions on the pre-operative bone model.

[0252] Clause 5B. The method of any one of clauses IB to 4B, wherein the predetermined regions are defined for the specific anatomy, the specific procedure, or both.

[0253] Clause 6B. The method of any one of clauses IB to 5B, wherein at least one of: the pre-operative bone model is pre-segmented, receiving the pre-operative bone model comprises receiving imagery from at least one of a CT scan or an MRI scan, or receiving locational data comprises receiving locational data from a point probe.

[0254] Clause 7B. The method of any one of clauses IB to 6B, wherein registering the pre-operative bone model is based on finding closest points on the pre-operative bone model to a subset of the locational data.

[0255] Clause 8B. The method of any one of clauses IB to 7B, wherein the registration is perturbed and repeated with varying subsets of the locational data.

[0256] Clause 9B. The method of any one of clauses IB to 8B, further comprising: modifying, by the processor, the registered model based on the locational data to generate a modified model; and determining, by the processor, parameters of an implant based on the registered model and the modified model.

[0257] Clause 10B. The method of clause 9B, wherein at least one of: the parameters of the implant comprise a type and a size of the implant, the parameters of the implant comprise a position and an orientation of the implant with respect to the pre-operative bone model, or the parameters of the implant are adjusted with respect to the registered model and the modified bone model.

[0258] Clause 1 IB. The method of any one of clauses IB to 10B, further comprising: determining, by the processor, a resection based on the parameters of the implant; and robotically assisting, by the processor, a resection tool to perform the resection.

[0259] Clause 12B. One or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to carry out the method of any one of clauses IB to 1 IB.

[0260] Clause 13B. A processor-readable storage medium, the processor-readable storage medium including one or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to carry out the method of any one of clauses IB to 1 IB.

[0261] Clause 14B. A system for registering a pre-operative bone model, the system comprising: a processor; and a non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the processor to carry out the method of any one of clauses IB to 11B.

[0262] Clause IC. A method for registering a pre-operative bone model, the method comprising: receiving, by a processor, a pre-operative bone model; receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model; and modifying, by the processor, the registered model based on the locational data to generate a modified model.

[0263] Clause 2C. The method of clause IC, wherein modifying the registered model includes a non-rigid registration of the pre-operative bone model based on the locational data.

[0264] Clause 3C. The method of clause IC or clause 2C, wherein the pre-operative bone model is pre-segmented.

[0265] Clause 4C. The method of any one of clauses IC to 3C, wherein receiving the pre-operative bone model comprises receiving imagery from at least one of a CT scan or an MRI scan.

[0266] Clause 5C. The method of any one of clauses IC to 4C, further comprising: receiving a statistical shape model of the bone surface; and fitting the statistical shape model to the pre-operative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0267] Clause 6C. The method of clause 5, wherein registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

[0268] Clause 7C. The method of clause 6C, further comprising determining a distance of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

[0269] Clause 8C. The method of clause 7C, further comprising assessing a quality of the registration using the distance of the locational data to regions on the pre-operative bone model.

[0270] Clause 9C. The method of any one of clauses IC to 8C, wherein receiving locational data comprises receiving locational data from a point probe.

[0271] Clause 10C. The method of any one of clauses IC to 9C, wherein registering the pre-operative bone model is based on finding closest points on the pre-operative bone model to a subset of the locational data.

[0272] Clause 11C. The method of any one of clauses IC to 10C, wherein the registration is perturbed and repeated with varying subsets of the locational data.

[0273] Clause 12C. The method of any one of clauses IC to 1 IC, further comprising determining, by the processor, parameters of an implant based on the registered model and the modified model.

[0274] Clause 13C. The method of any one of clauses IC to 12C, wherein at least one of: the parameters of the implant comprise a type and a size of the implant, the parameters of the implant comprise a position and an orientation of the implant with respect to the preoperative bone model, the parameters of the implant are adjusted with respect to the registered model and the modified bone model.

[0275] Clause 14C. The method of any one of clauses IC to 13C, further comprising: determining, by the processor, a resection based on the parameters of the implant; and robotically assisting, by the processor, a resection tool to perform the resection.

[0276] Clause 15C. A system for registering a pre-operative bone model, the system comprising: a processor; and a non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the processor to carry out the method of any one of clauses IC to 14C.

[0277] Clause 16C. One or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to carry out the method of any one of clauses 1 to 14.

[0278] Clause 17C. A processor-readable storage medium, the processor-readable storage medium including one or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to carry out the method of any one of clauses 1 to 14.

[0279] Clause ID. A method for registering a pre-operative bone model, the method comprising: receiving, by a processor, a pre-operative bone model; performing a registration process comprising: receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model; and requiring a predetermined coverage of predetermined regions on the bone by the locational data.

[0280] Clause 2D. The method of clause ID, wherein the predetermined regions are defined for a specific anatomy, a specific procedure, or both.

[0281] Clause 3D. The method of clause ID, further comprising assessing a quality of the registration using distances of the locational data to regions on the pre-operative bone model.

[0282] Clause 4D. The method of clause 3D, further comprising determining, based on the assessing, whether to continue from the registration process.

[0283] Clause 5D. The method of clause 3D, further comprising informing the collection of locational data based on the distance of the locational data to the predetermined regions.

[0284] Clause 6D. The method of any one of clauses ID to 5D, further comprising: receiving a statistical shape model of the bone surface; and fitting the statistical shape model to the pre-operative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0285] Clause 7D. The method of clause 6D, wherein registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

[0286] Clause 8D. The method of clause 7D, further comprising determining a distance of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

[0287] Clause 9D. The method of clause 8D, wherein the predetermined regions are defined on the statistical shape model.

[0288] Clause 10D. The method of any one of clauses ID to 9D, further comprising presenting a visual representation of the pre-operative bone model or the registered preoperative bone model, the visual representation including an indication of the regions.

[0289] Clause 1 ID. The method of any one of clauses ID to 10D, further comprising: modifying, by the processor, the registered model based on the locational data to generate a modified model; and determining, by the processor, parameters of an implant based on the registered model and the modified model.

[0290] Clause 12D. The method of clause 11D, wherein one or more of: the parameters of the implant comprise a position and an orientation of the implant with respect to the pre-operative bone model, or the parameters of the implant are adjusted with respect to the registered model and the modified bone model.

[0291] Clause 13D. The method of clause 11D, further comprising: determining, by the processor, a resection based on the parameters of the implant; and robotically assisting, by the processor, a resection tool to perform the resection.

[0292] Clause 14D. The method of any one of clauses ID to 13D, further comprising instructing a user to collect the location data in the predetermined regions.

[0293] Clause 15D. A system for registering a pre-operative bone model, the system comprising: a processor; and a non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the processor to carry out the method of any one of clauses ID to 14D.

[0294] Clause 16D. One or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to carry out the method of any one of clauses ID to 14D.

[0295] Clause 17D. A processor-readable storage medium, the processor-readable storage medium including one or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to carry out the method of any one of clauses ID to 14D.

[0296] Clause IE. A method for registering a pre-operative bone model, the method comprising: receiving, by a processor, the pre-operative bone model; performing a registration process comprising: causing, by the processor, provision of a visual representation of the preoperative bone model, the visual representation including an indication of predetermined regions of the pre-operative bone model; receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model; assessing, by the processor, the locational data with respect to the predetermined regions to determine predetermined regions for which locational data collection is incomplete; and causing, by the processor, an indication in the visual representation of the predetermined regions for which locational data collection is incomplete.

[0297] Clause 2E. The method of clause IE, wherein assessing the locational data includes assessing a coverage of a predetermined region of the predetermined regions by locational data.

[0298] Clause 3E. The method of clause IE, wherein assessing the locational data includes assessing a quality of the registration of the pre-operative bone model in a predetermined region of the predetermined regions.

[0299] Clause 4E. The method of clause IE, comprising iteratively performing, until locational data collection is determined by the processor to be complete: the provision of a visual representation, the receiving locational data, the registering the pre-operative bone model, the assessing the locational data, and the indicating predetermined regions for which locational data collection is incomplete.

[0300] Clause 5E. The method of clause IE, comprising causing, by the processor, an indication, in the visual representation, of the assessment of the locational data for at least one region of the predetermined regions.

[0301] Clause 6E. The method of clause IE, comprising: outputting, by the processor, a prompt to the user to collect locational data in a particular region of the predetermined regions while it is determined that data collection is incomplete in the particular region, and in response to a determination that the collection of locational data in the particular region is complete, outputting, by the processor, a prompt to the user to collect locational data in another region of the one or more regions.

[0302] Clause 7E. The method of clause 3E, wherein the assessment of the quality of the registration in the predetermined region is based on at least one of: a metric, the metric based on the locational data and the pre-operative bone model in the predetermined region; a statistical measure of differences between at least a subset of the locational data and the preoperative bone model in the predetermined region; a statistical measure of differences between at least a subset of the locational data and the pre-operative bone model in the predetermined region; one or more of a standard deviation, a variance, a statistical mode, a cumulant, or a moment of differences between at least a subset of the locational data and the pre-operative bone model in the predetermined region; or a proportion of the differences between at least a subset of the locational data and the pre-operative bone model in the predetermined region that are within a predetermined threshold difference.

[0303] Clause 8E. The method of clause 7E, further comprises: receiving a statistical shape model of the bone surface; and fitting the statistical shape model to the preoperative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model, and wherein differences between at least the subset of the locational data and the pre-operative bone model in the predetermined region are determined based on respective distances between the subset of the locational data and the fitted statistical shape model.

[0304] Clause 9E. The method of clause IE, further comprising: receiving a statistical shape model of the bone surface; and fitting the statistical shape model to the preoperative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0305] Clause 10E. The method of clause 9E, wherein the statistical shape model includes identified anatomical regions, and wherein fitting the statistical shape model to the pre-operative bone model identifies regions of the pre-operative bone model that correspond with the respective identified anatomical regions.

[0306] Clause 1 IE. The method of clause 9E, wherein registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

[0307] Clause 12E. The method of clause HE, further comprising determining a distance of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

[0308] Clause 13E. The method of clause 12E, further comprising assessing a quality of the registration using the distance of the locational data to regions on the pre-operative bone model.

[0309] Clause 14E. The method of clause IE, further comprising: determining, by the processor, parameters of an implant, based on the registered model.

[0310] Clause 15E. The method of clause 14E, wherein at least one of: the parameters of the implant comprise a type and a size of the implant, the parameters of the implant comprise a position and an orientation of the implant with respect to the pre-operative bone model, or the parameters of the implant are adjusted with respect to the registered model.

[0311] Clause 16E. The method of clause 14E, further comprising: determining, by the processor, a resection based on the parameters of the implant; and robotically assisting, by the processor, a resection tool to perform the resection.

[0312] Clause 17E. A system for registering a pre-operative bone model, the system comprising: a processor; and a non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the processor to: receive the pre-operative bone model; perform a registration process comprising: causing provision of a visual representation of the pre-operative bone model, the visual representation including an indication of predetermined regions of the pre-operative bone model; receiving locational data on a bone surface; registering the pre-operative bone model based on the locational data to generate a registered model; assessing the locational data with respect to the predetermined regions to determine predetermined regions for which locational data collection is incomplete; and causing an indication in the visual representation of the predetermined regions for which locational data collection is incomplete.

[0313] Clause 18E. The system of clause 17E, wherein assessing the locational data includes assessing a coverage of a predetermined region of the predetermined regions by locational data.

[0314] Clause 19E. The system of clause 17E, wherein assessing the locational data includes assessing a quality of the registration of the pre-operative bone model in a predetermined region of the predetermined regions.

[0315] Clause 20E. The system of clause 17E, wherein the one or more programming instructions, when executed, cause the processor to iteratively perform, until locational data collection is determined by the processor to be complete: the provision of a visual representation, the receiving locational data, the registering the pre-operative bone model, the assessing the locational data, and the indicating predetermined regions for which locational data collection is incomplete.

[0316] Clause 21E. The system of clause 17E, wherein the one or more programming instructions, when executed, cause the processor to cause an indication, in the visual representation, of the assessment of the locational data for at least one region of the predetermined regions.

[0317] Clause 22E. A non-transitory, processor-readable storage medium, the non-transitory, processor-readable storage medium including one or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to: receive the pre-operative bone model; perform a registration process comprising: causing provision of a visual representation of the pre-operative bone model, the visual representation including an indication of predetermined regions of the pre-operative bone model; receiving locational data on a bone surface; registering the pre-operative bone model based on the locational data to generate a registered model; assessing the locational data with respect to the predetermined regions to determine predetermined regions for which locational data collection is incomplete; and causing an indication in the visual representation of the predetermined regions for which locational data collection is incomplete.

[0318] Clause 23E. The non-transitory, processor-readable storage medium of clause 22E, wherein assessing the locational data includes at least one of: assessing a coverage of a predetermined region of the predetermined regions by locational data, or assessing the locational data includes assessing a quality of the registration of the pre-operative bone model in a predetermined region of the predetermined regions.

[0319] Clause 24E. The non-transitory, processor-readable storage medium of clause 22E, wherein the one or more programming instructions, when executed, cause the processor to iteratively perform, until locational data collection is determined by the processor to be complete: the provision of a visual representation, the receiving locational data, the registering the pre-operative bone model, the assessing the locational data, and the indicating predetermined regions for which locational data collection is incomplete.

[0320] Clause 25E. The system of clause 22E, wherein the one or more programming instructions, when executed, cause the processor to cause an indication, in the visual representation, of the assessment of the locational data for at least one region of the predetermined regions.

[0321] Clause IF. A method for identifying regions on a pre-operative bone model, the method comprising: receiving, by a processor, the pre-operative bone model; receiving, by the processor, a statistical shape model of the bone surface, wherein regions are defined on the statistical shape model; fitting, by the processor, the statistical shape model to the pre-operative bone model; and identifying regions on the pre-operative bone model based on the fitting and the regions defined on the statistical shape model.

[0322] Clause 2F. The method of clause IF, further comprising: receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model.

[0323] Clause 3F. The method of clause IF, further comprising presenting the user with a visual representation of the pre-operative bone model, the visual representation including an indication of the regions.

[0324] Clause 4F. The method of clause IF, wherein the fitting the statistical shape model to the pre-operative bone model places the pre-operative bone model in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0325] Clause 5F. The method of clause IF, wherein the statistical shape model includes identified anatomical regions, and wherein fitting the statistical shape model to the pre-operative bone model identifies regions of the pre-operative bone model that correspond with the respective identified anatomical regions.

[0326] Clause 6F. The method of clause IF, wherein registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

[0327] Clause 7F. The method of clause 8F, further comprising determining distance of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

[0328] Clause 8F. The method of clause 7F, further comprising assessing a quality of the registration using the distance of the locational data to regions on the pre-operative bone model.

[0329] Clause 9F. The method of clause 2F, wherein the method further comprises: determining, by the processor, a quality of the registration of the pre-operative bone model in a region of the pre-operative bone model; and outputting, by the processor, an indication of progress of locational data collection, based on the determination of the quality of the registering.

[0330] Clause 10F. The method of clause 2F, further comprising: modifying, by the processor, the registered model based on the locational data to generate a modified model.

[0331] Clause 11F. The method of clause 10F, wherein modifying the registered model includes a non-rigid registration of the pre-operative bone model based on the locational data.

[0332] Clause 12F. The method of clause IF, further comprising determining, by the processor, parameters of an implant based on the registered model and the modified model.

[0333] Clause 13F. The method of clause 12F, wherein at least one of: the parameters of the implant comprise a type and a size of the implant, the parameters of the implant comprise a position and an orientation of the implant with respect to the pre-operative bone model, or the parameters of the implant are adjusted with respect to the registered model and the modified bone model.

[0334] Clause 14F. The method of clause 12F, further comprising: determining, by the processor, a resection based on the parameters of the implant; and robotically assisting, by the processor, a resection tool to perform the resection.

[0335] Clause 15F. A system for registering a pre-operative bone model, the system comprising: a processor; and a non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the processor to: receive the pre-operative bone model; receive a statistical shape model of the bone surface, wherein regions are defined on the statistical shape model; fit the statistical shape model to the pre-operative bone model; and identify regions on the pre-operative bone model based on the fitting and the regions defined on the statistical shape model.

[0336] Clause 16F. The system of clause 15F, wherein the one or more programming instructions, when executed, cause the processor to: receive locational data on a bone surface; register the pre-operative bone model based on the locational data to generate a registered model.

[0337] Clause 17F. The system of clause 15F, wherein the one or more programming instructions, when executed, cause the processor to present the user with a visual representation of the pre-operative bone model, the visual representation including an indication of the regions.

[0338] Clause 18F. The system of clause 15F, wherein the fitting the statistical shape model to the pre-operative bone model places the pre-operative bone model in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0339] Clause 19F. The system of clause 15F, wherein the statistical shape model includes identified anatomical regions, and wherein fitting the statistical shape model to the pre-operative bone model identifies regions of the pre-operative bone model that correspond with the respective identified anatomical regions.

[0340] Clause 20F. The method of clause 16F, wherein the method further comprises: determining, by the processor, a quality of the registration of the pre-operative bone model in a region of the pre-operative bone model; and outputting, by the processor, an indication of progress of locational data collection, based on the determination of the quality of the registering.

[0341] Clause 21F. A non-transitory, processor-readable storage medium, the non-transitory, processor-readable storage medium including one or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to: receive the pre-operative bone model; receive a statistical shape model of the bone surface, wherein regions are defined on the statistical shape model; fit the statistical shape model to the pre-operative bone model; and identify regions on the pre-operative bone model based on the fitting and the regions defined on the statistical shape model.

[0342] Clause 22F. The non-transitory, processor-readable storage medium of clause 2IF, wherein the one or more programming instructions, when executed, cause the processor to: receive locational data on a bone surface; register the pre-operative bone model based on the locational data to generate a registered model.

[0343] Clause 23F. The non-transitory, processor-readable storage medium of clause 2IF, wherein the one or more programming instructions, when executed, cause the processor to present the user with a visual representation of the pre-operative bone model, the visual representation including an indication of the regions.

[0344] Clause 24F. The non-transitory, processor-readable storage medium of clause 2IF, wherein the fitting the statistical shape model to the pre-operative bone model places the pre-operative bone model in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0345] Clause 25F. The non-transitory, processor-readable storage medium of clause 2IF, wherein the statistical shape model includes identified anatomical regions, and wherein fitting the statistical shape model to the pre-operative bone model identifies regions of the pre-operative bone model that correspond with the respective identified anatomical regions.

[0346] Clause 1G. A method for registering a pre-operative bone model, the method comprising: receiving, by a processor, a pre-operative bone model; receiving, by the processor, locational data on a bone surface; registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model; and modifying, by the processor, the registered model based on the locational data to generate a modified model.

[0347] Clause 2G. The method of clause 1G, wherein modifying the registered model includes a non-rigid registration of the pre-operative bone model based on the locational data.

[0348] Clause 3G. The method of clause 1G, wherein the registering the preoperative bone model includes a rigid registration of the pre-operative bone model based on the locational data.

[0349] Clause 4G. The method of clause 1G, further comprising: assessing, by the processor, the locational data with respect to predetermined regions of the pre-operative model to determine predetermined regions for which locational data collection is incomplete, and iteratively performing, until locational data collection is determined by the processor to be complete: the receiving locational data, the registering the pre-operative bone model, the assessing the locational data.

[0350] Clause 5G. The method of clause 4G, wherein the modifying the registered model is performed after locational data collection is determined by the processor to be complete.

[0351] Clause 6G. The method of clause 1G, further comprising: receiving a statistical shape model of the bone surface; and fitting the statistical shape model to the preoperative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0352] Clause 7G. The method of clause 6G, wherein the statistical shape model includes identified anatomical regions, and wherein fitting the statistical shape model to the pre-operative bone model identifies regions of the pre-operative bone model that correspond with the respective identified anatomical regions.

[0353] Clause 8G. The method of clause 6G, wherein registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

[0354] Clause 9G. The method of clause 8G, further comprising determining distances of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

[0355] Clause 10G. The method of clause 9G, further comprising assessing a quality of the registration using distances of the locational data to regions on the pre-operative bone model.

[0356] Clause 11G. The method of clause 1G, further comprising determining, by the processor, parameters of an implant based on the registered model and the modified model.

[0357] Clause 12G. The method of clause 11G, wherein at least one of: the parameters of the implant comprise a type and a size of the implant, the parameters of the implant comprise a position and an orientation of the implant with respect to the pre-operative bone model, or the parameters of the implant are adjusted with respect to the registered model and the modified bone model.

[0358] Clause 13G. The method of clause 11G, further comprising: determining, by the processor, a resection based on the parameters of the implant; and robotically assisting, by the processor, a resection tool to perform the resection.

[0359] Clause 14G. A system for registering a pre-operative bone model, the system comprising: a processor; and a non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the processor to: receive a pre-operative bone model; receive locational data on a bone surface; register the pre-operative bone model based on the locational data to generate a registered model; and modify the registered model based on the locational data to generate a modified model.

[0360] Clause 15G. The system of clause 14G, wherein modifying the registered model includes a non-rigid registration of the pre-operative bone model based on the locational data.

[0361] Clause 16G. The system of clause 14G, wherein the registering the preoperative bone model includes a rigid registration of the pre-operative bone model based on the locational data.

[0362] Clause 17G. The system of clause 14G, wherein the one or more programming instructions, when executed, cause the processor to: assess, by the processor, the locational data with respect to predetermined regions of the pre-operative model to determine predetermined regions for which locational data collection is incomplete, and iteratively perform, until locational data collection is determined by the processor to be complete: the receiving locational data, the registering the pre-operative bone model, the assessing the locational data.

[0363] Clause 18G. The system of clause 17G, wherein the modifying the registered model is performed after locational data collection is determined by the processor to be complete.

[0364] Clause 19G. The system of clause 14G, wherein the one or more programming instructions, when executed, cause the processor to: receive a statistical shape model of the bone surface; and fit the statistical shape model to the pre-operative bone model, wherein the pre-operative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model.

[0365] Clause 20G. The system of clause 19G, wherein the statistical shape model includes identified anatomical regions, and wherein fitting the statistical shape model to the pre-operative bone model identifies regions of the pre-operative bone model that correspond with the respective identified anatomical regions.

[0366] Clause 21G. A non-transitory, processor-readable storage medium, the non-transitory, processor-readable storage medium including one or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to: receive a pre-operative bone model; receive locational data on a bone surface; register the preoperative bone model based on the locational data to generate a registered model; and modify the registered model based on the locational data to generate a modified model.

[0367] Clause 22G. The system of clause 21G, wherein modifying the registered model includes a non-rigid registration of the pre-operative bone model based on the locational data.

[0368] Clause 23G. The system of clause 21G, wherein the registering the preoperative bone model includes a rigid registration of the pre-operative bone model based on the locational data.

[0369] Clause 24G. The system of clause 21G, wherein the one or more programming instructions, when executed, cause the processor to: assess, by the processor, the locational data with respect to predetermined regions of the pre-operative model to determine predetermined regions for which locational data collection is incomplete, and iteratively perform, until locational data collection is determined by the processor to be complete: the receiving locational data, the registering the pre-operative bone model, the assessing the locational data.

[0370] Clause 25G. The system of clause 24G, wherein the modifying the registered model is performed after locational data collection is determined by the processor to be complete.

Claims

1. A method for registering a pre-operative bone model, the method comprising:receiving, by a processor, the pre-operative bone model;receiving a statistical shape model of the bone surface; andfitting the statistical shape model to the pre-operative bone model, wherein the preoperative bone model is placed in a statistical approximation of an anatomic frame based upon the statistical shape model, wherein regions are defined on the statistical shape model;presenting the user with a visual representation of the pre-operative bone model, the visual representation including an indication of the regions;receiving, by the processor, locational data on a bone surface;registering, by the processor, the pre-operative bone model based on the locational data to generate a registered model.

2. The method of claim 1, wherein registering the pre-operative bone model generates a registered pre-operative bone model and a registered statistical shape model.

3. The method of claim 2, further comprising determining a distance of the locational data to regions on the pre-operative bone model based on the registered statistical shape model.

4. The method of claim 3, further comprising assessing a quality of the registration using distances of the locational data to the regions on the pre-operative bone model.

5. The method of any preceding claim, wherein the predetermined regions are defined for the specific anatomy, the specific procedure, or both.

6. The method of any preceding claim, wherein at least one of: the pre-operative bone model is pre-segmented, receiving the pre-operative bone model comprises receiving imagery from at least oneof a CT scan or an MRI scan, orreceiving locational data comprises receiving locational data from a point probe.

7. The method of any preceding claim, wherein registering the pre-operative bone model is based on finding closest points on the pre-operative bone model to a subset of the locational data.

8. The method of any preceding claim, wherein the registration is perturbed and repeated with varying subsets of the locational data.

9. The method of any preceding claim, further comprising:modifying, by the processor, the registered model based on the locational data to generate a modified model; anddetermining, by the processor, parameters of an implant based on the registered model and the modified model.

10. The method of claim 9, wherein at least one of:the parameters of the implant comprise a type and a size of the implant,the parameters of the implant comprise a position and an orientation of the implantwith respect to the pre-operative bone model, orthe parameters of the implant are adjusted with respect to the registered model and the modified bone model.

11. The method of any preceding claim, further comprising:determining, by the processor, a resection based on the parameters of the implant; and robotically assisting, by the processor, a resection tool to perform the resection.

12. A method for registering a pre-operative bone model, the method comprising:receiving, by a processor, a pre-operative bone model;receiving, by the processor, locational data on a bone surface;registering, by the processor, the pre-operative bone model based on the locationaldata to generate a registered model; andmodifying, by the processor, the registered model based on the locational data to generate a modified model.

13. The method of claim 1, wherein modifying the registered model includes a non-rigid registration of the pre-operative bone model based on the locational data.

14. A method for registering a pre-operative bone model, the method comprising:receiving, by a processor, a pre-operative bone model;performing a registration process comprising:receiving, by the processor, locational data on a bone surface;registering, by the processor, the pre-operative bone model based on thelocational data to generate a registered model; andrequiring a predetermined coverage of predetermined regions on the bone by the locational data.

15. The method of claim 14, further comprising assessing a quality of the registration using distances of the locational data to regions on the pre-operative bone model.16 The method of claim 15, further comprising determining, based on the assessing, whether to continue from the registration process.

17. The method of claim 15, further comprising informing the collection of locational data based on the distance of the locational data to the predetermined regions.

18. The method of any one of claims 14 to 17, further comprising presenting a visual representation of the pre-operative bone model or the registered pre-operative bone model, the visual representation including an indication of the regions.

19. The method of any one of claims 14 to 18, further comprising instructing a user to collect the location data in the predetermined regions.

20. One or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to carry out the method of any one of claims 1 to 19.

21. A processor-readable storage medium, the processor-readable storage medium including one or more programing instructions, wherein the one or more programming instructions, when executed, cause the processor to carry out the method of any one of claims 1 to 19.

22. A system for registering a pre-operative bone model, the system comprising: a processor; anda non-transitory, processor-readable storage medium, wherein the non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the processor to carry out the method of any one of claims 1 to 19.

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