Intraoperative Knee Joint Motion Assessment Method and System

Abstract

An intraoperative knee joint motion evaluation method and system are provided in an embodiment of the present disclosure, belonging to the technical field of knee joint data processing. The method includes: pre-acquiring a three-dimensional model of a leg bone related to the knee joint; respectively arranging at least one laser target on the femoral side and the tibial side of the knee joint; using a laser tracker to read the relative positions of the laser targets on the femoral side and the tibial side to form the relative motion trajectories of the femur and the tibia; by selecting at least one of a variety of built-in motion estimation evaluation algorithms, performing data processing on the relevant motion trajectories of the knee joint to form a gap curve for evaluating the motion of the knee joint. Through the processing solution of the present disclosure, the gap curve of the knee joint can be estimated, so as to comprehensively and accurately understand the joint motion situation, provide effective motion evaluation for orthopedic and sports medicine surgeries, and provide important data support for postoperative rehabilitation guidance.

Description

Technical Field

[0001] The present disclosure relates to the technical field of knee joint data processing, and in particular, to a method and system for intraoperative knee joint motion assessment. Background Art

[0002] Orthopedic and sports medicine surgeries aim to restore the normal motor function of the knee joint. A method with high precision and capable of accurately measuring the knee joint motion is the basis for preoperative surgical planning and intraoperative surgical navigation decision-making. The service life of joint prostheses, postoperative joint pain, etc. will directly affect the success or failure of the surgery and the postoperative effect. Since there are extremely complex interactions between the hard bone tissue and soft tissue during the knee joint motion, it poses many challenges to measure the true motion of the knee joint. The main reasons include the following points:

[0003] 1) The normal knee joint has a large range of motion. From full extension to full flexion, the range of motion angle may be 0° to 135°. According to the ideal evaluation method, joint motion evaluation should evaluate each motion position within the range of motion. However, currently in clinical practice, due to technical limitations, usually only two motion positions, i.e., full extension at 0° and knee flexion at 90°, can be measured.

[0004] 2) Since the motion of the knee joint varies with different loads. Therefore, it is necessary to measure under reasonable load conditions to obtain an accurate motion evaluation, which also greatly increases the measurement difficulty.

[0005] 3) In addition, among patients who need knee joint surgery, joint instability is prevalent, that is, at the same flexed position, the joint position is not unique. Summary of the Invention

[0006] In view of this, embodiments of the present disclosure provide a method and system for intraoperative knee joint motion assessment to at least partially solve the problems existing in the prior art.

[0007] In a first aspect, embodiments of the present disclosure provide a method for intraoperative knee joint motion assessment, including:

[0008] Pre-obtaining a three-dimensional model of the leg bone related to the knee joint;

[0009] Respectively setting at least one laser target on the femoral side and the tibial side of the knee joint to facilitate describing the movement trajectories of the femur and the tibia through the laser targets;

[0010] Based on a pre-set femoral coordinate system and tibial coordinate system, using a laser tracker to read the relative positions of the laser targets on the femoral side and the tibial side to form the relative movement trajectories of the femur and the tibia;

[0011] By selecting at least one of a variety of built-in motion estimation and evaluation algorithms, data processing is performed on the relevant motion trajectories of the knee joint to form a gap curve for evaluating the motion of the knee joint. The motion estimation and evaluation algorithms include at least one of a full-cycle measurement method, a variable-load measurement method, and a geometric smoothing filtering signal processing method on SE(3) Lie group.

[0012] According to a specific implementation manner of an embodiment of the present disclosure, the step of performing data processing on the relevant motion trajectories of the knee joint by selecting at least one of a variety of built-in motion estimation and evaluation algorithms to form a gap curve for evaluating the motion of the knee joint includes:

[0013] Swing the tibia while keeping the femur fixed to facilitate recording the pose positions of the tibia and the femur in three-dimensional space by a laser tracker;

[0014] Perform data operations on the pose positions of the tibia and the femur in three-dimensional space to obtain the relative position T between the femur and the tibia;

[0015] Based on the distribution of the relative position with respect to the flexion angle, form a gap curve for evaluating the motion of the knee joint. According to a specific implementation manner of an embodiment of the present disclosure, after forming the gap curve for evaluating the motion of the knee joint based on the distribution of the relative position with respect to the flexion angle, the method further includes:

[0016] Extract data values from the gap curve to obtain the medial and lateral gap values between the femur and the tibia; based on the medial and lateral gap values, perform a gap evaluation on the knee joint.

[0017] According to a specific implementation manner of an embodiment of the present disclosure, after forming the gap curve for evaluating the motion of the knee joint based on the distribution of the relative position with respect to the flexion angle, the method further includes:

[0018] Extract data values from the gap curve to obtain the force line alignment situation between the femur and the tibia, where the force line alignment situation includes the flexion angle, varus / valgus angle, and internal / external rotation angle related to the knee joint;

[0019] Based on the offline alignment situation, perform a force line evaluation on the knee joint. The gap evaluation and the force line evaluation together constitute the full-cycle evaluation of the knee joint.

[0020] According to a specific implementation manner of an embodiment of the present disclosure, the step of performing data processing on the relevant motion trajectories of the knee joint by selecting at least one of a variety of built-in motion estimation and evaluation algorithms to form a gap curve for evaluating the motion of the knee joint includes:

[0021] By applying an external force to the knee joint and recording the different relative position relationships between the femur and tibia of the knee joint at the same flexion angle, the knee joint movement conditions at different positions at the same flexion angle are evaluated.

[0022] According to a specific implementation manner of an embodiment of the present disclosure, the recording of the different relative position relationships between the femur and tibia of the knee joint at the same flexion angle includes:

[0023] By adopting a repeated measurement method, the medial and lateral clearance values at the same flexion angle are compared among the data collected multiple times, and the clearance values that meet the requirements are selected. Finally, the medial and lateral clearance values at all flexion angles are fitted into an optimal clearance curve.

[0024] According to a specific implementation manner of an embodiment of the present disclosure, the data processing of the relevant movement trajectories of the knee joint by selecting at least one of a variety of built-in motion estimation and evaluation algorithms to form a clearance curve for evaluating the movement of the knee joint includes:

[0025] A series of relative positions T between the femur and tibia are obtained to form a data sequence;

[0026] Using a preset formula, geometric smoothing filtering signal processing on the SE(3) Lie group of the data sequence is performed to obtain a filtered curve T p (u);

[0027] Monitor the newly obtained sampling values to determine whether new sampling values are added;

[0028] If so, update the number of sampling points, and use the updated number of sampling points to perform filtering processing on the data sequence with the newly added sampling points by using the preset formula again.

[0029] According to a specific implementation manner of an embodiment of the present disclosure, the using a preset formula to perform geometric smoothing filtering signal processing on the SE(3) Lie group of the data sequence to obtain a filtered curve T p (u) includes:

[0030] Set the parameters of the filtered curve T p (u) as p, and regard the motion curve T as a function T p (u) of the flexion angle, where u is the flexion angle. For the sampling points T1, T2,..., T n , the corresponding flexion angles are u1, u2,..., u n , and using the formula

[0031]

[0032] After optimization, the sum of the squared errors between the curve and each sampling point is minimized.

[0033] According to a specific implementation manner of the embodiments of the present disclosure, the method further includes:

[0034] For T p (u) Use a cubic spline curve. In the range from the minimum value to the maximum value, take 10 - 20 points on the connecting line as control points, and the spline interpolation is calculated using the formula for calculation.

[0035] where are the control points with respect to u in the spline function (j = 0, 1, 2, 3), Log(T) is the form of converting the Lie group T into the Lie algebra, and Exp(τ) is the form of converting the Lie algebra τ into the Lie group, and

[0036]

[0037] In a second aspect, the embodiments of the present disclosure provide an intraoperative knee joint movement evaluation system, including:

[0038] A memory in which a pre - acquired three - dimensional model of the leg bone related to the knee joint is stored;

[0039] Laser targets, which are respectively on the femoral side and the tibial side of the knee joint, so as to describe the movement trajectories of the femur and the tibia through the laser targets;

[0040] A laser tracker, based on a pre - set femoral coordinate system and tibial coordinate system, reads the relative positions of the laser targets on the femoral side and the tibial side by using the laser tracker to form the relative movement trajectories of the femur and the tibia;

[0041] A calculation module, by selecting at least one of a variety of built - in motion estimation and evaluation algorithms, processes the data of the relevant movement trajectories of the knee joint to form a clearance curve for evaluating the movement of the knee joint. The motion estimation and evaluation algorithms include at least one of a full - cycle measurement method, a variable - load measurement method, and a geometric smoothing filtering signal processing method on the SE(3) Lie group, so as to execute the intraoperative knee joint movement evaluation method in the foregoing first aspect or any implementation manner of the first aspect.

[0042] In a third aspect, the embodiments of the present disclosure further provide a non - transitory computer - readable storage medium, which stores computer instructions for causing the computer to execute the intraoperative knee joint movement evaluation method in the foregoing first aspect or any implementation manner of the first aspect.

[0043] Fourthly, an embodiment of the present disclosure further provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the intraoperative knee joint motion evaluation method in the foregoing first aspect or any implementation manner of the first aspect.

[0044] The intraoperative knee joint motion evaluation solution in the embodiment of the present disclosure includes: pre-acquiring a three-dimensional model of leg bones related to the knee joint; respectively arranging at least one laser target on the femoral side and the tibial side of the knee joint, so as to describe the movement trajectories of the femur and the tibia through the laser targets; based on a pre-set femoral coordinate system and tibial coordinate system, using a laser tracker to read the relative positions of the laser targets on the femoral side and the tibial side, and forming the relative movement trajectories of the femur and the tibia; by selecting at least one of a variety of built-in motion estimation and evaluation algorithms, performing data processing on the relevant motion trajectories of the knee joint to form a gap curve for evaluating the motion of the knee joint. The motion estimation and evaluation algorithms include at least one of a full-cycle measurement method, a variable-load measurement method, and a geometric smoothing filter signal processing method on SE(3) Lie group. Through the processing solution of the present disclosure, in actual clinical practice, a doctor can select a suitable measurement method according to needs. Through the gap curve estimated by this system, a doctor can comprehensively and accurately understand the joint motion of a patient, providing an effective motion evaluation method for orthopedic and sports medicine surgeries and providing an important basis for postoperative rehabilitation guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following will briefly introduce the drawings required for the embodiments. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 A schematic diagram of the installation of a laser target provided by an embodiment of the present disclosure;

[0047] Figure 2 A schematic diagram of the femoral coordinate system provided by an embodiment of the present disclosure;

[0048] Figure 3 A schematic diagram of the tibial coordinate system provided by an embodiment of the present disclosure;

[0049] Figure 4 A flowchart of the full-cycle measurement method provided by an embodiment of the present disclosure;

[0050] Figure 5 A schematic diagram of the knee joint flexion angle provided by an embodiment of the present disclosure;

[0051] Figure 6 Schematic diagram of valgus angle provided by an embodiment of the present disclosure;

[0052] Figure 7 Schematic diagram of varus angle provided by an embodiment of the present disclosure;

[0053] Figure 8 Schematic diagram of external rotation angle provided by an embodiment of the present disclosure;

[0054] Figure 9 Schematic diagram of internal rotation angle provided by an embodiment of the present disclosure;

[0055] Figure 10 Schematic diagram of medial and lateral clearances at 0° flexion provided by an embodiment of the present disclosure;

[0056] Figure 11 Schematic diagram of medial and lateral clearances at 90° flexion provided by an embodiment of the present disclosure;

[0057] Figure 12 Clearance curve graph provided by an embodiment of the present disclosure;

[0058] Figure 13 Flowchart of multi-sampling curve update strategy provided by an embodiment of the present disclosure. Detailed implementation manners

[0059] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0060] The following uses specific specific examples to illustrate the implementation manners of the present disclosure. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts belong to the scope of protection of the present disclosure.

[0061] It should be noted that the following description relates to various aspects of embodiments within the scope of the appended claims. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement a device and / or practice a method. In addition, this device can be implemented and this method can be practiced using other structures and / or functions in addition to one or more of the aspects described herein.

[0062] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present disclosure schematically. Only the components related to the present disclosure are shown in the diagrams, rather than being drawn according to the number, shape and size of the components in actual implementation. The type, quantity and proportion of each component in its actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0063] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the aspects described herein can be practiced without these specific details.

[0064] An embodiment of the present disclosure provides a method for intraoperative knee joint movement assessment. The method for intraoperative knee joint movement assessment provided in this embodiment can be executed by a computing device, which can be implemented as software, or as a combination of software and hardware. The computing device can be integrally provided in a server, a client, etc.

[0065] See Figure 1 , Figure 2 and Figure 3 , the method for intraoperative knee joint movement assessment in the embodiments of the present disclosure may include the following steps:

[0066] S101, pre-acquire a three-dimensional model of the leg bone related to the knee joint;

[0067] S102, respectively set at least one laser target on the femoral side and the tibial side of the knee joint, so as to describe the movement trajectories of the femur and the tibia through the laser targets;

[0068] S103, based on the pre-set femoral coordinate system and tibial coordinate system, use a laser tracker to read the relative positions of the laser targets on the femoral side and the tibial side, and form the relative movement trajectories of the femur and the tibia;

[0069] S104. By selecting at least one of a variety of built-in motion estimation evaluation algorithms, data processing is performed on the relevant motion trajectories of the knee joint to form a gap curve for evaluating the motion of the knee joint. The motion estimation evaluation algorithms include at least one of a full-cycle measurement method, a variable-load measurement method, and a geometric smoothing filtering signal processing method on SE(3) Lie group.

[0070] Specifically, in the process of implementing steps S101 - 104, the method of the present invention employs a system for accurately measuring knee joint motion during surgery, including sensors, measurement methods, and signal processing. It can be applied to the following several methods:

[0071] 1) Full-cycle measurement method:

[0072] It can measure the entire motion range of the joint and the motion conditions of the joint at various positions within the entire motion range.

[0073] 2) Variable-load measurement method:

[0074] This measurement method supports variable-load measurement. During surgery, force can be applied to the knee joint during the extension and flexion of the patient's affected limb, and then the medial and lateral clearances of the femorotibial joint can be measured at different flexion angles, varus / valgus angles, and internal / external rotation angles, so that doctors can comprehensively understand the motion conditions of the knee joint of the patient's affected limb.

[0075] At the same time, this measurement method supports multiple measurements. During surgery, multiple extension and flexion clearance measurements can be performed on the patient's affected limb. Finally, the most reasonable value is selected from the results of multiple measurements, and then an optimal clearance curve is fitted.

[0076] 3) Geometric smoothing filtering signal processing technology on SE(3) Lie group:

[0077] There are complex relative position constraint relationships between joints. Strictly speaking, the relative positions of joints are located on a Lie group SE(3). Since the SE(3) group is non-linear, when using common linear filtering and interpolation methods, the generated postures may not be on the SE(3) group or the interpolated positions are not strictly uniform.

[0078] The present invention proposes a geometric filtering method on the Lie group. Mathematically, it ensures that the generated postures are strictly located on the SE(3) group and are uniform, and can more accurately reflect the motion relationship of joints.

[0079] To implement the above solution, see Figure 1 , the system of the present application mainly includes:

[0080] 1) High-performance computer: realizing high-performance operation control;

[0081] 2) Three-dimensional model of the leg bone: obtained from preoperative CT;

[0082] 3) Two laser targets: One is placed on the femoral side of the knee joint, and the other is placed on the tibial side of the knee joint;

[0083] 4) Laser tracker: Used to read the relative positions of the two laser targets in real time.

[0084] See Figure 2 and Figure 3 , in order to conveniently obtain the position coordinates of the femur and tibia, it is necessary to establish a femoral coordinate system and a tibial coordinate system, and their definition methods are shown in Table 1 and Table 2:

[0085] Table 1 Definition method of femoral coordinate system

[0086] Coordinate Definition method Z-axis The line connecting the center of the knee joint to the center of the femoral head, with the positive direction pointing to the center of the femoral head; Y-axis The axis passing through the center of the knee joint and perpendicular to the coronal plane, with the positive direction pointing to the front of the femur; X-axis Determined according to the Y-axis and Z-axis by the right-hand rule.

[0087] Table 2 Definition method of femoral coordinate system

[0088] Coordinate Definition method Z-axis The line connecting the center of the ankle joint to the center of the knee joint, with the positive direction pointing to the center of the knee joint; Y-axis The axis passing through the center of the knee joint and perpendicular to the coronal plane, with the positive direction pointing to the front of the tibia; X-axis Determined according to the Y-axis and Z-axis by the right-hand rule.

[0089] The flowchart of the full-cycle measurement method is as Figure 4 shown, and its measurement steps include:

[0090] 1) Extension and flexion movement of the affected limb:

[0091] During the operation, when performing a full-cycle movement assessment, first fix the patient's femur, and then swing the patient's tibia to make the affected limb perform a movement from full extension to flexion.

[0092] 2) The laser tracker records the three-dimensional postures of the femur and tibia:

[0093] ① T f : Represents the three-dimensional posture of the femur

[0094] ② T t : Represents the three-dimensional posture of the tibia

[0095] 3) Arithmetic processing to obtain the relative position T of the femur and tibia:

[0096] Based on the obtained three-dimensional postures of the femur and tibia, using formula [1], calculate to obtain T, which is the relative position of the femur and tibia during the operation.

[0097] Formula [1]: T = inv(T t )T f (where inv represents the inverse operation)

[0098] 4) Obtain a series of Ts: When using the full-cycle measurement method, it is necessary to measure the entire process of the affected limb from extension to flexion, and record a series of relative positions T of the femur and tibia during the process, denoted as T1, T2, T3,..., T n。

[0099] 5) Plot the curve T(u): The entire motion is mathematically represented as a curve T(u), where T includes two parts: spatial position and spatial direction.

[0100] 6) Extract the required values.

[0101] In actual clinical practice, the values required for clinical applications will be extracted and displayed to the doctor to facilitate the doctor's motion assessment.

[0102] ① Spatial direction component of T: Represented by the flexion angle, varus / valgus angle, and internal / external rotation angle used in the line of force assessment.

[0103] ② Spatial position component of T: Represented by the medial and lateral gap values between the femur and tibia used in the gap assessment.

[0104] After completing the above full-cycle measurement, a line of force assessment can also be performed. See Figures 5 - 9 , and the main objects for the line of force assessment include: flexion angle, varus / valgus angle, internal / external rotation angle

[0105] 1) Flexion angle

[0106] In the sagittal plane, the angle between the negative Z-axis of the femur Z F and the negative Z-axis of the tibia Z T is the flexion angle.

[0107] 2) Varus / valgus angle

[0108] The angle between the anatomical axis Z of the femur A and the positive Z-axis of the tibia Z T is the valgus or varus angle.

[0109] ① Valgus angle: As Figure 6 shown, with the tibia Z-axis as the reference, Z A deviates outward, which is the valgus angle;

[0110] ② Varus angle: As Figure 7 shown, with the tibia Z-axis as the reference, Z A deviates inward, which is the varus angle;

[0111] 3) Internal / external rotation angle

[0112] In the cross-section, the angle between the X-axis of the tibia X T and the X-axis of the femur X F is the internal or external rotation angle.

[0113] ① External rotation angle: As Figure 8 shown, with the X-axis of the tibia X F as the reference, the tibia rotates outward to form the external rotation angle, which is the external rotation angle;

[0114] ②Internal rotation angle: As Figure 9 shown, with the tibial X-axis X F as the reference, the tibia rotates inward to form the internal rotation angle, which is the internal rotation angle.

[0115] During the operation, in the surgical navigation system, the femur and tibia of the patient's affected limb that have been registered can be tracked in real time. By monitoring the knee joint femur-tibia gap (the distance from the lowest points of the medial and lateral femoral condyles to the tibial plateau) in various situations, the movement of the knee joint can be evaluated. Figure 10 、 Figure 11 are schematic diagrams of the medial and lateral gaps of the knee joint at 0° flexion and 90° flexion respectively.

[0116] The full-cycle measurement method described in the present invention can continuously monitor the medial and lateral gaps of the knee joint at all flexion angles (i.e., at all positions) within the range of knee joint movement.

[0117] ①Gap curve graph:

[0118] During the movement process of the affected limb from full extension to knee flexion, the medial and lateral gaps of the knee joint at all flexion angles are monitored and fitted into the Figure 12 gap curve shown, and its related description is shown in Table 3.

[0119] Table 3 Description of the content of the gap curve graph

[0120]

[0121] In addition to being able to perform full-cycle measurement, it can also be measured by the variable load repeated measurement method. Since at the same flexion angle, there may be different relative position relationships between the femur and tibia of the knee joint. In order to fully evaluate the movement of the patient's knee joint, an external force can be applied to the knee joint to evaluate the movement of the knee joint at different positions at the same flexion angle of the patient's affected limb. The variable load repeated measurement method described in the present invention mainly includes:

[0122] 1) Variable load measurement method

[0123] The variable load repeated measurement method described in this system supports the above-mentioned variable load measurement: it supports recording the gap curve during the knee flexion process in the natural state of the patient's affected limb to evaluate the movement situation; at the same time, it also supports recording the gap curve during the knee flexion process when an external force acts on the knee joint to evaluate the movement situation. This process can be flexibly selected by the surgeon according to his own needs for a suitable evaluation method.

[0124] 2) Repeated measurement to screen reasonable gap values

[0125] As described above, at the same flexion angle, the knee joint may be in different positions. To better comprehensively evaluate the movement of the knee joint, this measurement method supports repeated measurements. By comparing the medial and lateral clearance values at the same flexion angle in multiple collected data, the most reasonable clearance values that best meet the needs of surgeons are selected. Finally, the medial and lateral clearance values at all flexion angles are fitted into an optimal clearance curve.

[0126] 3) Screening rules for medial and lateral clearance values

[0127] For multiple sets of medial and lateral clearance values measured repeatedly at the same flexion angle, the following screening rules are adopted:

[0128] In a single measurement, if the clearance value on either one or both sides at this flexion angle is greater than the corresponding side clearance value in the previous measurement, then the medial and lateral clearance values at this flexion angle are both taken as the medial and lateral clearance values of this measurement. Otherwise, take the medial and lateral clearance values of the previous measurement.

[0129] Taking the flexion angle of 60° as an example for illustration: Suppose in the first measurement, the medial clearance is 16 mm and the lateral clearance is 18 mm when flexed at 60°. In the second measurement, the medial and lateral clearances when flexed at 60° can be divided into three cases. As shown in the following table:

[0130] Table 4 Measurement of medial and lateral clearance values

[0131]

[0132] To make the measured motion curve smoother, in the present invention, a geometric smoothing filtering signal processing technology on the SE(3) Lie group is also applied, mainly including:

[0133] 1. Representation and estimation method of smooth curves on the SE(3) Lie group

[0134] ① Reasons for using filtering:

[0135] When using the full-cycle joint motion measurement method, ideally, the joint motion would be represented as a curve T of the relative joint pose. However, in reality, the laser tracker records a series of values of T1, T2, T3, T4,..., T. n Although the movement of the human leg is relatively smooth, during the actual measurement process, the data will carry a lot of noise, resulting in the difficulty of overlapping the multiple collected data of T. k Therefore, in the present invention, a method based on fitting smooth curves on the SE(3) Lie group will be used to achieve geometric smoothing filtering on the SE(3) Lie group.

[0136] ② Smooth filtering estimation method on the SE(3) Lie group:

[0137] Assume that the parameter of the curve is p, and the motion curve T is regarded as a function of the flexion angle T p (u), where u is the flexion angle. For the sampling points T1, T2, …, T n , the corresponding flexion angles are u1, u2, …, u n . After optimization using formula [2], the sum of the squared errors between the curve and each sampling point is minimized.

[0138] Formula [2]:

[0139] In the system, a cubic spline curve is used for T p (u). In the range from the minimum value to the maximum value, 10 - 20 points are taken as the control points for the connection line. Since T is non-linear, the spline interpolation is calculated using the more complex formula [3].

[0140] Formula [3]: Among them, are the control points of the spline function with respect to u (j = 0, 1, 2, 3), Log(T) is the form of the Lie group T transformed into the Lie algebra, and Exp(τ) is the form of the Lie algebra τ transformed into the Lie group, and

[0141]

[0142] In actual operation, the solution of formula [2] is not an analytical solution, but an estimated value obtained using the Gauss-Newton iteration method. First, a set of initial values p of the control points i of T i with respect to u are given Then, a regression model T i - T p (u i ) = ∈ i is approximated by formula [4]: Among them is the value of the Jacobian matrix of T p (u i ) with respect to at , that is:

[0143]

[0144] Then, formula [4] can be used for iterative calculation to obtain Thus, there is

[0145] The flow chart of the multi-sampling curve update strategy is as shown in Figure 13 and mainly includes:

[0146] ① Calculate the optimal curve:

[0147] After one sampling, there are T1, T2, T3, …, T n . According to formula [2], calculate the optimal curve T p (u).

[0148] ② Method for judging the update of the curve by multiple samplings:

[0149] When new sampling values T′1, T′2, …, T′ n arrive, calculate T p (u1), T p (u2), …, T p (u n ) according to the corresponding positions. And according to the variable load repeated measurement method, judge whether the updated T′ k should be added.

[0150] Suppose there are m points to be added, and their buckling angles are u′1, u′2, …, u′ m . Sample w points on average from the original T1, T2, …, T n . Use these w points and the newly added m points as sampling points. Re-estimate T p (u) using formula [2].

[0151] According to a specific implementation manner of an embodiment of the present disclosure, the method for processing data on the relevant movement trajectories of the knee joint by selecting at least one of a variety of built-in motion estimation evaluation algorithms to form a gap curve for evaluating the movement of the knee joint includes:

[0152] Swing the tibia while keeping the femur fixed so as to record the attitude positions of the tibia and the femur in the three-dimensional space by a laser tracker;

[0153] Perform data operations on the attitude positions of the tibia and the femur in the three-dimensional space to obtain the relative position T between the femur and the tibia;

[0154] Based on the distribution of the relative position with respect to the buckling angle, form a gap curve for evaluating the movement of the knee joint. According to a specific implementation manner of an embodiment of the present disclosure, after forming the gap curve for evaluating the movement of the knee joint based on the distribution of the relative position with respect to the buckling angle, the method further includes:

[0155] Extract data values from the gap curve to obtain the medial and lateral gap values between the femur and the tibia;

[0156] Based on the medial and lateral gap values, perform a gap evaluation on the knee joint.

[0157] According to a specific implementation manner of an embodiment of the present disclosure, after forming a gap curve for evaluating the movement of the knee joint based on the distribution of the relative position with respect to the flexion angle, the method further includes:

[0158] Extract data values for the gap curve to obtain the force line alignment between the femur and the tibia, where the force line alignment includes the flexion angle, varus / valgus angle, and internal / external rotation angle related to the knee joint;

[0159] Based on the offline alignment, perform a force line evaluation on the knee joint, and the gap evaluation and the force line evaluation together constitute the full-cycle evaluation of the knee joint.

[0160] According to a specific implementation manner of an embodiment of the present disclosure, the forming of a gap curve for evaluating the movement of the knee joint by selecting at least one of multiple built-in motion estimation evaluation algorithms for data processing of the relevant movement trajectory of the knee joint includes:

[0161] By applying an external force to the knee joint and recording the different relative position relationships between the femur and the tibia of the knee joint at the same flexion angle, evaluate the movement of the knee joint at different positions at the same flexion angle.

[0162] According to a specific implementation manner of an embodiment of the present disclosure, the recording of the different relative position relationships between the femur and the tibia of the knee joint at the same flexion angle includes:

[0163] Adopt a repeated measurement method, compare the medial and lateral gap values at the same flexion angle in multiple collected data, screen out the gap values that meet the requirements, and finally fit the medial and lateral gap values at all flexion angles into an optimal gap curve.

[0164] According to a specific implementation manner of an embodiment of the present disclosure, the forming of a gap curve for evaluating the movement of the knee joint by selecting at least one of multiple built-in motion estimation evaluation algorithms for data processing of the relevant movement trajectory of the knee joint includes:

[0165] Take a series of relative positions T between the femur and the tibia to form a data sequence;

[0166] Use a preset formula to perform geometric smoothing filtering signal processing on the data sequence on the SE(3) Lie group to obtain a filtered curve T p (u);

[0167] Monitor the newly obtained sampling values to determine whether new sampling values are added;

[0168] If so, update the number of sampling points, and use the updated number of sampling points. Here, use the preset formula to perform filtering processing on the data sequence with the newly added sampling points.

[0169] According to a specific implementation manner of the embodiments of the present disclosure, using the preset formula to perform geometric smoothing filtering signal processing on the data sequence on the SE(3) Lie group to obtain the filtering curve T p (u) includes:

[0170] Set the parameter of the filtering curve T p (u) to p, and regard the motion curve T as a function T p (u) of the flexion angle, where u is the flexion angle. For the sampling points T1, T2, …, T n , the corresponding flexion angles are u1, u2, …, u n , and use the formula

[0171]

[0172] After optimization, minimize the sum of the squared errors between the curve and each sampling point.

[0173] According to a specific implementation manner of the embodiments of the present disclosure, the method further includes:

[0174] Use a cubic spline curve for T p (u). In the range from the minimum value to the maximum value, take 10 - 20 points on the connecting line as control points, and calculate the spline interpolation using the formula for calculation,

[0175] where, is the control point (j = 0, 1, 2, 3) relative to u in the spline function, Log(T) is the form of converting the Lie group T into the Lie algebra, and Exp(τ) is the form of converting the Lie algebra τ into the Lie group, and

[0176]

[0177] Corresponding to the above method embodiments, an intraoperative knee joint motion evaluation system provided by the embodiments of the present application includes:

[0178] A memory, in which a three-dimensional model of the leg bone related to the knee joint obtained in advance is stored;

[0179] Laser targets, which are respectively on the femoral side and the tibial side of the knee joint, so as to describe the movement trajectories of the femur and the tibia through the laser targets;

[0180] A laser tracker, based on a preset femoral coordinate system and tibial coordinate system, reads the relative positions of laser targets on the femoral side and tibial side by using the laser tracker to form the relative movement trajectories of the femur and tibia.

[0181] A calculation module processes data on the relevant movement trajectories of the knee joint by selecting at least one of a variety of built-in motion estimation and evaluation algorithms, and forms a clearance curve for evaluating the movement of the knee joint. The motion estimation and evaluation algorithms include at least one of a full-cycle measurement method, a variable-load measurement method, and a geometric smoothing filtering signal processing method on the SE(3) Lie group, so as to implement the intraoperative knee joint movement evaluation method described in the foregoing method embodiments.

[0182] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0183] The units involved in the embodiments of the present disclosure can be implemented in software or in hardware. Among them, the name of the unit does not constitute a limitation on the unit itself in some cases. For example, the first acquisition unit can also be described as "the unit for acquiring at least two Internet protocol addresses".

[0184] It should be understood that the various parts of the present disclosure can be implemented by hardware, software, firmware, or a combination thereof.

[0185] The above is only the specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present disclosure should be covered by the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. An intraoperative knee joint movement evaluation system, characterized in that, Comprising: A memory in which a pre-acquired three-dimensional model of leg bones related to the knee joint is stored; Laser targets, which are respectively on the femoral side and the tibial side of the knee joint, so as to describe the movement trajectories of the femur and the tibia through the laser targets; A laser tracker, based on a pre-set femoral coordinate system and tibial coordinate system, uses the laser tracker to read the relative positions of the laser targets on the femoral side and the tibial side, and forms the relative movement trajectories of the femur and the tibia; A calculation module, by selecting at least one of a variety of built-in motion estimation and evaluation algorithms, processes the data of the relevant movement trajectories of the knee joint to form a gap curve for evaluating the movement of the knee joint. The motion estimation and evaluation algorithms include at least one of a full-cycle measurement method, a variable-load measurement method, and a geometric smoothing filtering signal processing method on the SE(3) Lie group, so as to perform the following method: Pre-acquire a three-dimensional model of leg bones related to the knee joint; Respectively set at least one laser target on the femoral side and the tibial side of the knee joint, so as to describe the movement trajectories of the femur and the tibia through the laser targets; Based on a pre-set femoral coordinate system and tibial coordinate system, use the laser tracker to read the relative positions of the laser targets on the femoral side and the tibial side, and form the relative movement trajectories of the femur and the tibia; By selecting at least one of a variety of built-in motion estimation and evaluation algorithms, process the data of the relevant movement trajectories of the knee joint to form a gap curve for evaluating the movement of the knee joint. The motion estimation and evaluation algorithms include at least one of a full-cycle measurement method, a variable-load measurement method, and a geometric smoothing filtering signal processing method on the SE(3) Lie group.

2. The system according to claim 1, wherein The step of, by selecting at least one of a variety of built-in motion estimation and evaluation algorithms, processing the data of the relevant movement trajectories of the knee joint to form a gap curve for evaluating the movement of the knee joint, includes: Swing the tibia while keeping the femur fixed, so as to record the attitude positions of the tibia and the femur in three-dimensional space through the laser tracker; Perform data operations on the attitude positions of the tibia and the femur in three-dimensional space to obtain the relative position T between the femur and the tibia; Based on the distribution of the relative position with respect to the flexion angle, form a gap curve for evaluating the movement of the knee joint.

3. The system according to claim 2, characterized in that, After forming the gap curve for evaluating the movement of the knee joint based on the distribution of the relative position with respect to the flexion angle, the method further includes: Extract data values from the gap curve to obtain the medial and lateral gap values between the femur and the tibia; Based on the medial and lateral gap values, perform a gap evaluation on the knee joint.

4. The system according to claim 3, wherein After forming the gap curve for evaluating the movement of the knee joint based on the distribution of the relative position with respect to the flexion angle, the method further includes: Extract data values from the gap curve to obtain the alignment of the force lines between the femur and the tibia. The alignment of the force lines includes the flexion angle, the varus / valgus angle, and the internal / external rotation angle related to the knee joint; Based on the alignment of the force lines, perform a force line evaluation on the knee joint. The gap evaluation and the force line evaluation together constitute the full-cycle evaluation of the knee joint.

5. The system according to claim 1, characterized in that, Processing data of relevant movement trajectories of the knee joint by selecting at least one of a variety of built-in motion estimation evaluation algorithms to form a gap curve for evaluating the movement of the knee joint, including: Evaluating the movement of the knee joint at different positions at the same flexion angle by applying an external force to the knee joint and recording different relative position relationships between the femur and tibia of the knee joint at the same flexion angle.

6. The system according to claim 5, wherein The recording of different relative position relationships between the femur and tibia of the knee joint at the same flexion angle includes: Adopting a repeated measurement method, comparing the medial and lateral gap values at the same flexion angle in multiple sets of collected data, screening out the gap values that meet the requirements, and finally fitting the medial and lateral gap values at all flexion angles into an optimal gap curve.

7. The system according to claim 1, wherein Processing data of relevant movement trajectories of the knee joint by selecting at least one of a variety of built-in motion estimation evaluation algorithms to form a gap curve for evaluating the movement of the knee joint, including: Taking a series of steps to obtain the relative position T between the femur and tibia to form a data sequence; Using a preset formula to perform geometric smoothing filtering signal processing on the data sequence on the SE(3) Lie group to obtain a filtered curve Tp(u); Monitoring newly obtained sampling values to determine whether new sampling values are added; If so, updating the number of sampling points and using the updated number of sampling points to perform filtering processing on the data sequence with the newly added sampling points using the preset formula again.

8. The system according to claim 7, characterized in that, Using a preset formula to perform geometric smoothing filtering signal processing on the data sequence on the SE(3) Lie group to obtain a filtered curve Tp(u) includes: Set the filtering curve T p The parameter of (u) is p, and the motion curve T is regarded as a function T p (u), where u is the buckling angle. For the sampling points T1, T2, …, T n , the corresponding buckling angles are u1, u2, …, u n . Using the formula After optimization, the sum of the squared errors between the curve and each sampling point is minimized.

9. The system according to claim 8, wherein The method further includes: Use a cubic spline curve for Tp(u). In the range from the minimum value to the maximum value, take 10 - 20 points on the connecting line as control points, and calculate the spline interpolation using the formula where are the control points relative to u in the spline function (j = 0, 1, 2, 3), Log(T) is the form of converting the Lie group T into the Lie algebra, and Exp(τ) is the form of converting the Lie algebra τ into the Lie group, and , , .

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