Method and system for reconstructing cartilage surface and electronic equipment

Abstract

The invention provides a method and system for reconstructing a cartilage surface and electronic equipment, and the method comprises the steps: obtaining an outer contour model of a bone cortex and a mark point set of the cartilage surface, and converting the outer contour model of the bone cortex and the mark point set of the cartilage surface into a preset image coordinate system; obtaining a projection point of each mark point in the mark point set on the cartilage surface on the outer contour of the bone cortex, and determining a coverage area of the projection points; performing elastic stretching on the coverage area of the projection point, and further simulating the wear degree of the cartilage surface to obtain a reconstructed cartilage surface; the problem that muscle or ligament abrasion is caused due to the fact that the influence of cartilage is not brought into prosthesis planning in an existing joint replacement operation is solved, practical and effective guidance can be provided during prosthesis planning and adjusting, prosthesis planning and adjusting are better facilitated, and the prosthesis planning and adjusting efficiency is improved. The intelligent degree, usability and reliability of prosthesis planning in joint replacement are improved to a great extent.

Description

Technical Field

[0001] The present invention relates to the technical field of cartilage surface reconstruction, and in particular to a method and system for cartilage surface reconstruction, and an electronic device. Background Art

[0002] Nowadays, the technology of replacing artificial joint prostheses is relatively mature and has gradually brought good news to more and more people. However, in early joint replacement surgeries, especially early traditional surgeries, the influence of bone surface cartilage on prosthesis placement has not been taken into account due to the influence of surgical accuracy at the time. Nowadays, with the rapid development of orthopedic surgical robots, joint replacement is becoming increasingly automated and repeatable, especially the accuracy has been greatly improved. For example, the existing knee replacement auxiliary system can achieve a surgical accuracy of about 1mm. In comparison, the influence of cartilage with a maximum thickness of ~3mm on prosthesis planning during surgery becomes intuitive and important.

[0003] In joint replacement surgery, prosthesis planning is based on the bone surface extracted from medical images. The general medical image modality selects X-ray tomography, which has a clearer image of high-density tissue (bone), and extracts the bone surface through the image. However, cartilage is difficult to distinguish in X-ray imaging due to its low density and similarity to muscle. At present, most joint replacement surgeries do not incorporate the impact of cartilage into prosthesis planning, which can easily cause a "step" to form at the contact point between the prosthesis and the real bone surface with cartilage, causing muscle or ligament wear and tear, causing a bad postoperative experience for the patient. In severe cases, a second surgery may be required for adjustment. Therefore, in existing technologies, the identification of cartilage surfaces is still a problem to be solved.

[0004] Therefore, the prior art needs to be further developed. Summary of the invention

[0005] The purpose of the present invention is to overcome the above technical deficiencies and provide a method and system for reconstructing cartilage surface, and an electronic device to solve the problems existing in the prior art.

[0006] To achieve the above technical objectives, according to a first aspect of the present invention, the present invention provides a method for reconstructing a cartilage surface, comprising:

[0007] S100, obtaining an outer contour model of the bone cortex and a set of marked points on the cartilage surface, and transforming the outer contour model of the bone cortex and the set of marked points on the cartilage surface into a preset image coordinate system;

[0008] S200, obtaining projection points of each marking point in the marking point set on the cartilage surface on the outer contour of the bone cortex, and determining the coverage area of ​​the projection points;

[0009] S300, elastically stretching the coverage area of ​​the projection point to simulate the wear degree of the cartilage surface and obtain a reconstructed cartilage surface.

[0010] Specifically, the step of obtaining the projection points of each marking point in the marking point set on the cartilage surface on the outer contour of the bone cortex includes:

[0011] By calling the FindClosestPoint function in the VTK library, the points on the cartilage surface that are closest to the outer contour of the bone cortex are found in turn, and the points on the cartilage surface that are closest to the outer contour of the bone cortex are used as the projection points of each marking point.

[0012] Specifically, determining the coverage area of ​​the projection point includes:

[0013] Obtaining coordinates of all projection points in a preset image coordinate system, and generating a first coverage area of ​​each projection point according to the coordinates;

[0014] According to the distribution of the projection points, the second coverage area of ​​the projection points is further screened.

[0015] Specifically, generating a first coverage area for each projection point according to the coordinates includes:

[0016] The first coordinate of each projection point in the preset image coordinate system is obtained, and then the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the first coordinate of each projection point are respectively obtained, and the maximum and minimum values ​​of the X-axis coordinate are obtained, which are recorded as Xmax and Xmin respectively. Similarly, the maximum and minimum values ​​of the Y-axis coordinate are obtained, which are recorded as Ymax and Ymin respectively, and the maximum and minimum values ​​of the Z-axis coordinate are obtained, which are recorded as Zmax and Zmin. The first coverage area of ​​each projection point is generated according to Xmax and Xmin, Ymax and Ymin, and Zmax and Zmin.

[0017] Specifically, generating the first coverage area of ​​each projection point according to the Xmax and Xmin, Ymax and Ymin, Zmax and Zmin includes:

[0018] The maximum value Xmax of the X-axis coordinate, the maximum value Ymax of the Y-axis coordinate, and the maximum value Zmax of the Z-axis coordinate are respectively increased by the first preset value extend, and are recorded as Xmax+extend, Ymax+extend, and Zmax+extend respectively;

[0019] The minimum value Xmin of the X-axis coordinate, the minimum value Xmin of the Y-axis coordinate, and the minimum value Xmin of the Z-axis coordinate are respectively reduced by the first preset value extend, and are respectively recorded as Xmin-extend, Ymin-extend, and Zmin-extend;

[0020] Take (Xmin-extend, Xmin+extend, Ymin-extend, Ymin+extend, Zmin-extend, Zmin+extend) as the first boundary coordinates of the projection point, and generate the first coverage area of ​​each projection point according to the first boundary coordinates.

[0021] Specifically, further screening the second coverage area of ​​the projection points according to the distribution of the projection points includes:

[0022] The first coordinates of each projection point on the three coordinate axes of the preset image coordinate system are respectively obtained, the correlation between the projection points is calculated according to the first coordinates, and a covariance matrix of each projection point is formed, and the eigenvector corresponding to the maximum eigenvalue of each projection point is calculated according to the covariance matrix to form a vector matrix, and the vector matrix is ​​used to transform the original coordinates of each projection point to obtain the second coordinates of each projection point, and a second coverage area is generated according to the second coordinates.

[0023] Specifically, generating the second coverage area according to the second coordinates includes:

[0024] Select the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the second coordinate of each projection point in the second coordinate respectively, and obtain the maximum and minimum values ​​of the X-axis coordinate in the second coordinate, which are recorded as X1max and X1min respectively. Similarly, obtain the maximum and minimum values ​​of the Y-axis coordinate in the second coordinate, which are recorded as Y1max and Y1min respectively, and obtain the maximum and minimum values ​​of the Z-axis coordinate in the second coordinate, which are recorded as Z1max and Z1min;

[0025] Take (X1min, X1max, Y1min, Y1max, Z1min, Z1max) as the second boundary coordinates of the projection point, and generate the second coverage area of ​​each projection point according to the second boundary coordinates.

[0026] Specifically, the S300 includes:

[0027] The shape of the outer contour of the bone cortex is used to approximate the shape of the cartilage surface. That is, the second coverage area of ​​each projection point is elastically stretched in the direction of the marked points on the cartilage surface. That is, the second coverage area is raised according to the distance distribution between the marked point set on the cartilage surface and the second coverage area, thereby simulating the wear degree of the cartilage surface and obtaining the reconstructed cartilage surface.

[0028] Specifically, the second covering area is raised according to the distance distribution between the marked point set on the cartilage surface and the second covering area, thereby simulating the wear degree of the cartilage surface to obtain the reconstructed cartilage surface, including:

[0029] The marked point set on the cartilage surface is taken as the center of the Gaussian model, and the point set in the second coverage area is regarded as generated by the Gaussian model. A probability density function is constructed according to the relationship between the marked point set and the point set in the second coverage area. The projection points that need to be raised are calculated and raised, thereby simulating the degree of wear on the cartilage surface and obtaining the reconstructed cartilage surface.

[0030] According to a second aspect of the present invention, there is provided a system for reconstructing a cartilage surface, comprising:

[0031] Acquisition module: used to acquire the outer contour model of the bone cortex and the set of marking points on the cartilage surface; or used to acquire the projection points of each marking point in the set of marking points on the cartilage surface on the outer contour of the bone cortex;

[0032] Control module: used to transform the outer contour model of the bone cortex and the set of marking points on the cartilage surface into a preset image coordinate system; or used to determine the coverage area of ​​the projection points; or used to elastically stretch the coverage area of ​​the projection points to simulate the degree of wear on the cartilage surface and obtain a reconstructed cartilage surface.

[0033] According to a third aspect of the present invention, there is provided an electronic device, comprising: a memory; and a processor, wherein the memory stores computer-readable instructions, and the computer-readable instructions, when executed by the processor, implement the above-mentioned method for reconstructing a cartilage surface.

[0034] Beneficial effects:

[0035] The present invention obtains the outer contour model of the cortical bone and the set of marking points on the cartilage surface, and transforms the outer contour model of the cortical bone and the set of marking points on the cartilage surface into a preset image coordinate system; obtains the projection points of each marking point on the cartilage surface on the outer contour of the cortical bone, and determines the coverage area of ​​the projection points; elastically stretches the coverage area of ​​the projection points to simulate the degree of wear on the cartilage surface to obtain a reconstructed cartilage surface. The present invention can support the existing imaging modality. During surgery, by collecting marking points on the cartilage surface, the cartilage surface is simulated and generated in key areas, with arbitrary positions and without restrictions, thereby providing effective guidance to doctors when planning and adjusting the prosthesis. The cortical bone surface is used to approximately replace the cartilage surface, and the actual cartilage height of each area is given according to the actual wear conditions, which is more conducive to prosthesis planning and adjustment, and solves the problem that the current joint replacement surgery does not incorporate the influence of cartilage into prosthesis planning, thereby causing wear of muscles or ligaments, and greatly improves the intelligence, usability and reliability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of a method for reconstructing a cartilage surface in a specific embodiment of the present invention;

[0037] Figure 2 is a schematic diagram of the composition of a system for reconstructing a cartilage surface provided in a specific embodiment of the present invention;

[0038] Figure 3 is a flow chart of an algorithm for generating a simulated cartilage surface provided in a specific embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of the relative positions of the marking points on the cartilage surface and the bone cortex model provided in a specific embodiment of the present invention;

[0040] Figure 5 is a schematic diagram of obtaining projection points of cartilage surface marker points on the outer contour of bone cortex provided in a specific embodiment of the present invention;

[0041] Figure 6 It is a schematic diagram of the positions of the cartilage surface marking points and the projection points on the outer contour of the bone cortex provided in a specific embodiment of the present invention;

[0042] Figure 7 is a cropped point cloud image of the coverage area of ​​the projection points provided in a specific embodiment of the present invention;

[0043] Figure 8 The cropped point cloud image of the effective range of the coverage area of ​​the projection points obtained by screening provided in the specific implementation manner of the present invention;

[0044] Fig. 9 It is a schematic diagram of the relative positions of the coverage area of ​​the projection points and the marking points on the cartilage surface provided in a specific embodiment of the present invention.

[0045] Fig.10 It is a schematic diagram of the cartilage surface obtained after elastic stretching of the coverage area of ​​the projection point provided in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by ordinary technicians in this field without making creative work should all fall within the scope of protection of this application. In addition, the directional words mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only reference to the directions of the accompanying drawings. Therefore, the directional words used are used to illustrate rather than limit the invention.

[0047] The present invention will be further described below in conjunction with the accompanying drawings and preferred embodiments.

[0048] See also Figure 1This embodiment provides a method for reconstructing a cartilage surface, comprising:

[0049] S100, obtaining an outer contour model of the bone cortex and a set of marked points on the cartilage surface, and transforming the outer contour model of the bone cortex and the set of marked points on the cartilage surface into a preset image coordinate system;

[0050] It is understandable that preoperative planning is performed based on medical images, and the preoperative prosthesis planning is brought into the intraoperative patient reference system through intraoperative registration, thereby guiding the robotic arm to perform osteotomy / bone grinding operations. Generally, the imaging mode used for preoperative examination of joints is X-ray tomography (CT), a relatively high-quality form of bone imaging. Its disadvantage is that cartilage is almost inseparable from muscle due to its low density. When performing three-dimensional reconstruction of the bone model, it is impossible to include cartilage, and only the outer contour of the bone cortex can be obtained. Therefore, intraoperative registration realizes the registration of the bone cortex in the image and the patient's own reference system. After the registration of the bone cortex in the image and the patient's reference system is achieved, the marked points on the cartilage surface can be collected during the operation as needed, and directly transformed to the image bone model reference system through the registration matrix, and then the cartilage surface reconstruction operation of the marked point area can be performed.

[0051] Furthermore, the bone cortex can be scanned by X-ray tomography to obtain an outer contour model of the bone cortex, and the marker points on the cartilage surface can be collected using a probe to form a marker point set; and the respective marker points in the cartilage surface marker point set can be transformed into the image bone model reference system using a registration matrix.

[0052] S200, obtaining projection points of each marking point in the marking point set on the cartilage surface on the outer contour of the bone cortex, and determining the coverage area of ​​the projection points.

[0053] Specifically, the step of obtaining the projection points of each marking point in the marking point set on the cartilage surface on the outer contour of the bone cortex includes:

[0054] By calling the FindClosestPoint function in the VTK library, the points on the cartilage surface that are closest to the outer contour of the bone cortex are found in turn, and the points on the cartilage surface that are closest to the outer contour of the bone cortex are used as the projection points of each marking point.

[0055] It should be noted here that the FindClosestPoint function is a function in the VTK (Visualization Toolkit) library used to find the nearest point to a given query point in three-dimensional space. This function is usually used with spatial locators such as vtkCellLocator or vtkKdTree to quickly find the nearest point in a complex geometric data set. The function of this function is to establish a mapping relationship between the outer contour of the bone cortex and the marked points on the cartilage surface. It can accurately find the nearest point to each marked point on the cartilage surface in the data set of the outer contour of the bone cortex.

[0056] See also Figure 5 , FindClosestPoint is not simply looking for the vertices that make up the mesh, but finding the nearest point on the mesh surface to simplify the mesh into a unit, such as Figure 5 As shown, to find the nearest neighbor point of point P to the grid composed of ABCD, a perpendicular line can be drawn through point P to the plane composed of ABCD, where the foot of the perpendicular is the nearest neighbor point of P on the grid. This method can effectively avoid the sparse number of vertices caused by the excessive smoothness of the bone cortical surface during the meshing process, resulting in a large difference between the nearest neighbor point and the nearest neighbor vertex. In such a grid with sparse vertices, if the method of finding vertices as the nearest neighbor point is adopted, due to the limited number of vertices and uneven distribution, the nearest neighbor vertex found may be quite different from the point closest to the actual collection point (which may be located at a non-vertex position on the grid surface). For example, the collection point may be close to the middle of an edge of the mesh surface, while the nearest vertex may be relatively far away from the collection point. In this way, it is impossible to accurately find the point closest to the collection point in a real sense. The search through the FindClosestPoint function is based on geometric relationships, that is, the vertical distance from the point to the surface is the shortest. It can more accurately find the point closest to point P on the mesh surface. Compared with finding the nearest neighbor vertex, it is more in line with the need to find the actual nearest point, avoiding errors caused by sparse vertices, thereby effectively avoiding the problem of large differences between the nearest neighbor point and the nearest neighbor vertex.

[0057] Specifically, determining the coverage area of ​​the projection point includes:

[0058] Obtaining coordinates of all projection points in a preset image coordinate system, and generating a first coverage area of ​​each projection point according to the coordinates;

[0059] According to the distribution of the projection points, the second coverage area of ​​the projection points is further screened.

[0060] It can be understood that the first coverage area generated first preliminarily defines the influence range of each projection point, provides a basic framework for subsequent further analysis and processing, and can preliminarily determine the area that may be involved around each marking point. Further screening the second coverage area of ​​the projection points according to the distribution of the projection points is a process of optimizing the first coverage area. Further optimizing the first coverage area makes the second coverage area closer to the distribution of each projection point.

[0061] Specifically, generating a first coverage area for each projection point according to the coordinates includes:

[0062] The first coordinate of each projection point in the preset image coordinate system is obtained, and then the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the first coordinate of each projection point are respectively obtained, and the maximum and minimum values ​​of the X-axis coordinate are obtained, which are recorded as Xmax and Xmin respectively. Similarly, the maximum and minimum values ​​of the Y-axis coordinate are obtained, which are recorded as Ymax and Ymin respectively, and the maximum and minimum values ​​of the Z-axis coordinate are obtained, which are recorded as Zmax and Zmin. The first coverage area of ​​each projection point is generated according to Xmax and Xmin, Ymax and Ymin, and Zmax and Zmin.

[0063] Specifically, generating the first coverage area of ​​each projection point according to the Xmax and Xmin, Ymax and Ymin, Zmax and Zmin includes:

[0064] The maximum value Xmax of the X-axis coordinate, the maximum value Ymax of the Y-axis coordinate, and the maximum value Zmax of the Z-axis coordinate are respectively increased by the first preset value extend, and are recorded as Xmax+extend, Ymax+extend, and Zmax+extend respectively;

[0065] The minimum value Xmin of the X-axis coordinate, the minimum value Xmin of the Y-axis coordinate, and the minimum value Xmin of the Z-axis coordinate are respectively reduced by the first preset value extend, and are respectively recorded as Xmin-extend, Ymin-extend, and Zmin-extend;

[0066] Take (Xmin-extend, Xmin+extend, Ymin-extend, Ymin+extend, Zmin-extend, Zmin+extend) as the first boundary coordinates of the projection point, and generate the first coverage area of ​​each projection point according to the first boundary coordinates.

[0067] It should be further explained that in order to ensure the effectiveness of the edge of the first coverage area as much as possible, in actual cutting, the boundary will be extended to a certain length in each direction, that is, the first preset value extend is increased. This length can be adjusted according to actual needs. The present invention does not impose further restrictions on the specific numerical value of the first preset value, as long as it can adapt to the method of simulating cartilage surface described in the present invention.

[0068] Specifically, further screening the second coverage area of ​​the projection points according to the distribution of the projection points includes:

[0069] The first coordinates of each projection point on the three coordinate axes of the preset image coordinate system are respectively obtained, the correlation between the projection points is calculated according to the first coordinates, and a covariance matrix of each projection point is formed, and the eigenvector corresponding to the maximum eigenvalue of each projection point is calculated according to the covariance matrix to form a vector matrix, and the vector matrix is ​​used to transform the original coordinates of each projection point to obtain the second coordinates of each projection point, and a second coverage area is generated according to the second coordinates.

[0070] Furthermore, since elastic stretching is subsequently used to reconstruct the cartilage surface, directly using the first coverage area as the object of elastic stretching will result in a portion far away from the projection point, thereby affecting the reconstruction result of the cartilage surface. In order to effectively exclude irrelevant areas, if the shape of a cube, i.e., the first coverage area, is directly used for cropping, it will bring potential risks to the subsequent algorithm. Therefore, based on the above steps, the present invention further screens the effective range according to the distribution status of the projection points, and removes the portion far away from the projection point. The specific screening steps are as follows:

[0071] (1) respectively obtain the first coordinates of the projection points, that is, the coordinate values ​​of the X-axis, Y-axis, and Z-axis, calculate the correlation between them, and form a covariance matrix A;

[0072] cov(X i |X j )=E[(X i -μ i )(X j -μ j )], i, j = x, y, z;

[0073]

[0074] Among them, cov(X i |X j ) refers to the calculation of X i and X j The covariance of X i , X j Respectively represent the coordinate values ​​of the projection point on the X-axis, Y-axis or Z-axis, μ i , μj Respectively represents the average value of the X-axis, Y-axis or Z-axis coordinate value of the projection point.

[0075] (2) Solve the eigenvalues ​​and eigenvectors of the covariance matrix A, where the eigenvector describes the characteristics of the projection point distribution. For example, the eigenvector corresponding to the maximum eigenvalue corresponds to the direction with the longest distance of the projection point distribution. The eigenvector describes the transformation relationship between the reference system constructed based on the distribution characteristics of the projection point set and the original reference system. The matrix composed of the eigenvectors corresponding to the maximum eigenvalues ​​of each projection point is denoted as the vector matrix M.

[0076] (3) All projection points are rotated with the geometric center as the origin, that is, the vector matrix M is directly applied. That is, through matrix multiplication, the projection points in the original coordinates are converted to the new reference system, so that the projection points are "corrected" along the direction of their longest distribution distance, that is, the part far away from the projection point is removed, so that the projection points have a more regular distribution in the new reference system, which is convenient for subsequent processing.

[0077] Specifically, generating the second coverage area according to the second coordinates includes:

[0078] Select the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the second coordinate of each projection point in the second coordinate respectively, and obtain the maximum and minimum values ​​of the X-axis coordinate in the second coordinate, which are recorded as X1max and X1min respectively. Similarly, obtain the maximum and minimum values ​​of the Y-axis coordinate in the second coordinate, which are recorded as Y1max and Y1min respectively, and obtain the maximum and minimum values ​​of the Z-axis coordinate in the second coordinate, which are recorded as Z1max and Z1min;

[0079] Take (X1min, X1max, Y1min, Y1max, Z1min, Z1max) as the second boundary coordinates of the projection point, and generate the second coverage area of ​​each projection point according to the second boundary coordinates.

[0080] Furthermore, according to the coordinates of the projection points converted to the new reference system, the area far away from the projection points is removed to generate a second coverage area that is more consistent with the distribution of the projection points.

[0081] S300, elastically stretching the coverage area of ​​the projection point to simulate the wear degree of the cartilage surface and obtain a reconstructed cartilage surface.

[0082] Specifically, the S300 includes:

[0083] The shape of the outer contour of the bone cortex is used to approximate the shape of the cartilage surface. That is, the second coverage area of ​​each projection point is elastically stretched in the direction of the marked points on the cartilage surface. That is, the second coverage area is raised according to the distance distribution between the marked point set on the cartilage surface and the second coverage area, thereby simulating the wear degree of the cartilage surface and obtaining the reconstructed cartilage surface.

[0084] Furthermore, in order to simulate the different wear of cartilage in different positions as much as possible, the present invention obtains the marking points on the cartilage surface and replaces the shape of the cartilage surface with the outer contour shape of the bone cortex covered by it, and performs elastic stretching operations to better fit the actual shape of the cartilage surface. This optimized approximation can more accurately reflect the morphological characteristics of the cartilage surface and provide a more reliable basis for subsequent analysis.

[0085] Specifically, the second covering area is raised according to the distance distribution between the marked point set on the cartilage surface and the second covering area, thereby simulating the wear degree of the cartilage surface to obtain the reconstructed cartilage surface, including:

[0086] The marked point set on the cartilage surface is taken as the center of the Gaussian model, and the point set in the second coverage area is regarded as generated by the Gaussian model. A probability density function is constructed according to the relationship between the marked point set and the point set in the second coverage area. The projection points that need to be raised are calculated and raised, thereby simulating the degree of wear on the cartilage surface and obtaining the reconstructed cartilage surface.

[0087] See also Figure 3-Figure 10 The working principle of the present invention is described below by using specific examples:

[0088] Step 1: Use the probe to collect a certain number of markers on the surface of the cartilage. In this example, six markers are selected. The specific number can be adjusted according to business needs, and the registration matrix is ​​used to transform them to the image group coordinate system. The prerequisite for this solution is to complete the registration of the image and the patient reference system. Therefore, in the following discussion, unless otherwise specified, the default transformation to the image coordinate system is used;

[0089] Among them, the collected cartilage surface markers and bone cortical models are as follows: Figure 4 As shown in the figure, the red star-shaped points represent the marking points on the cartilage surface, and the blue cylinder represents the cortical bone model. This example mainly describes the projection of the red star-shaped points on the outer contour of the CT segmented cortical bone model. Figure 4 The area of ​​the red line segment is the point where the marking points of the cartilage surface are cut out of the corresponding encirclement on the segmented surface. Then, the point cloud in the red line segment area is stretched with the red star point as the target to reconstruct the cartilage surface.

[0090] Step 2: In order to intercept the area covered by the marked point on the outer contour of the bone cortex, first project it onto the outer contour of the bone cortex model to confirm the coverage area of ​​the projection point. The specific steps are as follows:

[0091] (1) By calling the FindClosestPoint function in VTK, find the point closest to the marked point on the outer contour of the cortical bone model;

[0092] FindClosestPoint does not simply find the vertices that make up the mesh, but finds any point on the mesh surface that is closest to it. Figure 5 As shown, to find the nearest neighbor point of point P to the grid, a perpendicular line can be drawn from point P to the plane where ABCD is located, where the foot of the perpendicular is the closest neighbor point of P on the grid. This method can effectively avoid the problem that the number of vertices on the bone cortical surface is too sparse due to excessive smoothness during the meshing process, resulting in a large difference between the nearest neighbor point and the nearest neighbor vertex;

[0093] (2) Figure 6 As shown in the figure, the blue points are the marker points suspended above the bone cortex and on the surface of the cartilage, and the white points are the nearest neighbor points found on the outer contour of the bone cortex using the FindClosestPoint function, that is, the projection points, and the coverage area of ​​the projection points (white points) on the outer contour of the bone cortex is determined. The specific process of the coverage area size is as follows:

[0094] According to the three-dimensional coordinates of the six projection points, the coverage area in the three coordinate axes is determined. The minimum value Xmin in the x-axis direction is the minimum value of the x-coordinate among the six points, and the maximum value Xmax in the x-axis direction is the maximum value of the x-coordinate among the six points. Similarly, the maximum value (Ymax and Zmax) and minimum value (Ymin and Zmin) of the y-axis and z-axis are calculated. According to the coordinate range, the value of Bounds (boundary) Bounds = [Xmin, Xmax, Ymin, Ymax, Zmin, Zmax]. In order to ensure the effectiveness of the edge as much as possible, in actual cutting, the Bounds will be extended to a certain length in each direction. In this example, extend is set to 2-3mm, which can be adjusted according to the actual business; Bounds can be adjusted to: [Bounds[0]-extend, Bounds[1]+extend, Bounds[2]-extend, Bounds[3]+extend, Bounds[4]-extend, Bounds[5]+extend],

[0095] Get the maximum box cube area composed of Bounds:

[0096] (3) Since elastic stretching is used in the subsequent step, the cropped point cloud obtained by the method in step (2) is directly used, such as Figure 7 As shown, there will be some corners far away from the white points, which will affect the image reconstruction results of the cartilage surface. In order to effectively eliminate the potential risks of directly using the cube shape for cropping to the subsequent algorithm, based on the above step (2), the effective range is selected according to the current status of the projection point (white point). First, calculate the direction with the longest distribution distance of all projection points, which specifically includes the following steps:

[0097] 1) Get the x / y / z coordinate values ​​of the white points respectively, calculate the correlation between them, and form a covariance matrix:

[0098] cov(X i |X j )=E[(X i -μ i )(X j -μ j )], i, j = x, y, z;

[0099]

[0100] Among them, cov(X i |X j ) refers to the calculation of X i and X j The covariance of X i , X j Respectively represent the coordinate values ​​of the projection point on the X-axis, Y-axis or Z-axis, μ i , μ j Respectively represents the average value of the X-axis, Y-axis or Z-axis coordinate value of the projection point.

[0101] 2) Solve the eigenvalues ​​and eigenvectors of matrix A, where the eigenvector describes the characteristics of the white point distribution. For example, the eigenvector corresponding to the maximum eigenvalue corresponds to the direction with the longest white point distribution distance. Therefore, the eigenvector describes the transformation relationship between the reference system constructed based on the point set distribution characteristics and the original reference system, which is recorded as the vector matrix M;

[0102] (4) Using the orientation of the vector matrix M, all point sets are rotated with the geometric center as the origin, that is, directly acting on the vector matrix M. At this time, it is equivalent to "rotating" the white point set along the direction with the longest distribution distance;

[0103] Based on the positive transformation of the white point set, X1max, X1min, Y1max, Y1min, Z1max, and Z1min are also solved;

[0104] The points falling within the intervals of X1min-X1max, Y1min-Y1max, and Z1min-Z1max are retained, and the results are as follows Figure 8 As shown in the light green area, Figure 8 The light green area is the coverage area of ​​the projection point.

[0105] Step 3: In order to simulate the different wear of cartilage in different positions as much as possible, this example uses the method of collecting marker points to obtain several points on the cartilage surface, and replaces the shape of the cartilage surface with the shape of the covering bone cortex, such as Fig. 9 As shown, the blue point set is the marking point of the collected cartilage surface, and the light green point set is the area covered by the blue points on the bone cortex. Then, the light green point set is stretched with the blue point as the end point;

[0106] pass Fig. 9 It can be seen that the blue dots simulate the marking points on the surface of the cartilage, while the light green dot cloud simulates the points on the outer contour of the bone cortex. Due to the differences in human body structure and the degree of wear at different locations, the distances between different blue dots and the light green dot cloud are inconsistent. Even for areas with severe cartilage wear, the blue dots and the light green dot cloud positions overlap, while the unworn locations are elevated to a certain extent. Therefore, the solution of uniformly elevating the light green dot cloud is not advisable. The light green dot cloud must be elevated according to the distance between the corresponding blue dots and the light green dot cloud. This is equivalent to elastically stretching the light green dot cloud. The affine transformation algorithm is used to stretch the light green dot cloud. The specific implementation process is as follows:

[0107] (1) The point set registration problem is converted into a probability density problem. Assume that the two point clouds are X and Y, X is the source point, i.e., the marker point, with a dimension of N and fixed, and Y is the point to be registered, with a dimension of M. Assume that the point set Y is the center of the Gaussian model, and the data points in X can be regarded as generated by the Gaussian model. Then, the probability density function p(x) of each point to be registered Y generated by the source point can be written as:

[0108]

[0109] Wherein, D represents the dimension of the point set. Generally, this embodiment studies the transformation of three-dimensional space, that is, D=3, x represents the source point in the target point cloud, and y m is the current pose of the point cloud to be registered, p(x|m) represents the probability of each point y to be registered to the source point x under the current pose, and σ is the variance of the Gaussian model.

[0110] Among them, y m Optimized for real-time, m The transformation relationship between the initial posture is:

[0111] y m =Ry0m +t;

[0112] Among them, y0 m is the initial pose of the point y to be registered in the point cloud to be registered, R is the rotation matrix, t is the translation matrix. In the following expression, T(θ) can be used to uniformly represent R and t, where θ is the parameter vector of the abstract rotation matrix and translation matrix.

[0113] If point sets X and Y are exactly the same, that is, N = M, when point sets X and Y are correctly aligned, that is, there exists a transformation matrix T(θ). After point set Y is transformed using the transformation matrix T(θ), the two point sets completely overlap. At this time, there is always a point x i and m If the distance between two points is 0, then the exp part in the above formula is equal to 1. However, it can be seen from the above formula that, except for the points that coincide with X, the contribution of other Y points to this distance will decrease exponentially.

[0114] If the point sets X and Y are different, that is, N≠M, the number of point clouds in the two sets of data is inconsistent. In this case, an additional uniform distribution function p(x|M+1) is introduced:

[0115]

[0116] Assuming that the weight of the uniform distribution function is w, the above probability density function p(x) can be further expressed as:

[0117]

[0118] Assuming that each data point is independent and identically distributed, the likelihood function L(θ|σ 2 ) can be expressed as:

[0119]

[0120] By minimizing the likelihood function, we can get the parameters (θ|σ 2 ), the process of solving θ and σ can use the expectation maximization algorithm;

[0121] Step 4: According to the above algorithm, the simulated cartilage surface is obtained, such as Fig.10 As shown, the purple point cloud in the figure is the reconstructed image of the cartilage surface. The purple point cloud and the target blue point, that is, the marking point on the cartilage surface, completely overlap, and the image reconstruction is completed.

[0122] It should be noted here that the present invention obtains the outer contour model of the cortical bone and the set of marking points on the cartilage surface, and transforms the outer contour model of the cortical bone and the set of marking points on the cartilage surface into a preset image coordinate system; obtains the projection points of each marking point on the cartilage surface on the outer contour of the cortical bone, and determines the coverage area of ​​the projection points; elastically stretches the coverage area of ​​the projection points to simulate the degree of wear on the cartilage surface to obtain a reconstructed cartilage surface. It can support the existing imaging modality. During surgery, by collecting marking points on the cartilage surface, the cartilage surface is simulated and generated in key areas, with arbitrary positions and without restrictions, thereby providing effective guidance to doctors when planning and adjusting the prosthesis, using the cortical bone surface to approximately replace the cartilage surface, and giving the actual cartilage height of each area according to the actual wear conditions, which is more conducive to prosthesis planning and adjustment, and solves the problem that the current joint replacement surgery does not incorporate the influence of cartilage into prosthesis planning, thereby causing muscle or ligament wear, and greatly improves the intelligence, usability and reliability of the present invention.

[0123] See also Figure 2 This embodiment provides a system for reconstructing cartilage surface, the system comprising:

[0124] Acquisition module 100: used to acquire the outer contour model of the bone cortex and the set of marking points on the cartilage surface; or used to acquire the projection points of each marking point in the set of marking points on the cartilage surface on the outer contour of the bone cortex;

[0125] Control module 200: used to transform the outer contour model of the bone cortex and the set of marking points on the cartilage surface into a preset image coordinate system; or used to determine the coverage area of ​​the projection points; or used to elastically stretch the coverage area of ​​the projection points to simulate the degree of wear on the cartilage surface and obtain a reconstructed cartilage surface.

[0126] It should be noted here that the present invention obtains the outer contour model of the cortical bone and the set of marking points on the cartilage surface, and transforms the outer contour model of the cortical bone and the set of marking points on the cartilage surface into a preset image coordinate system; obtains the projection points of each marking point on the cartilage surface on the outer contour of the cortical bone, and determines the coverage area of ​​the projection points; elastically stretches the coverage area of ​​the projection points to simulate the degree of wear on the cartilage surface to obtain a reconstructed cartilage surface. It can support the existing imaging modality. During surgery, by collecting marking points on the cartilage surface, the cartilage surface is simulated and generated in key areas, with arbitrary positions and without restrictions, thereby providing effective guidance to doctors when planning and adjusting the prosthesis, using the cortical bone surface to approximately replace the cartilage surface, and giving the actual cartilage height of each area according to the actual wear conditions, which is more conducive to prosthesis planning and adjustment, and solves the problem that the current joint replacement surgery does not incorporate the influence of cartilage into prosthesis planning, thereby causing muscle or ligament wear, and greatly improves the intelligence, usability and reliability of the present invention.

[0127] In a preferred embodiment, the present application further provides an electronic device, the electronic device comprising:

[0128] A memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for reconstructing the cartilage surface is implemented. The computer device can be broadly a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected via a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, the steps of the method of the present invention are executed.

[0129] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0130] It should be noted here that the present invention obtains the outer contour model of the cortical bone and the set of marking points on the cartilage surface, and transforms the outer contour model of the cortical bone and the set of marking points on the cartilage surface into a preset image coordinate system; obtains the projection points of each marking point on the cartilage surface on the outer contour of the cortical bone, and determines the coverage area of ​​the projection points; elastically stretches the coverage area of ​​the projection points to simulate the degree of wear on the cartilage surface to obtain a reconstructed cartilage surface. It can support the existing imaging modality. During surgery, by collecting marking points on the cartilage surface, the cartilage surface is simulated and generated in key areas, with arbitrary positions and without restrictions, thereby providing effective guidance to doctors when planning and adjusting the prosthesis, using the cortical bone surface to approximately replace the cartilage surface, and giving the actual cartilage height of each area according to the actual wear conditions, which is more conducive to prosthesis planning and adjustment, and solves the problem that the current joint replacement surgery does not incorporate the influence of cartilage into prosthesis planning, thereby causing muscle or ligament wear, and greatly improves the intelligence, usability and reliability of the present invention.

[0131] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0132] The specific implementation of the present invention described above does not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for reconstructing a cartilage surface, characterized in that: include: S100, obtaining an outer contour model of the bone cortex and a set of marked points on the cartilage surface, and transforming the outer contour model of the bone cortex and the set of marked points on the cartilage surface into a preset image coordinate system; S200, obtaining projection points of each marking point in the marking point set on the cartilage surface on the outer contour of the bone cortex, and determining the coverage area of ​​the projection points; S300, elastically stretching the coverage area of ​​the projection point to simulate the wear degree of the cartilage surface and obtain a reconstructed cartilage surface.

2. The method for reconstructing cartilage surface according to claim 1, characterized in that: The step of obtaining the projection points of each marking point in the marking point set on the cartilage surface on the outer contour of the bone cortex includes: By calling the FindClosestPoint function in the VTK library, the points on the cartilage surface that are closest to the outer contour of the bone cortex are found in turn, and the points on the cartilage surface that are closest to the outer contour of the bone cortex are used as the projection points of each marking point.

3. The method for reconstructing cartilage surface according to claim 2, characterized in that: The determining the coverage area of ​​the projection point comprises: Obtaining coordinates of all projection points in a preset image coordinate system, and generating a first coverage area of ​​each projection point according to the coordinates; According to the distribution of the projection points, the second coverage area of ​​the projection points is further screened.

4. The method for reconstructing cartilage surface according to claim 3, characterized in that: The step of generating a first coverage area for each projection point according to the coordinates includes: The first coordinate of each projection point in the preset image coordinate system is obtained, and then the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the first coordinate of each projection point are respectively obtained, and the maximum and minimum values ​​of the X-axis coordinate are obtained, which are recorded as Xmax and Xmin respectively. Similarly, the maximum and minimum values ​​of the Y-axis coordinate are obtained, which are recorded as Ymax and Ymin respectively, and the maximum and minimum values ​​of the Z-axis coordinate are obtained, which are recorded as Zmax and Zmin. The first coverage area of ​​each projection point is generated according to Xmax and Xmin, Ymax and Ymin, and Zmax and Zmin.

5. The method for reconstructing cartilage surface according to claim 4, characterized in that: The generating a first coverage area of ​​each projection point according to the Xmax and Xmin, Ymax and Ymin, Zmax and Zmin comprises: The maximum value Xmax of the X-axis coordinate, the maximum value Ymax of the Y-axis coordinate, and the maximum value Zmax of the Z-axis coordinate are respectively increased by the first preset value extend, and are recorded as Xmax+extend, Ymax+extend, and Zmax+extend respectively; The minimum value Xmin of the X-axis coordinate, the minimum value Xmin of the Y-axis coordinate, and the minimum value Xmin of the Z-axis coordinate are respectively reduced by the first preset value extend, and are respectively recorded as Xmin-extend, Ymin-extend, and Zmin-extend; Take (Xmin-extend, Xmin+extend, Ymin-extend, Ymin+extend, Zmin-extend, Zmin+extend) as the first boundary coordinates of the projection point, and generate the first coverage area of ​​each projection point according to the first boundary coordinates.

6. The method for reconstructing cartilage surface according to claim 5, characterized in that: The further screening the second coverage area of ​​the projection points according to the distribution of the projection points includes: The first coordinates of each projection point on the three coordinate axes of the preset image coordinate system are respectively obtained, the correlation between the projection points is calculated according to the first coordinates, and a covariance matrix of each projection point is formed, and the eigenvector corresponding to the maximum eigenvalue of each projection point is calculated according to the covariance matrix to form a vector matrix, and the vector matrix is ​​used to transform the original coordinates of each projection point to obtain the second coordinates of each projection point, and a second coverage area is generated according to the second coordinates.

7. The method for reconstructing cartilage surface according to claim 6, characterized in that: The generating a second coverage area according to the second coordinates includes: Select the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the second coordinate of each projection point in the second coordinate respectively, and obtain the maximum and minimum values ​​of the X-axis coordinate in the second coordinate, which are recorded as X1max and X1min respectively. Similarly, obtain the maximum and minimum values ​​of the Y-axis coordinate in the second coordinate, which are recorded as Y1max and Y1min respectively, and obtain the maximum and minimum values ​​of the Z-axis coordinate in the second coordinate, which are recorded as Z1max and Z1min; Take (X1min, X1max, Y1min, Y1max, Z1min, Z1max) as the second boundary coordinates of the projection point, and generate the second coverage area of ​​each projection point according to the second boundary coordinates.

8. The method for reconstructing cartilage surface according to claim 6, characterized in that: The S300 includes: The shape of the outer contour of the bone cortex is used to approximate the shape of the cartilage surface. That is, the second coverage area of ​​each projection point is elastically stretched in the direction of the marked points on the cartilage surface. That is, the second coverage area is raised according to the distance distribution between the marked point set on the cartilage surface and the second coverage area, thereby simulating the wear degree of the cartilage surface and obtaining the reconstructed cartilage surface.

9. The method for reconstructing cartilage surface according to claim 7, characterized in that: The method of raising the second covering area according to the distance distribution between the marked point set on the cartilage surface and the second covering area, thereby simulating the wear degree of the cartilage surface to obtain the reconstructed cartilage surface, includes: The marked point set on the cartilage surface is taken as the center of the Gaussian model, and the point set in the second coverage area is regarded as generated by the Gaussian model. A probability density function is constructed according to the relationship between the marked point set and the point set in the second coverage area. The projection points that need to be raised are calculated and raised, thereby simulating the degree of wear on the cartilage surface and obtaining the reconstructed cartilage surface.

10. A system for reconstructing cartilage surface, characterized in that: include: An acquisition module (100): used to acquire an outer contour model of the bone cortex and a set of marking points on the cartilage surface; or used to acquire projection points of each marking point in the set of marking points on the cartilage surface on the outer contour of the bone cortex; A control module (200): used to transform the outer contour model of the bone cortex and the set of marking points on the cartilage surface into a preset image coordinate system; or for determining the coverage area of ​​the projection point; Or it is used to elastically stretch the coverage area of ​​the projection point, thereby simulating the degree of wear on the cartilage surface to obtain a reconstructed cartilage surface.

11. An electronic device, characterized in that: include: Memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for reconstructing a cartilage surface according to any one of claims 1 to 9 is implemented.

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