Method, system and electronic device for reconstructing cartilage surface

Abstract

The present invention provides a method and system for reconstructing a cartilage surface, and an electronic device, comprising: obtaining an outer contour model of the bone cortex and a set of marking points of the cartilage surface, and transforming the outer contour model of the bone cortex and the set of marking points of the cartilage surface into a preset image coordinate system; obtaining the projection points of each marking point in the set of marking points of the cartilage surface on the outer contour of the bone cortex, and determining the coverage area of ​​the projection points; elastically stretching the coverage area of ​​the projection points to simulate the degree of wear on the cartilage surface to obtain a reconstructed cartilage surface; solving the problem that current joint replacement surgery does not incorporate the influence of cartilage into prosthesis planning, thereby causing wear of muscles or ligaments, and can provide practical and effective guidance when performing prosthesis planning and adjustment, which is more conducive to prosthesis planning and adjustment. The present invention greatly improves the intelligence, usability and reliability of prosthesis planning in joint replacement.

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] Today, the technology for replacing artificial joint prostheses is relatively mature and is gradually bringing benefits to more and more people. However, in early joint replacement surgeries, especially early traditional surgeries, the impact of the cartilage on the surface of the bone on the placement of the prosthesis was not taken into account due to the limitations of surgical precision at the time. Today, with the rapid development of orthopedic surgical robots, joint replacements are becoming increasingly automated and repeatable, and especially the accuracy has been greatly improved. For example, the existing knee replacement assistance system can achieve a surgical accuracy of about 1mm. In comparison, the impact of cartilage, which can be as thick as 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. General medical imaging modalities use X-ray tomography, which provides clearer images of high-density tissue (bone), and extract the bone surface from these images. However, cartilage is difficult to distinguish from muscle in X-ray imaging due to its low density. Currently, most joint replacement surgeries do not incorporate the impact of cartilage into prosthesis planning, which can easily lead to the formation of a "step" at the contact point between the prosthesis and the actual bone surface with cartilage, causing muscle or ligament wear and tear, resulting in a poor postoperative experience for the patient. In severe cases, a second surgical adjustment may be required. Therefore, with existing technologies, the identification of cartilage surfaces remains an unresolved issue.

[0004] Therefore, the existing technology 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, as well as 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 cortical bone and a set of marked points on the cartilage surface, and transforming the outer contour model of the cortical bone 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, 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 closest to each marked point on the cartilage surface and the outer contour of the bone cortex are found in turn, and the points closest to each marked point on the cartilage surface and the outer contour of the bone cortex are used as the projection points of each marked 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 for 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] Obtain the first coordinate of each projection point in the preset image coordinate system, and then obtain the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the first coordinate of each projection point respectively, obtain the maximum and minimum values ​​of the X-axis coordinate, and record them as Xmax and Xmin respectively. Similarly, obtain the maximum and minimum values ​​of the Y-axis coordinate, and record them as Ymax and Ymin respectively, and obtain the maximum and minimum values ​​of the Z-axis coordinate, and record them as Zmax and Zmin. Generate the first coverage area of ​​each projection point based on the Xmax and Xmin, Ymax and Ymin, and Zmax and Zmin.

[0017] Specifically, generating a first coverage area for each projection point according to Xmax and Xmin, Ymax and Ymin, and 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 recorded as Xmin-extend, Ymin-extend, and Zmin-extend respectively;

[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] Obtain the first coordinates of each projection point on the three coordinate axes of the preset image coordinate system respectively, calculate the correlation between the projection points based on the first coordinates, and form a covariance matrix of each projection point, calculate the eigenvector corresponding to the maximum eigenvalue of each projection point based on the covariance matrix, form a vector matrix, use the vector matrix to realize the transformation of the original coordinates of each projection point, obtain the second coordinates of each projection point, and generate a second coverage area based on 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 a second coverage area for 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 approximately replace the shape of the cartilage surface. That is, the second coverage area of ​​each projection point is elastically stretched along the direction of the marking point on the cartilage surface. That is, the second coverage area is raised according to the distance distribution between the marking point set on the cartilage surface and the second coverage area, thereby simulating the degree of wear on the cartilage surface and obtaining the reconstructed cartilage surface.

[0028] Specifically, the step of raising the second covering area according to the distribution of distances between the set of marking points on the cartilage surface and the second covering area, thereby simulating the degree of wear on the cartilage surface to obtain a reconstructed cartilage surface, includes:

[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 elevated are calculated and elevated, 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 obtain the outer contour model of the bone cortex and the set of marker points on the cartilage surface; or used to obtain the projection points of each marker point in the set of marker 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 marked 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 when the computer-readable instructions are executed by the processor, the above-mentioned method for reconstructing a cartilage surface is implemented.

[0034] Beneficial effects:

[0035] The present invention obtains the outer contour model of the cortical bone and the set of marker points of the cartilage surface, and transforms the outer contour model of the cortical bone and the set of marker points of the cartilage surface into a preset image coordinate system; obtains the projection points of each marker 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 of the cartilage surface to obtain a reconstructed cartilage surface. It can support the existing imaging modality and simulate the generation of cartilage surfaces in key areas during surgery by collecting marker points on the cartilage surface. The positions are arbitrary and unrestricted, thereby providing effective guidance to doctors when planning and adjusting prostheses. 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 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 accordance with a specific embodiment of the present invention;

[0037] Figure 2 Schematic diagram of the composition of a system for reconstructing 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 Schematic diagram of the relative positions of the marking points on the cartilage surface and the cortical bone model provided in a specific embodiment of the present invention;

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

[0041] Figure 6 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 is provided in a specific embodiment of the present invention;

[0044] Figure 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] Figure 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 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 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 drawings. Therefore, the directional words used are used to illustrate rather than limit the invention.

[0047] The present invention will be further described below with reference to 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 cortical bone and a set of marked points on the cartilage surface, and transforming the outer contour model of the cortical bone 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 marking 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 marking point area can be performed.

[0051] Furthermore, the bone cortex can be scanned by X-ray tomography to obtain the 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 alignment matrix can be used to transform each marker point in the cartilage surface marker point set into the image bone model reference system.

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

[0053] Specifically, 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 closest to each marked point on the cartilage surface and the outer contour of the bone cortex are found in turn, and the points closest to each marked point on the cartilage surface and the outer contour of the bone cortex are used as the projection points of each marked 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 is to establish a mapping relationship between the outer contour of the cortex and the marker points on the cartilage surface. It can accurately find the nearest point to each marker point on the cartilage surface in the data set of the outer contour of the 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, simplifying the mesh into a unit, such as Figure 5 As shown in the figure, 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 excessive smoothness of the 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 a vertex as the nearest neighbor point is adopted, due to the limited number of vertices and uneven distribution, the nearest neighbor vertex found may be significantly different from the actual point closest to the acquisition 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 the true sense. The search through the FindClosestPoint function is based on the geometric relationship, 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 closest point, avoiding the error caused by sparse vertices, and thus 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 for 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 marker 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] Obtain the first coordinate of each projection point in the preset image coordinate system, and then obtain the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the first coordinate of each projection point respectively, obtain the maximum and minimum values ​​of the X-axis coordinate, and record them as Xmax and Xmin respectively. Similarly, obtain the maximum and minimum values ​​of the Y-axis coordinate, and record them as Ymax and Ymin respectively, and obtain the maximum and minimum values ​​of the Z-axis coordinate, and record them as Zmax and Zmin. Generate the first coverage area of ​​each projection point based on the Xmax and Xmin, Ymax and Ymin, and Zmax and Zmin.

[0063] Specifically, generating a first coverage area for each projection point according to Xmax and Xmin, Ymax and Ymin, and 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 recorded as Xmin-extend, Ymin-extend, and Zmin-extend respectively;

[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 will be increased. This length can be adjusted according to actual needs. The present invention does not impose further restrictions on the specific 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] Obtain the first coordinates of each projection point on the three coordinate axes of the preset image coordinate system respectively, calculate the correlation between the projection points based on the first coordinates, and form a covariance matrix of each projection point, calculate the eigenvector corresponding to the maximum eigenvalue of each projection point based on the covariance matrix, form a vector matrix, use the vector matrix to realize the transformation of the original coordinates of each projection point, obtain the second coordinates of each projection point, and generate a second coverage area based on 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 cube shape, 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) Obtain the first coordinates of the projection points, i.e., the coordinate values ​​of the X-axis, Y-axis, and Z-axis, respectively, 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 X-axis, Y-axis, or Z-axis coordinate values ​​of the projection point, μ i 、μj Represents the average value of the X-axis, Y-axis, or Z-axis coordinate values ​​of the projection point respectively.

[0075] (2) Solve the eigenvalues ​​and eigenvectors of the covariance matrix A, where the eigenvectors describe the characteristics of the distribution of the projection points. For example, the eigenvector corresponding to the maximum eigenvalue corresponds to the direction with the longest distance of the projection point distribution. The eigenvectors describe 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 "normalized" 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] (X1min, X1max, Y1min, Y1max, Z1min, Z1max) is used as the second boundary coordinates of the projection point, and a second coverage area of ​​each projection point is generated 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 approximately replace the shape of the cartilage surface. That is, the second coverage area of ​​each projection point is elastically stretched along the direction of the marking point on the cartilage surface. That is, the second coverage area is raised according to the distance distribution between the marking point set on the cartilage surface and the second coverage area, thereby simulating the degree of wear on 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 step of raising the second covering area according to the distribution of distances between the set of marking points on the cartilage surface and the second covering area, thereby simulating the degree of wear on the cartilage surface to obtain a reconstructed cartilage surface, includes:

[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 elevated are calculated and elevated, thereby simulating the degree of wear on the cartilage surface and obtaining the reconstructed cartilage surface.

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

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

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

[0090] Step 2: To capture the area covered by the marker points on the outer contour of the cortical bone, first project the points onto the outer contour of the cortical bone model to confirm the coverage area of ​​the projected points. 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] Among them, FindClosestPoint is not simply looking for the vertices that make up the mesh, but finding any point on the mesh surface that is closest to it. When the mesh is simplified into a unit, such as Figure 5 As shown in the figure, to find the nearest neighbor point of point P on the grid, we can draw a perpendicular line through point P to the plane where ABCD lies, where the foot of the perpendicular is the closest neighbor point of P on the grid. This method can effectively avoid the problem of sparse vertices caused by oversmoothing the bone cortical surface during meshing, which leads to 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 markers suspended above the cortical bone and on the surface of the cartilage, and the white points are the nearest neighbor points found on the outer contour of the cortical bone using the FindClosestPoint function, that is, the projection points. The coverage area of ​​the projection points (white points) on the outer contour of the cortical bone is determined. The specific process of the coverage area size is as follows:

[0094] Based on 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-coordinates of the six points, and the maximum value Xmax in the x-axis direction is the maximum value of the x-coordinates of 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. Based on 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 cropping, a certain length extend will be extended in each direction of Bounds. In this example, extend is set to 2-3mm, which can be adjusted according to 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 in the figure, 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 points (white points). First, calculate the direction with the longest distribution distance of all projection points, which 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 the 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 X-axis, Y-axis, or Z-axis coordinate values ​​of the projection point, μ i 、μ j Represents the average value of the X-axis, Y-axis, or Z-axis coordinate values ​​of the projection point respectively.

[0101] 2) Calculate the eigenvalues ​​and eigenvectors of matrix A. The eigenvectors describe the characteristics of the white point distribution. For example, the eigenvector corresponding to the largest eigenvalue corresponds to the direction with the longest white point distribution. Therefore, the eigenvectors describe the transformation relationship between the reference system constructed based on the point distribution characteristics and the original reference system, denoted as 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 "rectifying" the white point set along the direction of its longest distribution distance;

[0103] Based on the normalization 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 Figure 9 As shown, the blue point set is the marker 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 Figure 9 It can be seen that the blue points simulate the marking points on the surface of the cartilage, while the light green point cloud simulates the points on the outer contour of the bone cortex. Due to the differences in human body structure and the differences in the degree of wear at different locations, the distances between different blue points and the light green point cloud are inconsistent. Even for areas with severe cartilage wear, the blue points and the light green point cloud positions coincide, while the unworn areas are elevated to a certain extent. Therefore, it is not advisable to uniformly elevate the light green point cloud. The light green point cloud must be elevated according to the distance between the corresponding blue points and the light green point cloud. This is equivalent to elastically stretching the light green point cloud. The affine transformation algorithm is used to stretch the light green point cloud. The specific implementation process is as follows:

[0107] (1) Convert the point set registration problem 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] Where 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 pose and the initial pose 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, and 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 y m Coincidentally, that is, the distance between the 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 Figure 10 As shown in the figure, the purple point cloud 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 marker points on the cartilage surface, and transforms the outer contour model of the cortical bone and the set of marker points on the cartilage surface into a preset image coordinate system; obtains the projection points of each marker 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. This can support the existing imaging modality. During surgery, by collecting marker points on the cartilage surface, the cartilage surface is simulated and generated in key areas, with arbitrary positions and no restrictions, thereby providing doctors with practical and effective guidance 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 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.

[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 marker points on the cartilage surface; or used to acquire the projection points of each marker point in the set of marker 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 point; or used to elastically stretch the coverage area of ​​the projection point 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 marker points on the cartilage surface, and transforms the outer contour model of the cortical bone and the set of marker points on the cartilage surface into a preset image coordinate system; obtains the projection points of each marker 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. This can support the existing imaging modality. During surgery, by collecting marker points on the cartilage surface, the cartilage surface is simulated and generated in key areas, with arbitrary positions and no restrictions, thereby providing doctors with practical and effective guidance 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 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.

[0127] In a preferred embodiment, the present application further provides an 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 defined as 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 performed.

[0129] The present invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the method of an embodiment of the present invention to be performed. 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, can be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations can be performed by one or more computer devices or processors, and one or more other method steps / operations can be performed by one or more other computer devices or processors. One or more computer devices or processors can 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 marker points on the cartilage surface, and transforms the outer contour model of the cortical bone and the set of marker points on the cartilage surface into a preset image coordinate system; obtains the projection points of each marker 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. This can support the existing imaging modality. During surgery, by collecting marker points on the cartilage surface, the cartilage surface is simulated and generated in key areas, with arbitrary positions and no restrictions, thereby providing doctors with practical and effective guidance 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 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.

[0131] The various technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification as long as such combination does not conflict.

[0132] The specific embodiments of the present invention described above do not limit the scope of protection 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 scope of protection 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 cortical bone and a set of marked points on the cartilage surface, and transforming the outer contour model of the cortical bone 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 area covered by the projection point to simulate the degree of wear on 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 closest to each marked point on the cartilage surface and the outer contour of the bone cortex are found in turn, and the points closest to each marked point on the cartilage surface and the outer contour of the bone cortex are used as the projection points of each marked point.

3. The method for reconstructing cartilage surface according to claim 2, characterized in that: Determining the coverage area of ​​the projection point includes: Obtaining coordinates of all projection points in a preset image coordinate system, and generating a first coverage area for 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: Generating a first coverage area for each projection point according to the coordinates includes: Obtain the first coordinate of each projection point in the preset image coordinate system, and then obtain the X-axis coordinate, Y-axis coordinate, and Z-axis coordinate of the first coordinate of each projection point respectively, obtain the maximum and minimum values ​​of the X-axis coordinate, and record them as Xmax and Xmin respectively. Similarly, obtain the maximum and minimum values ​​of the Y-axis coordinate, and record them as Ymax and Ymin respectively, and obtain the maximum and minimum values ​​of the Z-axis coordinate, and record them as Zmax and Zmin. Generate the first coverage area of ​​each projection point based on the Xmax and Xmin, Ymax and Ymin, and Zmax and Zmin.

5. The method for reconstructing cartilage surface according to claim 4, characterized in that: Generating a first coverage area for each projection point according to the Xmax and Xmin, Ymax and Ymin, and Zmax and Zmin includes: 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 recorded as Xmin-extend, Ymin-extend, and Zmin-extend respectively; (Xmin-extend, Xmin+extend, Ymin-extend, Ymin+extend, Zmin-extend, Zmin+extend) is used as the first boundary coordinates of the projection point, and a first coverage area of ​​each projection point is generated according to the first boundary coordinates.

6. The method for reconstructing cartilage surface according to claim 5, characterized in that: The further screening of the second coverage area of ​​the projection points according to the distribution of the projection points includes: Obtain the first coordinates of each projection point on the three coordinate axes of the preset image coordinate system respectively, calculate the correlation between the projection points based on the first coordinates, and form a covariance matrix of each projection point, calculate the eigenvector corresponding to the maximum eigenvalue of each projection point based on the covariance matrix, form a vector matrix, use the vector matrix to realize the transformation of the original coordinates of each projection point, obtain the second coordinates of each projection point, and generate a second coverage area based on the second coordinates.

7. The method for reconstructing cartilage surface according to claim 6, characterized in that: 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; (X1min, X1max, Y1min, Y1max, Z1min, Z1max) is used as the second boundary coordinates of the projection point, and a second coverage area of ​​each projection point is generated 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 approximately replace the shape of the cartilage surface. That is, the second coverage area of ​​each projection point is elastically stretched along the direction of the marking point on the cartilage surface. That is, the second coverage area is raised according to the distance distribution between the marking point set on the cartilage surface and the second coverage area, thereby simulating the degree of wear on the cartilage surface and obtaining the reconstructed cartilage surface.

9. The method for reconstructing cartilage surface according to claim 7, characterized in that: The step of elevating the second covering area according to the distribution of the distances between the set of marking points on the cartilage surface and the second covering area, thereby simulating the degree of wear on the cartilage surface to obtain a 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 elevated are calculated and elevated, 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: The method for reconstructing a cartilage surface according to any one of claims 1 to 9, wherein the system comprises: 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; A control module (200) is 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 to determine the coverage area of ​​the projection points; or 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.

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 the cartilage surface according to any one of claims 1 to 9 is implemented.

Images