Method and device for planning osteotomy around acetabulum based on collision detection

Abstract

The invention provides an acetabulum periphery osteotomy planning method and device based on collision detection, and the method comprises the steps: obtaining a hip joint medical image, and constructing a hip joint three-dimensional model; according to the hip joint three-dimensional model, the initial adjusting position and the initial osteotomy position of the acetabulum are determined; simulating collision data of the femoral head and the acetabulum in different motion states; based on the collision data, the adjusting position and the osteotomy position of the acetabulum are optimized, and the optimal adjusting position and the optimal osteotomy position of the acetabulum are determined. According to the method, the acetabulum position and the osteotomy position are optimized by simulating the collision data of the femoral head and the acetabulum in different motion states, so that the accuracy of planning the acetabulum position and the osteotomy position is greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of medical image processing, and in particular to a method and device for planning periacetabular osteotomy based on collision detection. Background Art

[0002] Periacetabular osteotomy (PAO) is a surgical procedure used to treat developmental dysplasia of the hip. PAO improves hip coverage by repositioning the acetabulum, thereby relieving pain and delaying or preventing the progression of degenerative hip disease.

[0003] Currently, the acetabular position and osteotomy position are planned only by acetabular coverage, which is not accurate enough. Summary of the invention

[0004] The problem addressed by this application is that current planning of acetabular position and osteotomy position is insufficiently accurate.

[0005] In order to solve the above problems, the first aspect of the present application provides a periacetabular osteotomy planning method based on collision detection, comprising:

[0006] Obtain medical images of hip joint and construct a three-dimensional model of hip joint;

[0007] According to the three-dimensional model of the hip joint, determine the initial adjustment position and initial osteotomy position of the acetabulum;

[0008] Simulate the collision data between the femoral head and acetabulum in different motion states;

[0009] Based on the collision data, the adjustment position and the osteotomy position of the acetabulum are optimized to determine the best adjustment position and the best osteotomy position of the acetabulum.

[0010] The second aspect of the present application provides a periacetabular osteotomy planning device based on collision detection, which comprises:

[0011] A model building module, which is used to obtain medical images of the hip joint and build a three-dimensional model of the hip joint;

[0012] A position determination module, which is used to determine the initial adjustment position and initial osteotomy position of the acetabulum according to the three-dimensional model of the hip joint;

[0013] A collision detection module, which is used to simulate the collision data between the femoral head and the acetabulum in different motion states;

[0014] A position optimization module is used to optimize the adjustment position and osteotomy position of the acetabulum based on the collision data, and determine the best adjustment position and best osteotomy position of the acetabulum.

[0015] A third aspect of the present application provides an electronic device, comprising: a memory and a processor;

[0016] The memory is used to store programs;

[0017] The processor, coupled to the memory, is configured to execute the program to:

[0018] Obtain medical images of hip joint and construct a three-dimensional model of hip joint;

[0019] According to the three-dimensional model of the hip joint, determine the initial adjustment position and initial osteotomy position of the acetabulum;

[0020] Simulate the collision data between the femoral head and acetabulum in different motion states;

[0021] Based on the collision data, the adjustment position and the osteotomy position of the acetabulum are optimized to determine the best adjustment position and the best osteotomy position of the acetabulum.

[0022] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the above-mentioned collision detection-based periacetabular osteotomy planning method.

[0023] In the present application, the acetabulum position and the osteotomy position are optimized by simulating the collision data of the femoral head and the acetabulum in different motion states, thereby greatly increasing the accuracy of the acetabulum position and the osteotomy position planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of a method for planning periacetabular osteotomy according to an embodiment of the present application;

[0025] Figure 2 A flowchart of collision detection of a method for planning osteotomy around the acetabulum according to an embodiment of the present application;

[0026] Figure 3 is a structural block diagram of a periacetabular osteotomy planning device according to an embodiment of the present application;

[0027] Figure 4 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below in conjunction with the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0029] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by technicians in the field to which this application belongs.

[0030] In response to the above problems, the present application provides a new acetabular osteotomy planning scheme based on collision detection, which solves the problem of low accuracy of three-dimensional reconstruction results generated by current X-ray films by down-sampling the anteroposterior and lateral two-dimensional images respectively, and up-sampling the three-dimensional images obtained by conversion and fusion.

[0031] The embodiment of the present application provides a method for planning osteotomy around the acetabulum based on collision detection. The specific scheme of the method is as follows: Figure 1-Figure 2 As shown, the method can be performed by a periacetabular osteotomy planning device based on collision detection, and the periacetabular osteotomy planning device based on collision detection can be integrated into electronic devices such as computers, servers, computers, server clusters, and data centers. Figure 1 As shown, it is a flow chart of a method for planning osteotomy around the acetabulum based on collision detection according to an embodiment of the present application; wherein the method for planning osteotomy around the acetabulum based on collision detection comprises:

[0032] S101, acquiring medical images of the hip joint and constructing a three-dimensional model of the hip joint;

[0033] The hip joint medical image is a CT image or an MRI image including the hip joint.

[0034] In the present application, based on the medical image of the hip joint, three-dimensional point cloud data of the hip joint can be generated; based on the three-dimensional point cloud data of the hip joint, the three-dimensional point cloud data can be converted into a three-dimensional model of the hip joint through image processing technology.

[0035] S102, determining an initial adjustment position and an initial osteotomy position of the acetabulum according to the three-dimensional model of the hip joint;

[0036] Among them, the initial adjustment position of the acetabulum can be obtained in the following ways: identifying the key points of the three-dimensional model of the hip joint; analyzing the three-dimensional model of the hip joint to evaluate the direction of the acetabulum opening, the location and degree of coverage of the defect; and determining the initial adjustment position of the acetabulum based on the evaluation results and the standard acetabulum inclination angle.

[0037] In the initial adjustment position of the acetabulum, the direction of the acetabular opening was evaluated by the lateral center edge angle (LCEA) and the acetabular inclination angle (AIA), and the location and degree of coverage of the defect were evaluated by the head and acetabulum coverage ratio (HAI).

[0038] S103, simulated collision data between the femoral head and acetabulum in different motion states;

[0039] S104, optimizing the adjustment position and osteotomy position of the acetabulum based on the collision data, and determining the best adjustment position and best osteotomy position of the acetabulum.

[0040] In the present application, the acetabulum position and the osteotomy position are optimized by simulating the collision data of the femoral head and the acetabulum in different motion states, thereby greatly increasing the accuracy of the acetabulum position and the osteotomy position planning.

[0041] In one embodiment, in combination Figure 2 As shown,

[0042] The S103, simulating collision data between the femoral head and the acetabulum in different motion states, includes:

[0043] S301, obtaining the current acetabulum adjustment position and osteotomy position;

[0044] S302, representing the femoral head and acetabulum as a polygonal mesh, wherein the polygonal mesh is composed of a plurality of triangular facets;

[0045] In the present application, after obtaining the three-dimensional model of the femoral head, the surface point cloud of the femoral head is determined, and the surface point cloud is converted into a polygonal mesh composed of triangular facets. The surface point cloud can be converted into triangular facets by a triangulation algorithm.

[0046] Preferably, the generated polygonal mesh is optimized using a mesh optimization algorithm to reduce the number of triangles in the polygonal mesh and remove irregular shapes or noise in the mesh, while retaining the geometric features of the original model as much as possible.

[0047] Similarly, in this application, the polygonal mesh of the acetabulum is generated using the same method.

[0048] S303, simulating the motion state of the femoral head;

[0049] In the present application, the movement states of the femoral head may include flexion / extension, internal / external rotation, and abduction / adduction, wherein the range of motion of the femoral head is: flexion / extension: -10° to 120°; internal / external rotation: -45° to 45°; abduction / adduction: approximately -30° to 45°.

[0050] S304, determining the collision position between the femoral head and the acetabulum in the motion state;

[0051] In the present application, the collision position between the femoral head and the acetabulum in the motion state is determined based on the extreme motion state of the femoral head. For example, the collision position between the femoral head and the acetabulum is determined based on the motion state of the femoral head being flexed by -10°.

[0052] S305, traversing all the motion states of the femoral head, and determining the collision position in each motion state as the collision data.

[0053] In the present application, the motion state of the femoral head is infinite, and the motion states under several extreme conditions are set to represent all the motion states of the femoral head.

[0054] Among them, the motion states under several extreme conditions include flexion / extension state, internal rotation / external rotation state, and abduction / adduction state.

[0055] Among them, the range of motion of the femoral head is: flexion / extension: -10° to 120°; internal rotation / external rotation: -45° to 45°; abduction / adduction: approximately -30° to 45°. Based on this, the range of motion of the femoral head forms a closed area, and the motion state at the boundary of the range of motion is set as the motion state of the femoral head under extreme conditions.

[0056] In the present application, a plurality of discrete points are selected on the boundary of a closed area as the motion states in the aforementioned several extreme cases.

[0057] Among them, traversing the motion state under this limit case is deemed to be traversing all the motion states of the femoral head.

[0058] In this way, the motion state of the femoral head is represented by the motion state under several extreme conditions, thereby greatly reducing the amount of calculation. Moreover, by counting all the motion states of the femoral head, the motion state of the femoral head can be accurately simulated, and the collision data can be accurately counted.

[0059] In one embodiment, the step S304 of determining the collision position between the femoral head and the acetabulum in the motion state includes:

[0060] Determine the associated triangular surface between the femoral head and the acetabulum in this motion state;

[0061] Calculate the minimum distance between two associated triangles;

[0062] If the minimum distance between two associated triangles is less than a preset threshold, it is determined that the two associated triangles collide;

[0063] Traverse all associated triangles to determine the collision position under this motion state;

[0064] In the present application, the associated triangular facets of the femoral head and the acetabulum can be determined by generating a sphere with a triangular facet of the femoral head as the center and a preset value as the radius (the preset data is greater than the preset threshold corresponding to the collision), and the triangular facet on the acetabulum located in the sphere is the associated triangular facet of the triangular facet on the femoral head. Conversely, the associated triangular facets of a triangular facet on the acetabulum can be determined.

[0065] In the present application, by setting associated triangular facets, the minimum distance calculation between the femoral head and the acetabulum is reduced to the minimum distance calculation between the associated triangular facets, which greatly reduces the corresponding calculation amount.

[0066] In the present application, there are multiple associated triangles of the triangles on the acetabulum / femoral head, so there can be multiple associated triangle pairs; the minimum distance between any two associated triangles is also the minimum distance between the associated triangle pairs.

[0067] Preferably, if there are multiple associated triangular facets of the triangular facet on the acetabulum / femoral head, the radius of the generated sphere is gradually reduced until only one triangular facet intersects with the sphere, and the associated triangular facet is the screened associated triangular facet.

[0068] In this way, by screening, multiple associated triangular faces of the triangular faces on the acetabulum / femoral head are screened into one, thereby greatly reducing the number of associated triangular faces and the corresponding amount of calculation.

[0069] In one embodiment, in the calculation of the minimum distance between two associated triangular facets, two triangular facets are located on the femoral head and the acetabulum, respectively.

[0070] In the present application, the minimum distance between two triangles can be calculated using the GJK algorithm (Gilbert-Johnson-Keerthi).

[0071] In this application, if the minimum distance between two associated triangular facets is less than a preset threshold, the two associated triangular facets are the collision positions. All associated triangular facets are traversed, and the triangular facets that are the collision positions and the number of collisions of each triangular facet are counted as the statistical data of the collision position in the motion state.

[0072] In one implementation, after traversing all associated triangular facets and determining the collision position in the motion state, the step S305 further includes:

[0073] Generate osteotomy surface according to osteotomy position;

[0074] Based on the acetabulum center point and the collision position, a corresponding associated area is generated on the corresponding osteotomy surface;

[0075] The total area of ​​the associated regions in the motion state is counted as the collision data.

[0076] In the present application, the intersection of the osteotomy position and the surface of the hip joint model forms a corresponding osteotomy surface.

[0077] In the present application, the center point of the collision position is determined according to the collision position on the acetabulum; based on the center point of the acetabulum and the center point of the collision position, as well as the collision position, a regular figure of the minimum size including the collision position and with the axis passing through the center point of the acetabulum and the center point of the collision position is generated; the regular figure can be a cylinder or a cuboid; the overlapping part of the regular image and the osteotomy surface is the associated area.

[0078] In this application, the associated regions may overlap with each other, and the overlapping parts are not counted repeatedly.

[0079] In the present application, the total area of ​​the associated regions in the motion state is counted as the collision data in the motion state.

[0080] In the present application, the corresponding associated area is calculated to generate collision data for adjusting the osteotomy position, and then the corresponding osteotomy position is iterated.

[0081] In one embodiment, the step S102 of determining the initial osteotomy position of the acetabulum according to the three-dimensional model of the hip joint includes:

[0082] Generate multiple condition constraints according to the three-dimensional model of the hip joint, wherein the condition constraints at least include joint movement constraints, nerve sensitive area constraints and stress constraints;

[0083] Based on multiple conditional constraints, an osteotomy position that meets the multiple conditional constraints is generated as the initial osteotomy position.

[0084] Among them, the stress constraint is the joint stress distribution: the position of the acetabulum after osteotomy should ensure that the stress distribution of the hip joint is as uniform as possible when loading, and the stress direction is not parallel to the osteotomy surface. This avoids excessive stress on certain areas of the acetabulum and femoral head, thereby reducing the risk of joint degeneration.

[0085] In joint motion constraint, the osteotomy and adjustment position of the acetabulum should ensure that the patient's postoperative joint motion range is large enough, greater than the preset value, to meet daily needs.

[0086] In the nerve sensitive area constraint, the osteotomy position should avoid the distribution area of ​​important nerve bundles around the hip joint to avoid complications caused by postoperative nerve damage. According to the three-dimensional model of the hip joint, the nerve sensitive area is circled on the three-dimensional model, and the osteotomy position is constrained not to intersect with the nerve sensitive area.

[0087] In this application, the specific constraint formulas of joint movement constraints, nerve sensitive area constraints and stress constraints are determined according to actual conditions and will not be repeated in this application.

[0088] In the present application, based on multiple conditional constraints, in the generation of osteotomy positions that meet the multiple conditional constraints, the generated osteotomy positions simultaneously meet the specific constraint formulas of the multiple conditional constraints.

[0089] In one embodiment, the step S104, optimizing the adjustment position and osteotomy position of the acetabulum based on the collision data, includes:

[0090] Based on the collision positions, determining a region with fewer collision positions;

[0091] Adjusting the adjustment position of the acetabulum toward the area with less collision;

[0092] Generate collision condition constraints based on the sum of the areas of the associated regions;

[0093] generating an optimized osteotomy position according to the adjusted acetabulum adjustment position, the multiple condition constraints and the collision condition constraints;

[0094] The collision data is re-simulated and the adjustment position and osteotomy position of the acetabulum are iterated until the preset conditions are met.

[0095] In the present application, the area with fewer collision positions is the area where the femoral head and the acetabulum have sufficient margin; this part of the position is marked, and the center point coordinates of this position are determined; the adjustment position of the acetabulum is adjusted toward the area with fewer collision positions, that is, the bottom center of the acetabulum is rotated toward the center point coordinates.

[0096] The acetabulum is a part of the pelvis and is a hemispherical concave structure. The center of the bottom surface of the acetabulum (also called the acetabular center or acetabular apex) usually refers to the deepest point on the inner side of the acetabulum, that is, the center point of the acetabular depression.

[0097] In the present application, the adjustment position of the acetabulum is adjusted toward the area with fewer collision positions, and the acetabulum opening direction, the location and degree of covering the defect are evaluated simultaneously; it is determined that the acetabulum opening direction, the location and degree of covering the defect meet the corresponding constraints.

[0098] In the present application, the overlapping parts of the associated areas are not taken into account when generating collision condition constraints based on the sum of the areas of the associated areas; the generated collision condition constraints are to select the corresponding osteotomy positions so that the sum of the areas of the corresponding associated areas is minimized.

[0099] In this application, the specific formula of the collision condition constraint is generated according to the actual situation and is not limited in this application.

[0100] In the present application, an optimized osteotomy position is generated according to the adjusted acetabulum adjustment position, the multiple condition constraints and the collision condition constraints, the osteotomy position and the osteotomy surface are recalculated with the adjusted acetabulum adjustment position, and the collision condition constraints are added to the multiple condition constraints. Based on the added condition constraints, an osteotomy position that meets the multiple condition constraints is generated as the optimized osteotomy position.

[0101] An embodiment of the present application provides a periacetabular osteotomy planning device based on collision detection, which is used to execute the periacetabular osteotomy planning method based on collision detection described in the above content of the present application. The periacetabular osteotomy planning device based on collision detection is described in detail below.

[0102] like Figure 3 As shown, the acetabular periarthritis osteotomy planning device based on collision detection comprises:

[0103] A model building module 101 is used to obtain a medical image of the hip joint and build a three-dimensional model of the hip joint;

[0104] A position determination module 102, which is used to determine the initial adjustment position and initial osteotomy position of the acetabulum according to the three-dimensional model of the hip joint;

[0105] A collision detection module 103, which is used to simulate collision data between the femoral head and the acetabulum in different motion states;

[0106] The position optimization module 104 is used to optimize the adjustment position and the osteotomy position of the acetabulum based on the collision data, and determine the best adjustment position and the best osteotomy position of the acetabulum.

[0107] In a specific implementation, the collision detection module 103 is further used to:

[0108] Acquire the current acetabulum adjustment position and osteotomy position; represent the femoral head and acetabulum as a polygonal mesh, wherein the polygonal mesh is composed of a plurality of triangular facets; simulate the motion state of the femoral head; determine the collision position of the femoral head and acetabulum under the motion state; traverse all motion states of the femoral head, and determine the collision position under each motion state as the collision data.

[0109] In a specific implementation, the collision detection module 103 is further used to:

[0110] Determine the associated triangular facets between the femoral head and the acetabulum in the motion state; calculate the minimum distance between the associated triangular facets; if the minimum distance between the two associated triangular facets is less than a preset threshold, determine that the two associated triangular facets collide; traverse all the associated triangular facets to determine the collision position in the motion state;

[0111] In a specific implementation, in the calculation of the minimum distance between two associated triangular facets, two triangular facets are located on the femoral head and the acetabulum, respectively.

[0112] In a specific implementation, the collision detection module 103 is further used to:

[0113] An osteotomy surface is generated according to the osteotomy position; a corresponding associated area is generated on the corresponding osteotomy surface based on the acetabulum center point and the collision position; and the total area of ​​the associated area in the motion state is counted as the collision data.

[0114] In a specific implementation, the location determination module 102 is further configured to:

[0115] According to the three-dimensional model of the hip joint, multiple conditional constraints are generated, and the conditional constraints at least include joint movement constraints, nerve sensitive area constraints and stress constraints; based on the multiple conditional constraints, an osteotomy position that meets the multiple conditional constraints is generated as the initial osteotomy position.

[0116] In a specific implementation, the location optimization module 104 is further configured to:

[0117] Based on the collision position, determine the area with fewer collision positions; adjust the adjustment position of the acetabulum toward the area with fewer collision positions; generate a collision condition constraint based on the sum of the areas of the associated areas; generate an optimized osteotomy position according to the adjusted acetabulum adjustment position, the multiple condition constraints and the collision condition constraints; re-simulate the collision data and iterate the adjustment position and osteotomy position of the acetabulum until the preset conditions are met.

[0118] The acetabular osteotomy planning device based on collision detection provided in the above-mentioned embodiment of the present application has a corresponding relationship with the acetabular osteotomy planning method based on collision detection provided in the embodiment of the present application. Therefore, the specific content in the device has a corresponding relationship with the acetabular osteotomy planning method. The specific content can refer to the records in the acetabular osteotomy planning method, and will not be repeated in this application.

[0119] The acetabular osteotomy planning device based on collision detection provided in the above-mentioned embodiment of the present application and the acetabular osteotomy planning method based on collision detection provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0120] The above describes the internal functions and structure of the acetabular peri-osteotomy planning device based on collision detection, such as Figure 4 As shown, in practice, the collision detection-based periacetabular osteotomy planning device can be implemented as an electronic device, including: a memory 301 and a processor 303 .

[0121] The memory 301 may be configured to store programs.

[0122] In addition, the memory 301 may also be configured to store various other data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on the electronic device, contact data, phone book data, messages, pictures, videos, etc.

[0123] The memory 301 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0124] The processor 303 is coupled to the memory 301 and is configured to execute a program in the memory 301 to:

[0125] Obtain medical images of hip joint and construct a three-dimensional model of hip joint;

[0126] According to the three-dimensional model of the hip joint, determine the initial adjustment position and initial osteotomy position of the acetabulum;

[0127] Simulate the collision data between the femoral head and acetabulum in different motion states;

[0128] Based on the collision data, the adjustment position and the osteotomy position of the acetabulum are optimized to determine the best adjustment position and the best osteotomy position of the acetabulum.

[0129] In a specific implementation, the processor 303 is further configured to:

[0130] Acquire the current acetabulum adjustment position and osteotomy position; represent the femoral head and acetabulum as a polygonal mesh, wherein the polygonal mesh is composed of a plurality of triangular facets; simulate the motion state of the femoral head; determine the collision position of the femoral head and acetabulum under the motion state; traverse all motion states of the femoral head, and determine the collision position under each motion state as the collision data.

[0131] In a specific implementation, the processor 303 is further configured to:

[0132] Determine the associated triangular facets between the femoral head and the acetabulum in the motion state; calculate the minimum distance between the associated triangular facets; if the minimum distance between the two associated triangular facets is less than a preset threshold, determine that the two associated triangular facets collide; traverse all the associated triangular facets to determine the collision position in the motion state;

[0133] In a specific implementation, in the calculation of the minimum distance between two associated triangular facets, two triangular facets are located on the femoral head and the acetabulum, respectively.

[0134] In a specific implementation, the processor 303 is further configured to:

[0135] An osteotomy surface is generated according to the osteotomy position; a corresponding associated area is generated on the corresponding osteotomy surface based on the acetabulum center point and the collision position; and the total area of ​​the associated area in the motion state is counted as the collision data.

[0136] In a specific implementation, the processor 303 is further configured to:

[0137] According to the three-dimensional model of the hip joint, multiple conditional constraints are generated, and the conditional constraints at least include joint movement constraints, nerve sensitive area constraints and stress constraints; based on the multiple conditional constraints, an osteotomy position that meets the multiple conditional constraints is generated as the initial osteotomy position.

[0138] In a specific implementation, the processor 303 is further configured to:

[0139] Based on the collision position, determine the area with fewer collision positions; adjust the adjustment position of the acetabulum toward the area with fewer collision positions; generate a collision condition constraint based on the sum of the areas of the associated areas; generate an optimized osteotomy position according to the adjusted acetabulum adjustment position, the multiple condition constraints and the collision condition constraints; re-simulate the collision data and iterate the adjustment position and osteotomy position of the acetabulum until the preset conditions are met.

[0140] In the present application, the processor is also specifically used to execute all processes and steps of the above-mentioned periacetabular osteotomy planning method based on collision detection. The specific content can be referred to the records in the periacetabular osteotomy planning method, which will not be repeated in this application.

[0141] In this application, Figure 4 Only some components are shown schematically, which does not mean that the electronic device only includes Figure 4 Components shown.

[0142] The electronic device provided in this embodiment is based on the same inventive concept as the acetabular periarthritis osteotomy planning method based on collision detection provided in the embodiment of the present application, and has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0143] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0144] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0147] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0148] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0149] The present application also provides a computer-readable storage medium corresponding to the collision detection-based periacetabular osteotomy planning method provided in the aforementioned embodiment, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the collision detection-based periacetabular osteotomy planning method provided in any of the aforementioned embodiments.

[0150] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0151] The computer-readable storage medium provided in the above-mentioned embodiment of the present application and the acetabular periostectomy planning method based on collision detection provided in the embodiment of the present application are based on the same inventive concept, and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0152] It should be noted that in the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0153] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0154] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for planning periacetabular osteotomy based on collision detection, characterized in that: include: Obtain medical images of hip joint and construct a three-dimensional model of hip joint; According to the three-dimensional model of the hip joint, determine the initial adjustment position and initial osteotomy position of the acetabulum; Simulate the collision data between the femoral head and acetabulum in different motion states; Based on the collision data, the adjustment position and the osteotomy position of the acetabulum are optimized to determine the best adjustment position and the best osteotomy position of the acetabulum.

2. The method for planning periacetabular osteotomy based on collision detection according to claim 1, characterized in that: The simulated collision data between the femoral head and the acetabulum in different motion states include: Obtain the current acetabulum adjustment position and osteotomy position; The femoral head and the acetabulum are represented as polygonal meshes, wherein the polygonal meshes are composed of a plurality of triangular facets; Simulate the motion state of the femoral head; Determine the collision position between the femoral head and acetabulum in this motion state; All movement states of the femoral head are traversed, and the collision position in each movement state is determined as the collision data.

3. The method for planning periacetabular osteotomy based on collision detection according to claim 2, characterized in that: Determining the collision position of the femoral head and the acetabulum in the motion state includes: Determine the associated triangular surface between the femoral head and the acetabulum in this motion state; Calculate the minimum distance between two associated triangles; If the minimum distance between two associated triangular facets is less than a preset threshold, it is determined that the two associated triangular facets collide; Traverse all associated triangles to determine the collision position under this motion state.

4. The method for planning periacetabular osteotomy based on collision detection according to claim 3, characterized in that: In the calculation of the minimum distance between two associated triangular facets, two triangular facets are located on the femoral head and the acetabulum, respectively.

5. The method for planning periacetabular osteotomy based on collision detection according to claim 3, characterized in that: After traversing all associated triangular facets and determining the collision position in the motion state, the method further includes: Generate osteotomy surface according to osteotomy position; Based on the acetabulum center point and the collision position, a corresponding associated area is generated on the corresponding osteotomy surface; The total area of ​​the associated regions in the motion state is counted as the collision data.

6. The method for planning periacetabular osteotomy based on collision detection according to claim 2, characterized in that: Determining the initial osteotomy position of the acetabulum according to the three-dimensional model of the hip joint includes: Generate multiple condition constraints according to the three-dimensional model of the hip joint, wherein the condition constraints at least include joint movement constraints, nerve sensitive area constraints and stress constraints; Based on multiple conditional constraints, an osteotomy position that meets the multiple conditional constraints is generated as the initial osteotomy position.

7. The method for planning periacetabular osteotomy based on collision detection according to claim 6, characterized in that: The step of optimizing the adjustment position and osteotomy position of the acetabulum based on the collision data comprises: Based on the collision positions, determining a region with fewer collision positions; Adjusting the adjustment position of the acetabulum toward the area with less collision position; Generate collision condition constraints based on the sum of the areas of the associated regions; generating an optimized osteotomy position according to the adjusted acetabulum adjustment position, the multiple condition constraints and the collision condition constraints; The collision data is re-simulated and the adjustment position and osteotomy position of the acetabulum are iterated until the preset conditions are met.

8. A collision detection-based periacetabular osteotomy planning device, characterized in that: include: A model building module, which is used to obtain medical images of the hip joint and build a three-dimensional model of the hip joint; A position determination module, which is used to determine the initial adjustment position and initial osteotomy position of the acetabulum according to the three-dimensional model of the hip joint; A collision detection module, which is used to simulate the collision data between the femoral head and the acetabulum in different motion states; A position optimization module is used to optimize the adjustment position and osteotomy position of the acetabulum based on the collision data, and determine the best adjustment position and best osteotomy position of the acetabulum.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store programs; The processor, coupled to the memory, is configured to execute the program to: Obtain medical images of hip joint and construct a three-dimensional model of hip joint; According to the three-dimensional model of the hip joint, determine the initial adjustment position and initial osteotomy position of the acetabulum; Simulate the collision data between the femoral head and acetabulum in different motion states; Based on the collision data, the adjustment position and the osteotomy position of the acetabulum are optimized to determine the best adjustment position and the best osteotomy position of the acetabulum.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the acetabular periarthritis osteotomy planning method based on collision detection as described in any one of claims 1-7.

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