An orthopedic endoprosthesis fitting analysis method based on a three-dimensional model

Abstract

This invention belongs to the field of 3D model technology, specifically relating to a method for orthopedic implant fitting analysis based on a 3D model. This invention establishes a voxel model containing bone density attributes by performing voxelization reconstruction on bone images and extracting trabecular bone features to construct a microscopic topological weight field. Simultaneously, the implant model is meshed to generate a normal vector field, and tensor calculations are used to map the topological semantics onto the implant surface to form a virtual mechanical texture, converting microscopic bone strength into macroscopic mechanical fitting features. In interactive analysis, the topological semantic overlap between the two is calculated in real time, and the implant pose is automatically fine-tuned through a deformation-driven mechanism. This invention achieves deep matching based on biomechanical attributes by introducing topological semantic extraction, providing intuitive feedback on the distribution of mechanical support and improving fitting accuracy and stability.

Description

Technical Field

[0001] This invention belongs to the field of three-dimensional model technology, specifically relating to a method for orthopedic implant fitting analysis based on three-dimensional models. Background Technology

[0002] With the rapid development of digital medical technology, 3D reconstruction technology based on medical imaging is playing an increasingly important role in orthopedic surgical planning and implant fitting. Constructing high-precision 3D bone models using imaging data such as computed tomography (CT) scans provides clinicians with intuitive anatomical references, assisting in implant selection and spatial arrangement. 3D model fitting analysis, aiming to improve the stability and functional recovery of implants after implantation through precise quantification of the patient's skeletal geometry, has become a core research direction in precision orthopedic medicine.

[0003] Three-dimensional model-based orthopedic implant fitting analysis primarily focuses on spatially registering the digital model of the implant with the reconstructed model of the patient's skeleton. The process typically utilizes point cloud registration or surface fitting algorithms to calculate the geometrical overlap between the implant surface and the anatomical surface of the bone, determining the optimal implantation position and angle. This geometry-based analysis method aims to achieve a high degree of fit between the implant and the bone recipient in terms of anatomical contours, providing a morphological basis for surgical path design.

[0004] Existing fit analysis methods rely excessively on the external uniformity of geometry, failing to adequately consider the complex biomechanical properties within the bone. Traditional methods treat bone as a homogeneous geometric entity, neglecting the crucial influence of trabecular bone topology on implant screw holding force. This leads to severe mechanical distortions in fit results, especially in special cases such as osteoporosis. Current mechanical assessment methods are typically treated as independent post-processing steps, making it difficult to achieve real-time characterization and interactive feedback of mechanical parameters during the 3D model construction phase. This prevents a direct understanding of stress distribution patterns at the implant-bone interface. Furthermore, the lack of a joint characterization mechanism for macroscopic geometry and microscopic topology hinders the system's ability to automatically optimize implant spatial posture under complex stress environments, increasing the risk of postoperative implant loosening or failure. Summary of the Invention

[0005] The purpose of this invention is to provide a method for orthopedic implant fitting analysis based on a three-dimensional model, which can effectively solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a method for orthopedic implant fitting analysis based on a three-dimensional model, comprising the following specific steps:

[0007] Step 1: Acquire medical image data of the bone region to be analyzed and perform preprocessing. Map the gray values ​​representing tissue density in the image to a three-dimensional spatial grid using a voxelization reconstruction algorithm to establish a bone voxel model that includes the association between three-dimensional spatial coordinates and bone density attributes.

[0008] Step 2: Construct a microscopic topological weight field. By extracting local structural features from the bone voxel model, identify the distribution density and arrangement direction of trabeculae inside the bone, and generate topological semantic data for quantitatively describing the internal support capacity of the bone.

[0009] Step 3: Establish a three-dimensional geometric model of the inner plant, and mesh the surface of the three-dimensional geometric model of the inner plant to generate a normal vector field composed of several discrete surface elements and their corresponding surface normal vectors;

[0010] Step 4: Generate virtual mechanical textures. Based on tensor calculation methods, map the topological semantic data in the microscopic topological weight field to the surface of the three-dimensional geometric model of the endophyte, and convert the bone strength features at the microscopic level into mechanical adaptation features at the macroscopic level.

[0011] Step 5: Perform interactive adaptation analysis. During the relative spatial transformation between the three-dimensional geometric model of the implant and the skeletal voxel model, calculate the topological semantic overlap between the two in the contact area and implantation path in real time.

[0012] Step 6: Implement pose fine-tuning and adaptation feedback. Based on the comparison results of the topological semantic overlap degree and the preset threshold, automatically adjust the spatial pose of the three-dimensional geometric model of the endophyte in the skeletal voxel model through the deformation driving mechanism.

[0013] Preferably, in step 1, the medical image data of the bone region to be analyzed is acquired using a computed tomography (CT) scanner, and the acquired image data consists of tomographic slices. During preprocessing, the system performs isotropic resampling on the original tomographic slices to ensure that the sampling step size is completely consistent along each axis in the three-dimensional coordinate system. The voxelization reconstruction algorithm defines a spatial lattice composed of uniform cubic units in three-dimensional space, and allocates the two-dimensional pixel grayscale values ​​of each tomographic slice to the corresponding three-dimensional voxel units according to a spatial mapping relationship. For each voxel unit, the system stores its center coordinates in three-dimensional space and a normalized scalar value, which is linearly positively correlated with the bone density at the corresponding location.

[0014] Preferably, in step 1, to ensure the accuracy of the bone boundaries, the preprocessing process further includes implementing an image segmentation algorithm based on a combination of threshold segmentation and region growing. The system first sets a preset grayscale range based on the typical grayscale distribution range of human skeletal tissue in computed tomography images, and removes non-skeletal tissue voxels outside this preset grayscale range. Subsequently, a region growing algorithm is used to cluster connected bone voxels to construct a complete, noise-free 3D skeletal model.

[0015] Preferably, in step 2, the process of constructing the micro-topological weight field involves deep semantic extraction of the bone's micro-topological structure. The system defines an analysis sphere of a preset radius around each voxel unit of the bone voxel model and calculates the gradient covariance matrix of all voxel grayscale values ​​within this analysis sphere. By performing eigenvalue decomposition on this gradient covariance matrix, three mutually orthogonal eigenvectors and their corresponding eigenvalues ​​are obtained. The magnitude of the eigenvalues ​​reflects the non-uniformity of bone tissue distribution within a local region. When the difference between eigenvalues ​​exceeds a predetermined proportion, the system determines that the region exhibits significant anisotropy and defines the direction of the principal eigenvector as the growth direction of the trabeculae.

[0016] Preferably, in step 2, the generated topological semantic data includes a second-order symmetric tensor, which integrates a local bone density scalar and a trabecular bone direction vector. The system uses this second-order symmetric tensor to assign a topological weight value to each voxel unit. This topological weight value is used to characterize the structural stability of a spatial point under external force. Higher topological weight values ​​are assigned to regions with high bone density and trabecular bone alignment aligned with the preset force direction; while lower topological weight values ​​are assigned to regions with osteoporosis or disordered trabecular bone structure.

[0017] Preferably, in step 3, the process of establishing the three-dimensional geometric model of the endplant is completed by importing a preset standardized digital model. Before generating the normal vector field, the system uses a triangular meshing algorithm to discretize the surface of the endplant, dividing its surface into tens of thousands of fine triangular elements. For each triangular element, the system calculates the unit normal vector of the triangular element based on the spatial coordinates of its three vertices. This unit normal vector points from the inside of the model to the outside. The set of all unit normal vectors constitutes a normal vector field covering the entire surface of the endplant, which is used for subsequent calculations of the mechanical interaction between the endplant and the skeleton.

[0018] Preferably, in step 4, the process of generating the virtual mechanical texture is a cross-scale feature mapping. The system performs a dot product operation on the normal vector of the surface element of the 3D geometric model of the inner plant and the direction tensor in the bone topological weight field. The specific logic of this dot product operation is as follows: for each sampling point on the surface of the inner plant, its corresponding spatial coordinates in the bone voxel model are retrieved, and the topological semantic tensor at the coordinates is extracted. By calculating the product of the surface normal vector and the topological semantic tensor, a scalar value representing the holding force intensity of the sampling point is obtained. These scalar values ​​are overlaid on the surface of the 3D geometric model of the inner plant in a color mapping manner to form a virtual mechanical texture that can intuitively reflect the distribution of mechanical support inside the bone.

[0019] Preferably, in step 4, the generated virtual mechanical texture includes not only static intensity distribution but also dynamic adaptation potential evaluation. The system weights the corresponding mechanical texture according to the preset screw hole positions or load-bearing areas on the implant. On the potential contact surface between the implant and the bone, the numerical value of the virtual mechanical texture directly represents the biomechanical stability that the area can provide after implantation.

[0020] Preferably, in step 5, during interactive adaptation analysis, the system monitors the spatial relationship between the inner plant model and the skeletal model in real time. When the operator or automatic control algorithm applies translation or rotation transformations to the inner plant model, the system updates the relative coordinate system of the two at a predetermined time frequency. Within each update cycle, the system identifies the spatial overlap area between the inner plant and skeletal models and performs topological semantic overlap calculations for all voxel units within the area.

[0021] Preferably, in step 5, the calculation logic for the topological semantic overlap is to perform spatial integration on the surface normal vector field of the implant and the topological weight field of the bone. Specifically, the system calculates the topological weighted sum between all preset load-bearing parts of the implant and their corresponding bone voxels. The magnitude of this topological weighted sum depends on the degree of matching between the implant geometry and the microscopic topological structure inside the bone. If the angle between the screw axis direction of the implant and the main growth direction of the trabecular bone inside the bone is less than a preset angle threshold, the overlap score of the region will be assigned a higher weight.

[0022] Preferably, in step 6, the posture fine-tuning and adaptation feedback mechanism includes two levels: real-time visual warning and automatic optimization. At the visual warning level, the system displays the adaptation quality in real-time on the 3D display interface using color gradients based on the calculated topological semantic overlap. When the topological weight of the bone region corresponding to the screw hole location of the implant is lower than a preset safety threshold, the region will be highlighted with a warning color, indicating a potential risk of implant loosening at the current location.

[0023] Preferably, in step 6, the deformation-driven mechanism achieves automatic attitude fine-tuning by executing a gradient-ascending local optimization algorithm. The system uses the current topological semantic overlap as the objective function and calculates the gradient of the objective function in the six degrees of freedom of the inner plant. Based on the gradient direction, the system drives the inner plant model to perform small incremental displacements and rotations in three-dimensional space until the objective function reaches a local maximum. During this process, the inner plant model, as if driven by a virtual force field, automatically moves towards a spatial location with higher internal skeletal support strength and better topological matching.

[0024] Preferably, the method for orthopedic implant fitting analysis based on a three-dimensional model further includes establishing a fitting evaluation report. This report summarizes multiple logical indicators from the fitting process, including the anatomical fit between the implant and the bone surface, the average topological weight of the load-bearing area, the bone density gradient distribution along the screw implantation path, and the expected mechanical stability score. These indicators are all derived through quantitative statistical analysis of voxel data and topological semantics within the three-dimensional model.

[0025] Preferably, the trabecular growth direction identified in step 2 is also used to analyze the force transmission path of the bone. The system utilizes fluid simulation logic to simulate the trabecular structure as a force streamline, calculating how stress is transmitted in the bone voxel model under preset physiological loads. After implantation, the system recalculates the changes in stress distribution to ensure that the introduction of the implant does not lead to excessive local stress concentration or stress shielding effects.

[0026] Preferably, the three-dimensional geometric model of the implant established in step 3 also includes attribute-defined screw components. Each screw component is assigned specific thread topology parameters. During the adaptation analysis, the system not only calculates the pose of the implant body but also performs independent topological semantic analysis on the screwing trajectory of each screw. By calculating the cumulative topological weights of the bone voxels traversed along the screw path, the initial stability of each screw is predicted.

[0027] Preferably, the real-time calculation process in step 5 is handled using a parallel computing framework. The system divides the skeletal voxel model into multiple sub-regions and uses multiple processing units to simultaneously perform spatial retrieval and numerical calculations of topological semantic overlap. This approach ensures that the data update latency is below a predetermined threshold during complex 3D model interactions, guaranteeing the real-time nature of operation feedback.

[0028] Preferably, the automatic adjustment logic in step 6 is also constrained by anatomical boundaries. When performing deformation-driven fine-tuning, the system sets a maximum permissible deviation range. If the optimal position indicated by the optimization algorithm exceeds clinically acceptable anatomical boundaries or touches a pre-defined neurovascular protection area, the system will forcibly terminate displacement in that direction and search for a suboptimal solution within the constraints.

[0029] Preferably, the orthopedic implant fitting analysis method based on a three-dimensional model also involves predicting and simulating postoperative fitting effects. After determining the optimal pose, the system simulates the fusion process between the implant and the bone by modifying the attribute values ​​at the corresponding positions in the bone voxel model. By performing cyclic topological evolution calculations over a predetermined time span, the system simulates the remodeling process of bone tissue under implant stimulation and predicts long-term mechanical stability trends.

[0030] Preferably, the construction of the topological weight field also incorporates enhancement processing based on multi-scale feature fusion. Based on the extracted voxel gradients, the system further analyzes skeletal morphological features at a larger spatial scale, such as the thickness distribution of the cortical bone and the geometric contour of the metaphysis. By nonlinearly fusing microscopic trabecular bone features with macroscopic bone density distribution, a more comprehensive topological semantic field reflecting the biomechanical properties of the skeleton is generated.

[0031] Preferably, the material properties of the endophyte model are defined as a linear isotropic elastic body during interactive adaptation analysis. While calculating the overlap, the system also estimates the minute deformation of the endophyte under stress. Although the deformation is small, when calculating the topological semantic overlap, the system dynamically corrects the surface normal vector field based on the stress deformation trend of the endophyte to improve the accuracy of the analysis results under physiological load conditions.

[0032] Preferably, the orthopedic implant fitting analysis method based on a 3D model also supports comparative analysis of multiple types of implants. The system can simultaneously load multiple implant models of different models or specifications, and perform the aforementioned topological semantic mapping and overlap calculation on each implant model. By comparing the optimal overlap values ​​of each model within the same bone region, a quantitative decision-making basis is provided for clinical selection.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. This invention breaks through the limitation of traditional orthopedic 3D models serving only as geometric visual containers by introducing a topological semantic extraction algorithm. By transforming grayscale information in medical images into a topological weight field with mechanical meaning, it achieves a joint representation of the microscopic supporting structure and macroscopic geometric morphology within the bone. This design allows fit analysis to evolve from simple geometric shape matching to structural matching based on biomechanical properties, improving the accuracy of fit results in real mechanical environments.

[0035] 2. This invention generates virtual mechanical textures, visually mapping the previously invisible internal mechanical properties of bone onto the surface of the implant model. This visualization mechanism provides doctors with intuitive mechanical feedback, enabling them to clearly identify weak and strong areas of implant holding force during the surgical planning stage, and to predict and avoid the risk of postoperative implant loosening or implantation failure.

[0036] 3. The deformation-driven mechanism and automatic optimization algorithm employed in this invention enable intelligent adjustment of the spatial pose of the implant. Compared to traditional manual interaction, the system can automatically find the optimal implantation location based on the topological semantic overlap gradient, shortening surgical planning time and reducing uncertainties caused by human factors.

[0037] 4. This invention integrates mechanical analysis directly into the construction and interaction process of the 3D model, rather than as a separate post-processing step. This real-time mechanical evaluation mechanism enables the adaptation analysis process to provide immediate feedback, quickly identifying the balance point between geometric fit and mechanical stability in complex anatomical structures, providing reliable technical support for precision orthopedic medicine.

[0038] 5. The method of the present invention is not only applicable to conventional orthopedic surgical planning, but also provides analytical accuracy far exceeding that of traditional geometric registration methods when facing special cases such as osteoporosis or complex deformities, through refined modeling of the microscopic topology, thereby improving the success rate of surgery and the quality of patient rehabilitation in complex cases. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0040] Figure 2 This is a schematic diagram of the cross-scale feature mapping principle framework between the microscopic topological weight field of the skeleton and the normal vector field of the inner plant surface in this invention;

[0041] Figure 3 This is a flowchart illustrating the main stages of skeleton voxel model reconstruction and microscopic topological semantic extraction in this invention.

[0042] Figure 4This is a schematic diagram of the multi-level interaction relationship and data flow between the endophyte and skeletal voxel models in this invention;

[0043] Figure 5 This is a schematic diagram illustrating the principle of the automatic adjustment and deformation driving mechanism of the spatial pose of internal plants based on the topological semantic overlap gradient in this invention. Detailed Implementation

[0044] Example 1, Reference Figures 1 to 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0045] In a method for orthopedic implant fitting analysis based on a 3D model, step 1 involves acquiring medical image data of the bone region to be analyzed and performing preprocessing. A voxelization reconstruction algorithm maps the gray values ​​representing tissue density in the images to a 3D spatial grid, establishing a bone voxel model that includes the correlation between 3D spatial coordinates and bone density attributes. Specifically, the acquisition of medical image data of the bone region to be analyzed is performed using a computed tomography (CT) scanner. The acquired image data consists of a series of tomographic slices with specific gray-level gradients. These tomographic slices are stored in a standard digital imaging and communication medical format and contain anatomical information of the patient's bone tissue.

[0046] During preprocessing, the system performs isotropic resampling on the original tomographic slices because the original scan data often has inconsistencies in inter-slice spacing and intra-slice pixel spacing. The system uses linear interpolation or cubic spline interpolation algorithms to adjust the asymmetric voxels into a cubic form, ensuring that the sampling step size is completely consistent along all axes in the three-dimensional coordinate system, for example, set to 0.5 mm or smaller, thus eliminating geometric distortions during 3D reconstruction.

[0047] In step 1 above, the voxelization reconstruction algorithm defines a spatial lattice composed of uniform cubic units in three-dimensional space, and allocates the two-dimensional pixel grayscale values ​​of each tomographic slice to the corresponding three-dimensional voxel units according to the spatial mapping relationship. The system first establishes a three-dimensional bounding box covering the entire target area and divides it into hundreds of millions of tiny voxels. For each voxel unit, the system stores its center coordinates in three-dimensional space and its normalized scalar value. The calculation logic of this scalar value is to map the original Haugh unit grayscale value to the interval between 0 and 1, and this scalar value has a linear positive correlation with the bone density at the corresponding location.

[0048] The described "voxelation reconstruction algorithm" includes a precise spatial coordinate mapping and grayscale interpolation process. For the input medical image tomographic slice sequence, the system first constructs a three-dimensional Cartesian grid, whose grid point coordinates... From the target isotropic voxel size Decision. For any target voxel center coordinates In the original non-isotropic data space The corresponding positions in the image are determined through a linear mapping relationship. The spatial coordinates of the original data are determined by the pixel spacing in the image file metadata. and interlayer spacing Definition. The "linear interpolation algorithm" specifically refers to trilinear interpolation. For the target voxel V Its normalized scalar value The calculation is as follows:

[0049]

[0050] in, This represents the grayscale value of the original image at continuous coordinates (obtained through intra-layer bilinear interpolation and inter-layer linear interpolation). The weights are for trilinear interpolation, derived from the target point. The relative position within the original voxel cell determines the position. This is a normalized bone mineral density scalar, which is equivalent to the original CT Hardy unit. The mapping relationship is as follows: . These are the upper and lower limits of the preset grayscale range related to bone density.

[0051] To ensure the accuracy of bone boundaries, the preprocessing also includes implementing an image segmentation algorithm based on a combination of threshold segmentation and region growing. The system first sets a preset grayscale range based on the typical grayscale distribution range of human skeletal tissue in computed tomography images, for example, between 200 and 2000 Haugh units, and removes non-skeletal tissue voxels such as fat, muscle, or air outside this range. Then, using a region growing algorithm, high-density regions selected by the user or automatically identified are used as seed points to cluster connected bone voxels. During clustering, the system checks the grayscale gradient between adjacent voxels; if the gradient value is lower than a preset abrupt change threshold, they are classified as the same skeletal entity, constructing a complete, noise-free 3D skeletal model.

[0052] In the above method, step 2 involves constructing a microscopic topological weight field. This is achieved by extracting local structural features from the skeletal voxel model, identifying the distribution density and orientation of trabecular bone within the bone, and generating topological semantic data to quantify the bone's internal support capacity. The process of constructing the microscopic topological weight field involves deep semantic extraction of the skeletal microscopic topological structure. The system defines an analysis sphere with a preset radius around each voxel unit of the skeletal voxel model; the radius is typically set to 3 to 5 times the characteristic length of the trabecular bone at the corresponding bone location. The system calculates the gradient covariance matrix of the grayscale values ​​of all voxels within the analysis sphere.

[0053] The construction of the "gradient covariance matrix" is achieved through the following mathematical operations. For any voxel in the skeletal voxel model... Define a radius around it. spherical neighborhood First, the central difference method is used to calculate the value of each voxel in the neighborhood. Gray-level gradient vectors in three orthogonal directions The formulas for calculating each component of the gradient vector are as follows:

[0054]

[0055] in, Voxel representation exist The gray values ​​of adjacent voxels in the direction. , , Let be the step size for the isotropic voxel model. Subsequently, the "outer product operation and local weighted summation" is defined as follows: in the neighborhood... The outer product matrix of the gradients of all voxels is then averaged using a Gaussian weighted average to obtain the result located at... 3×3 symmetric gradient covariance matrix of a point :

[0056]

[0057] in, This represents the outer product operation of vectors; Therefore A Gaussian kernel function centered at the center is used to assign higher weights to voxels closer to the center. Eigenvalue decomposition yields three eigenvalues. and corresponding feature vectors , , The criterion that "the difference between eigenvalues ​​exceeds a predetermined proportion" is specifically determined by calculating the anisotropy coefficient. To achieve:

[0058]

[0059] when When the value exceeds a preset threshold (e.g., 0.5), it is determined that there is significant anisotropy in the local region, and the main feature vector is... The direction is defined as the main growth direction of the trabeculae.

[0060] The system first calculates the gray-level partial derivatives of each voxel in the three dimensions of X, Y, and Z, and then performs an outer product operation on these partial derivatives and performs a local weighted summation to obtain a 3×3 symmetric matrix.

[0061] In step 2, eigenvalue decomposition is performed on the symmetric matrix to obtain three mutually orthogonal eigenvectors and their corresponding eigenvalues. The magnitude of the eigenvalues ​​reflects the non-uniformity of bone tissue distribution within a local region. The system arranges the eigenvalues ​​in descending order. If the largest eigenvalue is significantly larger than the other two eigenvalues, and the difference between the eigenvalues ​​exceeds a predetermined proportion, such as more than twice the difference, the system determines that the region exhibits significant anisotropy and defines the direction of the principal eigenvector as the growth direction of the trabeculae.

[0062] The generated topological semantic data contains a second-order symmetric tensor that integrates a local bone density scalar and a trabecular bone direction vector. The system uses this second-order symmetric tensor to assign a topological weight value to each voxel unit. The calculation logic for this topological weight value involves multiplying the local bone density scalar with a directional consistency coefficient. Higher topological weight values ​​are assigned to regions with high bone density and trabecular bone alignment aligned with the preset force direction; while lower topological weight values ​​are assigned to regions of osteoporosis, cystic changes, or disordered trabecular bone structure. This approach transforms the 3D model from an empty geometric shell into a semantic entity filled with mechanical properties.

[0063] The "second-order symmetric tensor" The construction is to scalarize local bone mineral density. Information related to trabecular bone direction The fusion is performed. Its mathematical form is a projection tensor, defined as:

[0064]

[0065] This tensor describes the local skeletal material along the trabecular direction of the main bone. The mechanical bearing capacity. The "topology weight value" Used for quantifying character voxels In the preset force direction The structural stability is calculated using the following formula:

[0066]

[0067] in, Let be a unit vector representing the principal force direction of the bone region under physiological load (e.g., the longitudinal pressure direction of the femoral neck). This formula indicates that a high topological weight value is assigned when the local bone density is high and the trabecular bone direction is consistent with the principal force direction, and vice versa. This weight value constitutes the basic unit of the topological semantic field.

[0068] In step 2 above, the identified trabecular growth direction is also used to analyze the force transmission path of the bone. The system utilizes fluid simulation logic to simulate the trabecular structure as a flow line under stress, calculating how stress is transmitted in the bone voxel model under preset physiological loads. This simulation process is achieved by iteratively solving the stress balance equation, ensuring that the topological weight at each point reflects its contribution to the overall load-bearing structure. Furthermore, the construction of the topological weight field incorporates enhancement processing based on multi-scale feature fusion. Based on the extracted voxel gradients, the system further analyzes bone morphological features at a larger spatial scale, such as the thickness distribution of the cortical bone and the geometric contour of the metaphysis. By nonlinearly fusing microscopic trabecular features with macroscopic bone density distribution, for example using weighted averaging or fuzzy logic judgment, a more comprehensive topological semantic field reflecting the biomechanical properties of the bone is generated.

[0069] Step 3 involves establishing a three-dimensional geometric model of the inner plant and meshing its surface to generate a normal vector field composed of discrete facets and their corresponding surface normal vectors. The establishment of the three-dimensional geometric model is accomplished by importing a pre-defined standardized digital model. Before generating the normal vector field, the system uses a triangular meshing algorithm to discretize the surface of the inner plant, dividing it into tens of thousands of fine triangular facets. For each triangular facet, the system calculates its unit normal vector based on the spatial coordinates of its three vertices. Specifically, this is done by calculating the cross product of the vectors of the two sides of the triangle and normalizing it. This unit normal vector points from the inside of the model to the outside. The set of all unit normal vectors constitutes the normal vector field covering the entire surface of the inner plant.

[0070] In step 3, the established 3D geometric model of the implant also includes attribute-defined screw components. Each screw component is assigned specific thread topology parameters, such as pitch, thread angle, and effective diameter. During the adaptation analysis, the system not only calculates the pose of the implant body but also performs independent topological semantic analysis on the screwing trajectory of each screw. This analysis simulates the screw rotation and screwing process, calculating the set of voxels swept by each point on the screw surface along the motion path.

[0071] The material properties of the endophyte model are defined as a linear isotropic elastic body. While calculating the overlap, the system also estimates the minute deformation of the endophyte under skeletal reaction forces. Although this deformation is small in absolute value, when calculating the topological semantic overlap, the system dynamically corrects the surface normal vector field based on the deformation trend of the endophyte under stress, that is, it rotates the normal vector by a corresponding minute angle to improve the accuracy of the analysis results under physiological load conditions.

[0072] The "deformation estimation" employs a simplified elasticity model. The surface normal vector of the inner plant is... The dynamic deflection correction is equivalent to calculating the normal vector due to the normal force on the plant at the contact point. The action produces a tiny rotation and a new direction. Assume local deformation of the endophyte leads to a certain sampling point on the surface... Displacement Then the rotation angle of the normal vector at that point. Approximation and local curvature It is related to displacement. A simplified calculation model that can be implemented is: the corrected normal vector. This can be achieved by modifying the original normal vector. Apply a tangential axis The rotation yields:

[0073]

[0074] in, It is about the axis Rotation The Rodrigues rotation matrix of the angle; The elastic deformation at the contact point is proportional to the elastic deformation. It can be set to , This is a preset empirical scaling factor. This correction allows the normal vector to follow the micro-deformation trend of the inner plant surface, improving the accuracy of mechanical matching calculations.

[0075] In the above method, step 4 generates a virtual mechanical texture by mapping the topological semantic data in the microscopic topological weight field to the surface of the three-dimensional geometric model of the inner plant using tensor computation methods, thus converting the bone strength features at the microscopic level into mechanical adaptation features at the macroscopic level. The process of generating the virtual mechanical texture is a cross-scale feature mapping. The system performs a dot product operation between the normal vector of the surface element of the three-dimensional geometric model of the inner plant and the direction tensor in the bone topological weight field. The specific logic of the dot product operation is as follows: for each sampling point on the surface of the inner plant, the system retrieves its corresponding spatial coordinates in the bone voxel model.

[0076] Since the implant model uses floating-point coordinates, the system employs a trilinear interpolation algorithm to extract the topological semantic tensor at the coordinates. By calculating the product of the surface normal vector and the topological semantic tensor, a scalar value representing the holding force strength at the sampling point is obtained. The magnitude of this scalar value reflects the degree of matching between that point on the implant surface and the internal support structure of the skeleton.

[0077] The "product of surface normal vector and topological semantic tensor" is a tensor shrinking operation. For sampling points on the inner plant surface... Its unit normal vector is The system retrieves the topological semantic tensor T(v_s) at the corresponding spatial coordinates in the skeletal voxel model. This represents the scalar value characterizing the holding force intensity at that point. Calculated using the following formula:

[0078]

[0079] This formula is a quadratic operation between a vector and a second-order tensor. Its physical meaning is to calculate the effective "stiffness" or "resistance" component that the skeletal material can provide in the normal direction of the plant surface. The larger the value, the stronger the line at that point. The stronger the expected skeletal holding force in a direction, the better. This scalar value is then mapped to a color lookup table for visualization.

[0080] In step 4, these scalar values ​​are overlaid onto the surface of the implant's 3D geometric model using color mapping, forming a virtual mechanical texture that visually reflects the distribution of mechanical support within the bone. For example, high-intensity areas are displayed as dark blue, and low-intensity areas as bright red. The generated virtual mechanical texture includes not only static intensity distribution but also dynamic adaptation potential evaluation. The system weights the corresponding mechanical textures based on the preset screw hole locations or load-bearing areas on the implant. On the potential contact surface between the implant and bone, the value of the virtual mechanical texture directly represents the biomechanical stability that area can provide after implantation. If the texture value of a certain area continues to decrease, it indicates that the bone at that location cannot provide sufficient anchoring force for the implant.

[0081] In the above method, step 5 involves performing interactive adaptation analysis. During the relative spatial transformation between the 3D geometric model of the implant and the voxel model of the skeleton, the topological semantic overlap between the two in the contact area and implantation path is calculated in real time. During interactive adaptation analysis, the system monitors the spatial relationship between the implant model and the skeleton model in real time. When the operator or automatic control algorithm applies translation or rotation transformations to the implant model, the system updates the relative coordinate system of the two at a predetermined time frequency, such as 60 frames per second. Within each update cycle, the system identifies the spatial overlap area between the implant and the skeleton model.

[0082] The "topological semantic overlap" Defined as the inplant contact area Above, holding force strength scalar Weight of endophyte importance Area integral:

[0083]

[0084] in, This refers to the total area of ​​the contact zone between the implant and the skeleton. The preset weighting function assigns higher values ​​to critical load-bearing areas (such as around screw holes). =1.5), and takes lower values ​​in other regions (e.g. =0.8). On a discrete grid, this integral is transformed into a summation form:

[0085]

[0086] in, The number of triangular facets in contact between the endophyte surface and the skeleton; , , The first The area of ​​each facet element, the scalar value of the holding force corresponding to the center point, and the importance weight. When screw assemblies are involved, the cumulative topological weights along their paths are... It can be added to the total overlap as an additional weighting factor. The calculation.

[0087] In step 5, the calculation logic for topological semantic overlap involves spatial integration of the implant surface normal vector field and the bone topological weight field. Specifically, the system calculates the topological weighted sum between all preset load-bearing parts of the implant and their corresponding bone voxels. The magnitude of this topological weighted sum depends on the degree of matching between the implant geometry and the microscopic topological structure within the bone. If the angle between the screw axis of the implant and the main growth direction of the trabecular bone is less than a preset angle threshold, such as less than 15 degrees, the overlap score of the region will be assigned a higher weight coefficient. This calculation method ensures that the overlap accurately reflects the engagement quality between the screw and the trabecular bone. To ensure smooth interaction, the real-time calculation process employs a parallel computing framework. The system divides the bone voxel model into multiple sub-regions and uses multiple graphics processing units or multi-core processors to synchronously perform spatial retrieval and numerical calculation of topological semantic overlap. This approach ensures minimal data update latency during complex 3D model interaction, guaranteeing real-time operation feedback.

[0088] Step 6 involves implementing posture fine-tuning and adaptation feedback. Based on the comparison between the topological semantic overlap and a preset threshold, the spatial pose of the 3D geometric model of the implant within the skeletal voxel model is automatically adjusted through a deformation-driven mechanism. The posture fine-tuning and adaptation feedback mechanism comprises two levels: real-time visual warning and automatic optimization. At the visual warning level, the system displays the adaptation quality in real-time on the 3D display interface using color gradients based on the calculated topological semantic overlap. When the topological weight of the skeletal region corresponding to the screw hole position of the implant is lower than a preset safety threshold, the region will be highlighted in a warning color, accompanied by a text prompt indicating a potential risk of implant loosening at the current location.

[0089] In step 6, the deformation-driven mechanism achieves automatic attitude fine-tuning by executing a gradient-ascending local optimization algorithm. The system uses the current topological semantic overlap as the objective function and calculates the gradient of the objective function in the six degrees of freedom of the endophyte, including translation along the X, Y, and Z axes and rotation around these axes. The gradient represents the direction in which the overlap increases most rapidly in 3D space. Based on the gradient direction, the system drives the endophyte model to perform small incremental displacements and rotations in 3D space. The step size of each increment is dynamically adjusted according to the magnitude of the gradient. The system continues this incremental update until the objective function reaches a local maximum, i.e., the overlap no longer increases significantly. During this process, the endophyte model, as if driven by some virtual force field, automatically moves towards a spatial position with higher internal skeletal support and better topological matching. Furthermore, the automatic adjustment logic is also constrained by anatomical boundaries. When performing deformation-driven fine-tuning, the system sets a maximum allowable deviation range. If the optimal position indicated by the optimization algorithm exceeds the clinically acceptable anatomical boundary, or touches the neurovascular protection area mask set in advance by the doctor, the system will forcibly terminate the displacement in the direction and search for a suboptimal solution within the constraints.

[0090] The "gradient ascent-based local optimization algorithm" uses topological semantic overlap. The objective function is defined in the parameter space consisting of six degrees of freedom. Find the maximum value on the gradient. Numerical gradient. Calculated using the finite difference method:

[0091]

[0092] Where j=1,...,6 corresponds to six degrees of freedom; It is a unit vector; This represents the step size of the small perturbation corresponding to the degree of freedom. Gradient vector. The direction of the fastest increase in overlap was determined. The "projection gradient method" was used to handle anatomical boundary constraints. When the algorithm proposes the next position... Violation of constraint set At that time, the system first calculates the feasible descent direction. :

[0093]

[0094] in, This means projecting any point onto the constrained feasible set. The projection operator on. Then, along the direction... Perform a line search to determine the optimal step size, ensuring that the implant does not exceed the clinically safe range during movement.

[0095] The method also includes establishing a fit evaluation report. This report summarizes multiple logical indicators during the fit process, including the anatomical fit between the implant and the bone surface. Anatomical fit is calculated by determining the average Euclidean distance between the inner surface of the implant and the outer surface of the bone. The report also includes the average topological weight of the load-bearing area, the bone density gradient distribution along the screw implantation path, and the expected mechanical stability score. These indicators are all derived through quantitative statistical analysis of voxel data and topological semantics within the 3D model. The method also involves predictive simulation of postoperative fit outcomes. After determining the optimal pose, the system simulates the fusion process between the implant and bone by modifying the attribute values ​​at corresponding locations in the bone voxel model.

[0096] Specifically, the voxel weights along the implantation path are adjusted upwards within a certain proportion to simulate bone induction effects, or downwards to simulate potential bone resorption. By performing cyclical topological evolution calculations over predetermined time spans, such as 3, 6, and 12 months post-surgery, the system can simulate the remodeling process of bone tissue under implant stimulation and predict long-term mechanical stability trends. The method also supports comparative analysis of multiple implant types. The system can simultaneously load multiple implant models of different models or specifications and perform the aforementioned topological semantic mapping and overlap calculations on each implant model, providing quantitative decision-making basis for clinical selection.

[0097] To further refine the engineering implementation details of the above method, the following provides an in-depth computational description of the data processing flow involved in each step.

[0098] In the preprocessing stage of step 1, the system sorts the DICOM sequence and constructs an original 3D data array based on the image position (ImagePosition Patient) and slice thickness (Slice Thickness) attributes. Due to potential noise during medical image acquisition, the system applies a 3D Gaussian filter kernel to smooth the voxel array. The standard deviation of the Gaussian kernel is dynamically determined based on the image's signal-to-noise ratio. Subsequent isotropic resampling employs a voxel coordinate-based relocation technique. The system defines the target grid spacing as variable D, converts the original voxel indices to world coordinates, and then calculates their linear interpolation weights within the target grid to obtain the scalar values ​​of the new voxels.

[0099] In the image segmentation sub-step of step 1, the criterion for the region growing algorithm is set as follows: if the gray value deviation of adjacent voxels is less than 10% of the original voxel's gray value, and the voxel's gray value is higher than a preset lower threshold, then the adjacent voxels are included in the current skeleton set. For cavities existing inside the skeleton, the system applies morphological closing operations, using a dilation-erosion process to fill the tiny gaps between bone trabeculae while preserving the macroscopic medullary cavity structure, generating an accurate skeleton body model mask.

[0100] In step 2, when constructing the microscopic topological weight field, the gradient covariance matrix was calculated using the central difference method. For a point in the voxel array, the gradient value in the X direction... ;for For index The voxel intensity value at that location; For index voxel intensity value at represents the discrete index of the voxel in the X direction. Corresponding to the voxel to the right of this point, Corresponding to the voxel adjacent to the left of this point; These are the discrete indices of the voxels in the Y and Z directions; Let X be the voxel step size in the X direction. Similarly, calculate the gradients in the Y and Z directions. The nine elements of the gradient covariance matrix are the sum of the pairwise products of these gradients within the analysis sphere. During eigenvalue decomposition, solve for the three roots of the characteristic polynomial. The eigenvector corresponding to the largest root reflects the dominant direction of the material distribution. To eliminate noise interference, the system introduces a directional coherence function. , It is a 3rd order square matrix. for The three eigenvalues, It is the maximum value among the three eigenvalues; The minimum value among the three eigenvalues. If the function value is close to 1, it indicates that the trabeculae have a very strong directionality; if it is close to 0, it indicates that the region has an isotropic porous structure.

[0101] In step 3, the triangular mesh generation employs a constrained Delaunay triangulation method. The system first extracts the boundary curves of the plant CAD model and then distributes seed points across the surface. The density of seed points is dynamically distributed according to the surface curvature. In areas with high curvature, such as threaded grooves, the seed point density increases; in flat areas, the density decreases. The generated triangular elements must satisfy a mass constraint that the ratio of the longer side to the shorter side is less than 3 to ensure the stability of subsequent normal vector calculations. The normal vector of each element is calculated using the vector product formula of two edge vectors, and then divided by the area of ​​the element to achieve normalization.

[0102] In step 4, when generating the virtual mechanics texture, the tensor mapping logic employs quadratic form operations between a second-order tensor and a vector. Let the topological semantic tensor be matrix T, and the surface normal vector be vector N, then the mapped scalar value... ; This operation represents transpose, and its physical meaning is to calculate the effective Young's modulus component in the direction of the normal vector. The generated scalar field is mapped into the GPU's texture memory. By writing shader programs, the system retrieves RGB color values ​​from a preset color lookup table (Color LUT) based on the magnitude of the scalar value, and updates the fragment colors on the surface of the inner plant model in real time.

[0103] In the interactive analysis of step 5, the system maintains a 4×4 transformation matrix to record the real-time position and pose of the inner plant model relative to the skeletal voxel model. The transformation matrix is ​​updated whenever the user drags the model. The system uses inverse transformation logic to transform the coordinates of the skeletal voxels to the local coordinate system of the inner plant model, determining whether the skeletal voxel falls within the geometric envelope of the inner plant. This spatial collision detection algorithm combines axis-aligned bounding boxes (AABB) and hierarchical bounding boxes (BVH) acceleration structures, significantly reducing the number of voxels required for integration.

[0104] In the deformation-driven fine-tuning of step 6, the convergence criterion for the gradient ascent algorithm is set as follows: the increment of overlap between two iterations is less than one ten-thousandth of the initial overlap. The step size factor employs a decay mechanism; the initial step size is larger to overcome local troughs, and as the number of iterations increases, the step size decreases to ensure stable convergence. In each iteration, the system applies small perturbations to the translation and rotation amounts respectively, and calculates the rate of change of overlap. Since six variables are involved, the system performs six perturbation calculations to construct the complete gradient vector. If the automatic path adjustment touches the anatomical constraints, the system applies the projective gradient method, projecting the gradient vector onto the tangent plane of the constraint boundary, allowing the model to slide along the boundary to find the optimal position, rather than penetrating the boundary.

[0105] Example 2: This example provides a specific application scenario for the orthopedic implant fitting analysis method based on a three-dimensional model, taking the fitting analysis of lumbar pedicle screw fixation surgery as an example.

[0106] In step 1 above, CT images of the patient's lumbar spine region were acquired, with a slice spacing of 0.625 mm. The system then established a voxel model of the lumbar spine skeleton through preprocessing. During this process, the system specifically enhanced the boundary between the cortical and cancellous bone in the pedicle region. A bilateral filtering algorithm was used to filter out metallic artifact noise in the images while preserving the clarity of the bone edges.

[0107] In step 2 above, a microscopic topological weight field is constructed for the narrow stress channels within the pedicle. Since the pedicle is a critical component bearing the spinal load, the trabeculae within it are typically oriented along the long axis of the pedicle. The system accurately identifies this dominant direction through gradient covariance matrix analysis and assigns a higher topological weight to this direction. If the patient has osteoporosis, and the system detects generally low feature values, it will mark the region as a weakly supported area in the topological semantic data.

[0108] In step 3 above, a standardized three-dimensional geometric model of the lumbar vertebral implant, including the pedicle screws and connecting rods, is loaded. The system performs high-precision meshing on the threaded portion of the screws, generating a fine normal vector field. The screw material is set to titanium alloy, and its elastic modulus parameter is entered into the system.

[0109] In step 4 above, a virtual mechanical texture is generated. As the screw model approaches the pedicle, the system calculates in real time the mapping relationship between each thread tooth on the screw surface and the topological weights inside the bone. Through color mapping, the doctor can clearly see which threads engage with the solid trabeculae when the screw enters the pedicle, and which are in the empty medullary cavity.

[0110] In step 5 above, interactive adaptation analysis is performed. The doctor adjusts the screw insertion angle and depth on the software interface. The system calculates the topological semantic overlap of the screw on the current trajectory in real time. If the screw deviates from the optimal force path, the overlap value will decrease. The system also monitors the distance of the screw implantation path to the lateral spinal canal boundary to ensure safety.

[0111] In step 6 above, fine-tuning of the posture is performed. If the initial screw placement by the surgeon is not mechanically optimal, the system activates a deformation-driven mechanism. Guided by a gradient ascent algorithm, the screw model automatically fine-tunes its tilt and pitch angles to ensure its axis closely aligns with the main growth direction of the trabecular bone within the pedicle. The system generates a fit evaluation report, providing a score for the expected pull-out force of the screw to assist the surgeon in assessing the reliability of the surgical plan.

[0112] In the application example, the parallel computing framework in step 5 uses a cluster of stream processors for data distribution. Each stream processor is responsible for calculating the interaction between the sub-mesh region on the screw surface and the corresponding bone voxel. The system utilizes a texture filtering unit to perform fast interpolation retrieval of voxel data, compressing the computation process, which originally required several seconds, to the millisecond level.

[0113] The visualization and early warning mechanism in step 6 also includes a three-dimensional acoustic prompt function. As the overlap increases, the prompt audio frequency rises; when a safety boundary is reached, the system emits a low-frequency warning sound. This multi-sensory interaction further improves the efficiency of adaptation analysis.

[0114] Example 3: This example provides details of the application of another three-dimensional model-based orthopedic implant fitting analysis method in the reconstruction of complex acetabular fractures.

[0115] In step 1 above, for comminuted acetabular fractures, the image segmentation algorithm employs a semantic segmentation network based on deep learning for assistance. The system uses a pre-trained convolutional neural network model to automatically identify the boundaries of each bone fragment. For each bone fragment, the system establishes a voxel model and records its initial relative spatial pose.

[0116] In step 2 above, when constructing the microscopic topological weight field, the system not only considers the direction of the trabecular bone but also introduces the contact stress characteristics between bone fragments. The system calculates the gradient distribution at the interface of the bone fragments to generate topological semantics characterizing the reduction quality. In the acetabular weight-bearing area, the system performs a secondary weighting of the topological weights to reflect the core support role of the region during human standing and walking.

[0117] In step 3 above, a three-dimensional geometric model of the acetabular reconstruction plate is established. Since the plate needs to be pre-bent according to the bone morphology, the system allows nonlinear deformation operations on the plate model in three-dimensional space. After the plate deforms, the system recalculates the normal vector field of its surface in real time, ensuring that the vector direction is always perpendicular to the deformed surface.

[0118] In step 4 above, a virtual mechanical texture is generated. The system sets the screw hole locations at various points on the steel plate as key monitoring points. By mapping the normal field of the steel plate surface to the topological weight field of the acetabular fracture fragment, the system displays the expected holding force intensity at each hole location on the steel plate surface.

[0119] In step 5 above, the interactive fitting analysis focuses on the anatomical reduction relationship between the plate and the fracture fragment. The system calculates in real time the fit and overlap between the bottom of the plate and the bone surface, as well as the topological semantic overlap between the screws screwed into the plate screw holes and the internal structure of the distal bone fragment. This multi-dimensional overlap calculation uses a weighted summation method, setting the geometric fit weight to 0.3 and the mechanical topological matching weight to 0.7.

[0120] In step 6 above, the attitude fine-tuning mechanism drives the plate to slide and rotate on the acetabular surface to find a spatial position that covers the most fracture fragments while ensuring good screw fixation strength for each fragment. The deformation-driven algorithm employs Pareto optimal search logic to handle this multi-objective optimization problem, maximizing the overall system's mechanical stability while ensuring anatomical fit of the plate.

[0121] In this embodiment, the multi-scale feature fusion enhancement process in step 2 plays a crucial role. For the thicker iliac bone region above the acetabulum, the system extracts macroscopic cortical bone thickness features and fuses them with microscopic trabecular bone texture. This allows the fit analysis to automatically identify the high-strength bony channels most suitable for placing long screws.

[0122] The deformation-driven mechanism in step 6 also incorporates constraint feedback based on collision detection. During automatic fine-tuning, if the steel plate model penetrates the bone model, the system generates a virtual repulsive force pointing outward. This virtual repulsive force serves as a correction term for gradient calculation, ensuring that the final position of the steel plate remains above the bone surface, consistent with actual clinical operation logic.

[0123] Finally, the adaptation evaluation report generated by the system includes a fracture reduction stability cloud map. This fracture reduction stability cloud map predicts the range of micromotion that may occur during weight-bearing rehabilitation by analyzing the cumulative topological weight distribution of each fracture fragment after implant fixation. If the predicted micromotion exceeds the physiological threshold for bone healing, the system will automatically suggest adding additional lag screws or adjusting the placement of the plate.

[0124] The method of this invention can also achieve multi-center data sharing and comparison by establishing a cloud database. The image data and generated topological weight field acquired in step 1 can be encrypted and uploaded to the cloud. By comparing the fitting parameters of the current case with those of thousands of similar cases in the database, the system uses machine learning algorithms to recommend the optimal implant selection and pose layout. This big data-driven fitting analysis further enhances the scientific rigor and reliability of the solution.

[0125] Furthermore, the implant model established in step 3 supports user-defined modifications. Surgeons can adjust the local geometric parameters of the implant in real time based on specific situations discovered during surgery, and the system will immediately respond and re-execute the analysis process from steps 4 to 6. This high degree of flexibility allows this method to adapt to changing clinical environments.

[0126] In all the embodiments described above, the mathematical logic involved is implemented using an instruction set in a computer program. For example, the eigenvalue decomposition of the gradient covariance matrix can be performed within a textually described logical framework using the standard Jacobian rotation or divide-and-conquer method. All numerical comparisons, such as the comparison of overlap with a threshold, are handled by logical decision branches. When specific logical conditions are met, the system triggers corresponding feedback or adjustment actions.

[0127] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional model-based orthopedic implant fitting analysis method, characterized by, Includes the following steps: Medical image data of the skeletal region to be analyzed is acquired and preprocessed. The gray values ​​representing tissue density in the image are mapped to a three-dimensional spatial grid through a voxelization reconstruction algorithm to establish a bone voxel model that includes the association between three-dimensional spatial coordinates and bone density attributes. A microscopic topological weight field is constructed, and by extracting local structural features from the bone voxel model, the distribution density and arrangement direction of trabeculae inside the bone are identified, generating topological semantic data for quantitatively describing the internal support capacity of the bone. A three-dimensional geometric model of the inner plant is established, and a mesh is generated on the surface of the three-dimensional geometric model of the inner plant to generate a normal vector field composed of several discrete surface elements and their corresponding surface normal vectors. A virtual mechanical texture is generated, and the topological semantic data in the microscopic topological weight field is mapped to the surface of the three-dimensional geometric model of the endophyte based on the tensor calculation method, so as to convert the bone strength features at the microscopic level into mechanical adaptation features at the macroscopic level. Interactive adaptation analysis is performed, and during the relative spatial transformation between the three-dimensional geometric model of the implant and the voxel model of the skeleton, the topological semantic overlap between the two in the contact area and the implantation path is calculated in real time. The pose fine-tuning and adaptation feedback are implemented. Based on the comparison results of the topological semantic overlap degree and the preset threshold, the spatial pose of the three-dimensional geometric model of the endophyte in the skeletal voxel model is automatically adjusted through the deformation driving mechanism.

2. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 1, characterized in that, The acquisition of medical image data of the bone region to be analyzed and the execution of preprocessing specifically include: acquiring raw image data composed of tomographic slices through a computed tomography (CT) scanner. The original image data is subjected to isotropic resampling processing. Asymmetric voxels are adjusted into cubic forms using linear interpolation or cubic spline interpolation algorithms to ensure that the sampling step size is consistent along each axis in the three-dimensional spatial coordinate system. Image segmentation processing based on a combination of threshold segmentation and region growing is implemented. A preset gray range is set according to the typical gray range of human skeletal tissue in the image, and non-skeletal tissue voxels outside the preset gray range are removed. Using the identified high-density regions as seed points, cluster the bone voxels with connectivity and check the gray-level change gradient between adjacent voxels. When the gray-level change gradient is lower than a preset mutation threshold, the adjacent voxels are classified as the same bone entity, and a noise-free 3D bone model is constructed.

3. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 1, characterized in that, The construction of the microscopic topological weight field specifically includes: defining an analysis sphere with a preset radius around each voxel unit of the skeletal voxel model; The central difference method is used to calculate the gray-level partial derivatives of each voxel in the three dimensions within the analysis sphere, and the gradient covariance matrix corresponding to each voxel is constructed by the outer product operation of the gray-level partial derivatives and the local weighted summation. Perform eigenvalue decomposition on the gradient covariance matrix to obtain three mutually orthogonal eigenvectors and their corresponding eigenvalues. Arrange the feature values ​​in descending order and calculate the anisotropy coefficient that reflects the non-uniformity of bone tissue distribution in a local area; When the difference between the feature values ​​exceeds a predetermined ratio, it is determined that the local region has anisotropic features, and the direction of the principal feature vector is defined as the growth direction of the trabeculae.

4. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 1, characterized in that, The generation of topological semantic data specifically includes: constructing a second-order symmetric tensor based on local bone density scalars and trabecular bone direction vectors; Each voxel unit is assigned a topological weight value that characterizes structural stability using the second-order symmetric tensor. An enhancement process based on multi-scale feature fusion is introduced to analyze skeletal morphological features on a larger spatial scale based on the extraction of voxel gradients. The skeletal morphological features include the thickness distribution of the bone cortex and the geometric contour of the metaphysis. By nonlinearly fusing microscopic trabecular bone features with macroscopic bone density distribution, a topological semantic field that comprehensively reflects the biomechanical properties of bones is generated.

5. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 1, characterized in that, The process of establishing a three-dimensional geometric model of the inner plant and generating a normal vector field specifically includes: importing a preset standardized digital model and using a triangular meshing algorithm to discretize the surface of the three-dimensional geometric model of the inner plant, dividing its surface into several triangular facets. Based on the spatial coordinates of the three vertices of each triangular element, the unit normal vector pointing from the inside of the model to the outside is obtained by calculating the cross product of the vectors of the two sides of the triangle and normalizing the result. The set of all unit normal vectors is used to form the normal vector field of the complete surface of the plant under the cover; The material properties of the three-dimensional geometric model of the inner plant are defined as linear isotropic elastic body. Under the reaction force of the skeleton, the deformation of the inner plant is estimated, and the normal vector in the surface normal vector field is dynamically deflected and corrected according to the displacement trend generated by the deformation.

6. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 5, characterized in that, The three-dimensional geometric model of the internal plant also includes attribute-defined screw components, and its specific analysis logic includes: assigning each screw component thread topology parameters including pitch, thread angle and effective diameter; Independent topological semantic analysis is performed on the screwing trajectory of each screw. By simulating the screw rotation and screwing process, the set of voxels swept by each point on the screw surface along the motion path is identified. The cumulative topological weights of the bone voxels traversed along the implantation path of the screw assembly are calculated to predict the initial stability of each screw.

7. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 1, characterized in that, The generation of virtual mechanical textures specifically includes: performing a dot product operation between the surface element normal vector of the three-dimensional geometric model of the endophyte and the direction tensor in the skeletal topological weight field through cross-scale feature mapping logic; For each sampling point on the surface of the inner plant, its corresponding spatial coordinates in the skeletal voxel model are retrieved, and the topological semantic tensor at the spatial coordinates is extracted using a trilinear interpolation algorithm. By calculating the product of the surface normal vector and the topological semantic tensor, a scalar value characterizing the holding force strength of the sampling point is obtained; The scalar value is mapped to the texture memory of the graphics processing unit, and the corresponding color value is obtained from a preset color lookup table according to the magnitude of the scalar value. The color value is then overlaid on the surface of the three-dimensional geometric model of the plant in a color mapping manner to form a virtual mechanical texture that reflects the distribution of mechanical support inside the skeleton.

8. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 1, characterized in that, The interactive adaptation analysis includes: establishing and maintaining a transformation matrix that records the real-time position and orientation of the endophyte model relative to the skeletal voxel model; When the inner plant model undergoes displacement or rotation transformation, the transformation matrix is ​​updated at a predetermined time frequency, and the coordinates of the skeleton voxels are transformed to the local coordinate system of the inner plant model using inverse transformation logic. Spatial collision detection is performed using axis-aligned bounding boxes or hierarchical bounding boxes to identify the areas where the inner plant model and the skeleton model spatially overlap. In the spatial overlapping area, the topological semantic overlap degree calculation is performed. The surface normal vector field of the inner plant and the topological weight field of the skeleton are spatially integrated to obtain the topological weight weighted sum between the preset load-bearing part of the inner plant and the corresponding skeleton voxel. When the angle between the screw axis of the implant and the main growth direction of the trabeculae inside the bone is less than a preset angle threshold, the weight coefficient of the overlap score of the corresponding area is increased.

9. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 1, characterized in that, The implementation of attitude fine-tuning and adaptation feedback specifically includes: executing a local optimization algorithm based on gradient ascent, using the current topological semantic overlap as the objective function, and calculating the gradient of the objective function in the six degrees of freedom of motion of the inner plant model, including translation along the three orthogonal axes and rotation around the three orthogonal axes; A complete gradient vector is constructed by applying small perturbations to each of the six degrees of freedom and calculating the rate of change of the objective function. The inner plant model is driven to perform incremental displacement and rotation in three-dimensional space according to the direction of the gradient vector, and the incremental step size of each step is dynamically adjusted according to the magnitude of the gradient vector until the increment of the objective function between two consecutive iterations is lower than the preset minimum proportion of the initial overlap. During the posture adjustment process, anatomical boundary constraints are introduced to set the maximum allowable deviation range and the mask area composed of neurovascular protection areas; When the path indicated by the gradient-based local optimization algorithm touches the anatomical boundary or the mask region, the gradient vector is projected onto the tangent plane of the constraint boundary using the projection gradient method, driving the endophyte model to search for a suboptimal solution within the constraint range along the boundary.

10. The method for orthopedic implant fitting analysis based on a three-dimensional model according to claim 1, characterized in that, It also includes the following steps: Establish an adaptation evaluation report, and use quantitative statistics to obtain the anatomical fit between the implant and the bone surface, the average topological weight of the load-bearing area, the bone density gradient distribution of the screw implantation path, and the mechanical stability score. The postoperative adaptation effect prediction simulation was performed. After determining the optimal pose, the fusion process between the implant and the skeleton was simulated by modifying the attribute values ​​of the corresponding positions in the bone voxel model. Perform cyclic topological evolution calculations over a predetermined time span to simulate the remodeling process of skeletal tissue under endophytic stimulation and predict long-term mechanical stability trends. It supports comparative analysis of multiple types of implants, and can load multiple implant models of different models or specifications. It performs topological semantic mapping and overlap calculation on each implant model, and provides a basis for clinical selection decision by comparing the optimal overlap values ​​of each model horizontally.

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