Biological soft tissue virtual simulation method and system and medium

Abstract

The invention relates to a biological soft tissue virtual simulation method and system and a medium, and the method comprises the following steps: constructing a three-dimensional soft tissue model based on medical image data, dispersing the three-dimensional soft tissue model into a particle system, and binding initial constraint parameters; detecting collision between the particles and the surgical instrument based on a symbolic distance field technology, if collision is not detected, outputting the positions of the particles, generating a simulation result, if collision is detected, applying repulsive force to the particles to correct a deformation path, judging whether stress is balanced or the maximum number of iterations is reached, if yes, returning to perform collision detection, and if not, returning to perform collision detection. The particle speed is updated in real time, the particle motion trend is predicted through an implicit time integration algorithm, position constraint, volume constraint, shape constraint and physical constraint are solved in a coupling mode through a Gaussian-Seidel iteration method, constraint rigidity is dynamically adjusted to be matched with real organization characteristics, and the particle position is updated. Compared with the prior art, the method has the advantage that deformation accords with a real physical change rule.

Description

Technical Field

[0001] The present invention relates to the technical field of soft tissue simulation modeling, and in particular to a method, system and medium for virtual simulation of biological soft tissues. Background Art

[0002] The technology of biological soft tissue modeling and simulation is the core technology in fields such as medical training and virtual surgery. Existing technologies mainly construct soft tissue models from medical image data and simulate their deformation behaviors. Traditional methods mainly rely on geometric modeling, simulating the deformation process by extracting the surface contour features of soft tissues. Although such methods are simple to implement, they only focus on morphological changes and cannot reflect the true biomechanical properties of soft tissues, such as viscoelasticity and non-linear deformation, resulting in insufficient simulation accuracy. Especially when dealing with dynamic interactions, the model is prone to distortion or logical errors, making it difficult to meet the requirements of high-precision surgical simulation.

[0003] To improve the realism, existing technologies have introduced physical modeling methods to simulate the stress distribution and energy conduction of soft tissues through mechanical equations. However, such methods have extremely high requirements for computing resources and usually rely on high-performance hardware for real-time solving, resulting in a significant increase in system costs. In addition, the parameter settings of physical modeling are complex and rely on a large amount of experimental data for calibration. In practical applications, the debugging cycle is long, the generality is poor, and the calculation is time-consuming, making it difficult to meet the requirements of real-time interaction.

[0004] The simulation method based on position dynamics optimizes the deformation process through geometric constraints. Although it has improved in computing efficiency, its core defect lies in insufficient physical accuracy. For example, CN107330972A discloses a real-time soft tissue deformation method for simulating biomechanical properties, which includes the following steps: generating soft tissue physical model data based on three-dimensional visualization data of soft tissue organs; generating collision detection model data based on the soft tissue physical model data; performing collision detection by loading the physical model data and the collision detection model data through a game engine; calculating the state of the deformed soft tissue through an optimization solution method of constraints. However, this method cannot accurately describe the true biomechanical properties of soft tissues, resulting in a significant deviation between the deformation simulation results and the actual biological tissue response. Especially when dealing with complex deformations or dynamic interactions, the model is prone to non-physical mutations or energy non-conservation phenomena, making it difficult to meet the requirements for the rigor of simulation results in medical scenarios. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, system and medium for virtual simulation of biological soft tissues, which introduce biomechanical parameters and physical properties into the traditional simulation framework based on position dynamics, and solve the problems of simulation distortion, insufficient accuracy and dynamic response mismatch caused by the lack of a material property mapping mechanism in traditional methods.

[0006] The object of the present invention can be achieved by the following technical solutions: A virtual simulation method for biological soft tissues, comprising the following steps: Construct a three-dimensional soft tissue model based on medical image data; Discretize the three-dimensional soft tissue model into a particle system and bind initial constraint parameters; Detect the collision between particles and surgical instruments based on the signed distance field technology. If no collision is detected, output the particle positions to generate the simulation result. If a collision is detected, Apply a repulsive force to the particles to correct the deformation path, and determine whether the force is balanced or the maximum number of iterations is reached. If so, return to perform collision detection. Otherwise, update the particle velocities in real time, predict the particle motion trend through the implicit time integration algorithm, use the Gauss-Seidel iteration method to couple and solve the position constraints, volume constraints, shape constraints and physical constraints, dynamically adjust the constraint stiffness to match the real tissue characteristics, and update the particle positions; wherein, in the physical constraints, the biomechanical parameters are converted into constraint weight coefficients through a parameterization mapping mechanism to support the rapid switching between different tissue types and high-precision simulation.

[0007] Specifically, constructing the three-dimensional soft tissue model based on medical image data means: based on medical image data, segment the target soft tissue area and perform three-dimensional reconstruction to generate a three-dimensional tetrahedral mesh model, define the grid vertex coordinates, normal vectors, topological connection relationships and tetrahedral element information, so that the geometric structure of the model is consistent with the real anatomical features, and obtain the three-dimensional soft tissue model.

[0008] Specifically, discretizing the three-dimensional soft tissue model into a particle system means: Map each vertex of the grid to a dynamic particle, with the particle numbers corresponding one-to-one to the vertex numbers. The initial positions of the particles directly inherit the vertex coordinates, the initial velocities are set to zero, and the mass of each particle is calculated by averaging the adjacent tetrahedral elements.

[0009] The position constraint is expressed as: , wherein, respectively represent the positions of two vertices in any tetrahedral element, a , b = 1, 2, 3, 4, d is the initial distance calibrated by the rest spring length.

[0010] The volume constraint, that is, maintaining the volume conservation of the tetrahedral element, is expressed as: , wherein, , , They are the four vertex positions of any tetrahedral element respectively. is the initial volume of the tetrahedron. , , .

[0011] The shape constraint is expressed as: , where, n represents the total number of particles in the cluster. Without considering the edge connection relationship of the tetrahedron, the cluster is a set of particles in a 3D grid. and respectively represent the particle positions before and after deformation of the i -th particle in the cluster. R represents the rotation matrix. and represent the translation vectors before and after deformation. and are the predicted rotation matrix and translation vector.

[0012] The physical constraint is expressed as: , where, is the deformation gradient. and are the first and second parameters of the Lamé constant respectively. The elastic energy density of the Neo-Hookean material is denoted as , is the energy resisting compression and expansion. is the energy resisting deformation.

[0013] The biomechanical parameters include Young's modulus and Poisson's ratio.

[0014] A virtual simulation system for biological soft tissues, used to implement the above method. The system includes: Three-dimensional soft tissue model construction module: Construct a three-dimensional soft tissue model based on medical image data; Particle system construction and initialization module: Discretize the three-dimensional soft tissue model into a particle system and bind initial constraint parameters; Collision detection module: Detect the collision between particles and surgical instruments based on the signed distance field technology. If no collision is detected, output the particle positions to generate the simulation result. If a collision is detected, call the particle update module; Particle update module: Apply repulsive forces to the particles to correct the deformation path, and determine whether the force is balanced or the maximum number of iterations is reached. If so, call the collision detection module for collision detection; otherwise, update the particle velocity in real time, predict the particle motion trend through the implicit time integration algorithm, and use the Gauss-Seidel iteration method to couple and solve the position constraint, volume constraint, shape constraint, and physical constraint, and dynamically adjust the constraint stiffness to match the real tissue characteristics to update the particle position; in the physical constraint, the biomechanical parameters are converted into constraint weight coefficients through the parameterized mapping mechanism to support the rapid switching between different tissue types and high-precision simulation.

[0015] A storage medium, on which a program is stored, and when the program is executed, the above method is implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) Real-time: The present invention deeply integrates the position dynamics framework and the implicit integration algorithm, and realizes the rapid convergence of constraints through Gauss-Seidel iteration, significantly improving the calculation efficiency compared with the traditional physical simulation model and achieving real-time performance.

[0017] (2) Precise: The present invention is based on the multi-constraint coupling iteration optimization mechanism, and through the collaborative correction of volume conservation, shape matching, stable particle spacing, and hyperelastic response, ensures that the simulated deformation trajectory is strictly consistent with the non-linear mechanical response of real biological tissues, reducing the theoretical and experimental deviations caused by modeling and improving the simulation modeling accuracy.

[0018] (3) Physically realistic: The present invention inputs the hyperelastic material constraint function with physical properties and biomechanical parameters, making the dynamic simulation have the physical material characteristics of tissues and conforming to the physically realistic world.

[0019] (4) Diversity: The present invention adjusts the initial values of the constraints through Young's modulus, viscoelastic coefficient, etc., accurately matches the stress relaxation and creep effects of different tissues such as tumors and muscles, breaks through the limitation of the single mechanical characteristics of the traditional method, and supports the rapid switching between different tissue types and high-precision simulation. Brief Description of the Drawings

[0020] Figure 1 is the method flow chart of the present invention; Figure 2 is the schematic diagram of the iteration process when a collision is detected in the present invention. Detailed Embodiment

[0021] The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives the detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0022] This embodiment provides a virtual simulation method for biological soft tissues. By combining a position dynamics framework with a physical constraint function and a biomechanical parameter mapping mechanism, a high-precision physical model is given to solve the problems of insufficient accuracy, low efficiency, and distorted dynamic response existing in the traditional method for soft tissue deformation simulation.

[0023] Specifically, as Figure 1 shown, this method includes the following steps: Step 1) Construct a three-dimensional soft tissue model based on medical image data.

[0024] Specifically, based on medical image data, the target soft tissue region is segmented and three-dimensionally reconstructed to generate a three-dimensional tetrahedral mesh model. This model needs to completely represent the geometric shape of the target soft tissue (such as the liver, muscle, etc.), define the grid vertex coordinates, normal vectors, topological connection relationships, and tetrahedral element information, so that the geometric structure of the model is consistent with the real anatomical features, and a three-dimensional soft tissue model is obtained.

[0025] Step 2) Discretize the three-dimensional soft tissue model into a particle system and bind initial constraint parameters.

[0026] Specifically, each vertex of the grid is mapped to a dynamic particle, and the particle numbers correspond one-to-one with the vertex numbers. The initial positions of the particles directly inherit the vertex coordinates. The initial velocities of all particles are set to zero, and the mass of each particle is evenly calculated by the adjacent tetrahedral elements. Next, the initial positions of the particles are directly assigned as the grid vertex coordinates to ensure complete geometric consistency with the input three-dimensional soft tissue model.

[0027] Step 3) In biological soft tissues, such as during surgical operations like cutting and suturing, collisions will occur and external forces will be applied to the particles. Therefore, in this step, based on the signed distance field technology, the collisions between the particles and the surgical instruments are detected. If no collision is detected, the particle positions are output to generate the simulation results. If a collision is detected, jump to step 4).

[0028] In this embodiment, based on the signed distance field technology, the spatial interaction between the particles and the surgical instruments is monitored in real time, the penetration area is detected, and the penetration depth is calculated to ensure sub-millimeter detection accuracy. The core function of this module is to use the signed distance function to calculate the minimum distance between objects to determine whether a collision occurs. Once a collision is detected, the module will use a collision detection algorithm to handle the interaction between fast-moving objects and avoid penetration errors. The management of collision response is based on the distance and normal information provided by the signed distance field, including the calculation of the rebound direction, the adjustment of the penetration depth, and the appropriate modification of the dynamic response, such as the adjustment of velocity and angular momentum.

[0029] In a preferred embodiment, generating the simulation results specifically includes the following steps: Adopt physically based real-time rendering technology to accurately simulate the soft tissue surface (such as wrinkles, subsurface scattering), dynamically calculate the light source reflection and soft shadows, and intuitively present the stress distribution through color gradients (red high-stress area, blue low-stress area), supporting high-resolution multi-view dynamic observation.

[0030] Present three-dimensional stereoscopic images through display devices such as virtual reality or augmented reality, support gesture interaction to adjust the viewing angle, and synchronize the deformation state and stress distribution in real time to achieve full-immersion visualization.

[0031] Step 4) Apply repulsive forces to the particles to correct the deformation path, and judge whether the force is balanced or the maximum number of iterations is reached. If so, return to perform collision detection; otherwise, perform constraint iterative optimization to make the movement of the object conform to the real physical world through four powerful constraints, and update the particle positions.

[0032] In this embodiment, the process of constraint iterative optimization is as Figure 2 shown, including the following steps: Update the particle velocities in real time, and predict the particle movement trends through implicit time integration algorithms. Use the Gauss-Seidel iterative method to solve the position constraints, volume constraints, shape constraints, and physical constraints coupledly, dynamically adjust the constraint stiffness to match the real tissue characteristics, update the particle positions, and then judge again whether the force is balanced or the maximum number of iterations is reached. Before this step, first load the biomechanical parameters such as Young's modulus and Poisson's ratio calibrated by experiments, and then convert the biomechanical parameters into the constraint weight coefficients of physical constraints through a parametric mapping mechanism to support the rapid switching between different tissue types and high-precision simulations.

[0033] The position constraint is expressed as: , where respectively represent the positions of two vertices in any tetrahedral element, a , b = 1, 2, 3, 4, d is the initial distance calibrated by the rest spring length.

[0034] The volume constraint is to maintain the conservation of the tetrahedral element volume, which is expressed as: , where, since the 3D mesh used in this embodiment is composed of tetrahedrons, so , , are respectively the positions of the four vertices of a tetrahedral element, is the initial volume of the tetrahedron, , , .

[0035] The shape constraint is expressed as: , where, n represents the total number of particles in the cluster. Without considering the edge connection relationship of the tetrahedron, the cluster is a set of particle collections in a 3D grid, and represent the particle positions before and after the deformation of the i -th particle in the cluster respectively, R represents the rotation matrix, and represent the translation vectors before and after the deformation respectively, and are the predicted rotation matrix and translation vector obtained by finally minimizing the objective function .

[0036] The physical constraint is expressed as: , where, is the deformation gradient, and are the first and second parameters of the Lamé constant respectively. The elastic energy density of the Neo-Hookean material is denoted as , is the energy resisting compression and expansion, is the energy resisting deformation.

[0037] At the beginning of the dynamic solution process, the application of external forces is started: when the surgical instrument interacts with the soft tissue (such as cutting, puncturing), the force feedback by the haptic device will update the particle velocity in real time, and then the displacement trend of the particles is predicted through the implicit integration algorithm. During this process, four types of physical constraints are called in sequence to iteratively correct the predicted positions. First, the volume constraint is used to maintain the volume conservation of the tetrahedral elements to prevent tissue collapse or expansion; then the shape constraint is adopted to align the local geometric shapes before and after the deformation to ensure structural stability; then the position constraint is used to adjust the spacing between adjacent particles to the initial length to eliminate excessive deformation; finally, based on the physical constraint (hyperelastic material constraint), the nonlinear stress-strain response of the soft tissue is simulated to ensure that the mechanical properties conform to the real biological tissue behavior. After each iteration, it is detected whether the particle system is in an equilibrium state or reaches the maximum number of iterations. If the conditions are not met, the correction continues until dynamic equilibrium or forced termination. During this process, biomechanical parameters such as Young's modulus and Poisson's ratio are dynamically mapped to the constraint stiffness coefficients to achieve real-time adaptation from experimental data to simulation parameters, ensuring that the mechanical differences of different tissue types (such as normal liver tissue and tumor) are accurately presented in the simulation.

[0038] In this embodiment, the Gauss-Seidel method is used for constrained iterative optimization. The volume, shape, distance, and hyperelastic constraints are called in sequence to gradually correct the particle positions, eliminate non-physical deformations, and ensure that the mechanical response conforms to the characteristics of real tissues. The Gauss-Seidel method is a method for solving linear equations. First, it is a system of equations of degree 2 with n linear equations where and are known,[ and the unknowns can be iteratively approximated using this method for the vector. The initial guess is usually where . Using the k-th iteration of to obtain an approximation of and using the next (i.e., k + 1)-th iteration of to obtain an approximation of

[0039] Specifically, the element-level formula iteration is as follows: While applying a normal repulsive force to the penetrating particles to correct the motion trajectory, a friction suppression mechanism is introduced to eliminate non-physical slippage, ensuring a stable and non-penetrating deformation process. The specific implementation of this module is to multiply these damping factor coefficients when calculating the external force in the system according to parameters in reality such as the measured friction coefficient and air resistance, thus ensuring compliance with the motion laws of the real physical world.

[0040] The present invention first introduces biomechanical parameters and physical properties into the traditional simulation framework based on position dynamics, solving the problems of simulation distortion, insufficient accuracy, and dynamic response mismatch caused by the lack of a material property mapping mechanism in traditional methods. By embedding biomechanical parameters such as Young's modulus, Poisson's ratio, and viscoelastic coefficient into the iterative parameters of the constraint function, directly mapping them into the constraint function, and finally satisfying the stiffness, damping, and energy attenuation characteristics, a physical consistency correlation from microscopic particle interaction to macroscopic tissue deformation is constructed. When soft tissue is subjected to external forces (such as cutting and puncturing), the tissue density stability is first maintained through volume constraints to prevent non-physical collapse; subsequently, the local stiffness is dynamically adjusted based on the hyperelastic model to simulate the mechanical differences between tumors and normal tissues; at the same time, a viscoelastic attenuation function is introduced to correct the constraint weight to reproduce the stress relaxation and creep effects of soft tissues. During the iterative solution process, the biomechanical parameters drive the constraint correction amount in real time to ensure that the deformation process strictly follows the physical laws. Finally, a high-fidelity simulation tool is realized for virtual surgery training.

[0041] In this embodiment, the motion trend of particles is predicted by an implicit time integration algorithm. The implicit integral position prediction is based on Newton's second law. The displacement trend of particles is predicted through the implicit integration algorithm to update the position and velocity for subsequent constraint iteration. This law is based on the energy potential The force derived from the following equation , and perform implicit position-level time discretization on the equation, denoted by the time step index t:

[0042] where M is the mass matrix, a is the acceleration, and x is the position of the particle. It should be noted that the gradient operator ∇ is a row vector containing partial derivatives.

[0043] The above is the introduction of the method embodiment. The following further illustrates the solution of the present invention through a system embodiment.

[0044] A virtual simulation system for biological soft tissues, used to implement the method described above. The system includes: Three-dimensional soft tissue model construction module: Construct a three-dimensional soft tissue model based on medical image data; Particle system construction and initialization module: Discretize the three-dimensional soft tissue model into a particle system and bind initial constraint parameters; Collision detection module: Detect the collision between the particle and the surgical instrument based on the signed distance field technology. If no collision is detected, output the particle position and generate the simulation result. If a collision is detected, call the particle update module; Particle update module: Apply a repulsive force to the particle to correct the deformation path, and determine whether the force is balanced or the maximum number of iterations is reached. If so, call the collision detection module to perform collision detection. Otherwise, update the particle velocity in real time, predict the particle motion trend through the implicit time integration algorithm, and use the Gauss-Seidel iteration method to couple and solve the position constraint, volume constraint, shape constraint, and physical constraint, and dynamically adjust the constraint stiffness to match the real tissue characteristics and update the particle position; among them, in the physical constraint, the biomechanical parameters are converted into constraint weight coefficients through the parameterized mapping mechanism to support the rapid switching and high-precision simulation of different tissue types.

[0045] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiment, which will not be elaborated here.

[0046] When the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0047] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field based on the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. A virtual simulation method for biological soft tissues, characterized in that It includes the following steps: Construct a three-dimensional soft tissue model based on medical image data; Discretize the three-dimensional soft tissue model into a particle system and bind initial constraint parameters; Detect the collision between particles and surgical instruments based on the signed distance field technology. If no collision is detected, output the particle positions and generate simulation results. If a collision is detected, then Apply a repulsive force to the particles to correct the deformation path, and determine whether the force is balanced or the maximum number of iterations is reached. If so, return to perform collision detection. Otherwise, update the particle velocities in real time, predict the particle motion trend through the implicit time integration algorithm, use the Gauss-Seidel iteration method to couple and solve the position constraint, volume constraint, shape constraint, and physical constraint, dynamically adjust the constraint stiffness to match the real tissue characteristics, and update the particle positions; wherein, in the physical constraint, the biomechanical parameters are converted into constraint weight coefficients through a parameterized mapping mechanism to support the rapid switching between different tissue types and high-precision simulation.

2. The virtual simulation method for biological soft tissue according to claim 1, characterized in that The construction of the three-dimensional soft tissue model based on medical image data is specifically as follows: Based on medical image data, segment the target soft tissue region and perform three-dimensional reconstruction to generate a three-dimensional tetrahedral mesh model, define the mesh vertex coordinates, normal vectors, topological connection relationships, and tetrahedral element information, so that the geometric structure of the model is consistent with the real anatomical features, and obtain a three-dimensional soft tissue model.

3. A virtual simulation method for biological soft tissues according to claim 1, characterized in that The discretization of the three-dimensional soft tissue model into a particle system is specifically as follows: Map each vertex of the mesh to a dynamic particle. The particle numbers correspond one-to-one with the vertex numbers. The initial positions of the particles directly inherit the vertex coordinates, the initial velocities are set to zero, and the mass of each particle is evenly calculated by the adjacent tetrahedral elements.

4. A virtual simulation method for biological soft tissues according to claim 1, characterized in that, The position constraint is expressed as: ， wherein, respectively represent the positions of two vertices in any tetrahedral element, a , b = 1, 2, 3, 4, d is the initial distance calibrated by the static spring length.

5. A method for virtual simulation of biological soft tissue according to claim 1, characterized in that, The volume constraint, that is, maintaining the volume conservation of the tetrahedral elements, is expressed as: ， Among them, , , are respectively the four vertex positions of any tetrahedral element, is the initial volume of the tetrahedron, , , .

6. A virtual simulation method for biological soft tissues according to claim 1, characterized in that The shape constraint is expressed as: ， Among them, n represents the total number of particles in the cluster. Without considering the edge connection relationship of the tetrahedron, the cluster is a set of particles in a 3D grid, and respectively represent the particle positions before and after the deformation of the i th particle in the cluster, R represents the rotation matrix, and represent the translation vectors before and after the deformation, and are the predicted rotation matrix and translation vector.

7. A virtual simulation method for biological soft tissues according to claim 1, characterized in that The physical constraint is expressed as: ， where, is the deformation gradient, and are the first and second parameters of the Lamé constant, respectively. The elastic energy density of the Neo-Hookean material is denoted as , is the energy resisting compression and expansion, is the energy resisting deformation.

8. A virtual simulation method for biological soft tissues according to claim 1, characterized in that The biomechanical parameters include Young's modulus and Poisson's ratio.

9. A virtual simulation system for biological soft tissues, characterized in that, For implementing the method described in any one of claims 1-8, the system includes: A three-dimensional soft tissue model construction module: Construct a three-dimensional soft tissue model based on medical image data; A particle system construction and initialization module: Discretize the three-dimensional soft tissue model into a particle system and bind initial constraint parameters; A collision detection module: Detect the collision between particles and surgical instruments based on the signed distance field technology. If no collision is detected, output the particle positions and generate simulation results. If a collision is detected, then call the particle update module; A particle update module: Apply a repulsive force to the particles to correct the deformation path, and determine whether the force is balanced or the maximum number of iterations is reached. If so, call the collision detection module to perform collision detection. Otherwise, update the particle velocities in real time, predict the particle motion trend through the implicit time integration algorithm, use the Gauss-Seidel iteration method to couple and solve the position constraint, volume constraint, shape constraint, and physical constraint, dynamically adjust the constraint stiffness to match the real tissue characteristics, and update the particle positions; wherein, in the physical constraint, the biomechanical parameters are converted into constraint weight coefficients through a parameterized mapping mechanism to support the rapid switching between different tissue types and high-precision simulation.

10. A storage medium, on which a program is stored, characterized in that, When the program is executed, it implements the method described in any one of claims 1-8.

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