Heterogeneous head model construction method and system for multi-physics field simulation

Abstract

The invention relates to a heterogeneous human head model construction method and system oriented to multi-physics field simulation, based on real human head T1 weighted magnetic resonance image data, key tissues such as white matter, gray matter and cerebrospinal fluid are accurately segmented, and a three-dimensional geometric model used for generating carrying tissue identification information is researched and developed; converting the model data into a file format conforming to the NASTRAN standard by utilizing an independently developed format conversion program, and completely retaining organization classification information; finally, different tissue areas are automatically recognized through physical field simulation software, corresponding frequency dependent material attributes are distributed, and a finite element model capable of being directly used for simulation is formed. According to the method, seamless integration of the high-precision heterogeneous human head model and the simulation platform is achieved, tedious manual repairing and assignment operation is not needed, the method has the advantages of being high in precision, high in efficiency and good in compatibility, and the authenticity and reliability of biomedical simulation such as transcranial magnetic stimulation and radio frequency coil design can be remarkably improved.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of biomedical engineering and computational electromagnetics, and in particular to a method and system for constructing heterogeneous human head models for multiphysics simulation. Background Technology

[0002] With the development of computational medicine, numerical models based on computer simulations have become indispensable tools for studying internal physical phenomena of the human body, optimizing medical treatment plans, and designing novel medical devices. In the fields of neuroscience and neural engineering, especially in techniques such as transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (TES), it is necessary to accurately simulate the distribution of electromagnetic fields in human head tissues to assess the accuracy of stimulation targets and energy deposition effects. Similarly, in fields such as tumor hyperthermia and microwave imaging, simulation accuracy is directly related to the safety of treatment and the reliability of imaging.

[0003] Achieving high-precision simulation requires a physical model that accurately reflects the complexity of anatomical structures and the heterogeneity of tissue material properties. However, the following significant problems and shortcomings currently exist in this technological field:

[0004] (1) Limitations of Homogenized Models: Currently, many head models built into or widely used in mainstream multiphysics simulation software such as COMSOL Multiphysics are homogeneous or simplified. These models treat complex head structures (such as the brain) as one or several homogeneous materials with average material properties, failing to accurately distinguish key tissues such as white matter, gray matter, and cerebrospinal fluid. Since these tissues exhibit orders of magnitude differences in physical parameters such as electrical conductivity, dielectric constant, and thermal conductivity, homogenization can lead to serious deviations in simulation results, failing to meet the high accuracy requirements of scientific research and preclinical studies.

[0005] (2) Compatibility Barriers Between High-Precision Models and Multiphysics Simulation Platforms: On the other hand, some international research institutions have developed heterogeneous human models with extremely high anatomical accuracy and tissue detail. However, these models are usually tied to specific simulation platforms or software ecosystems. While these dedicated platforms are powerful in their respective fields, they are often inferior to general-purpose multiphysics simulation software such as COMSOL Multiphysics in terms of the flexibility of multiphysics coupling, the intuitiveness of the user interface, and the ability to customize complex physical processes. This prevents many COMSOL users from utilizing these high-quality anatomical models, resulting in wasted resources and technical barriers.

[0006] (3) Existing compatibility solutions have defects: Although there are some methods to import third-party models into COMSOL, this process usually has many problems such as loss of geometric details, topological errors, and difficulty in assigning material properties. These problems make it extremely difficult and time-consuming to build a simulation-ready high-precision heterogeneous human head model.

[0007] Therefore, there is an urgent need in this field for a method and system for constructing human head models that can take into account high anatomical accuracy and tissue heterogeneity, and can be seamlessly and efficiently integrated with multiphysics simulation software such as COMSOL Multiphysics, in order to overcome the shortcomings of the existing technologies and promote the application of biomedical simulation technology in scientific research and clinical practice. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a method and system for constructing heterogeneous human head models for multiphysics simulation, so as to solve or partially solve the compatibility barrier and poor compatibility problem between high-precision models and multiphysics simulation platforms.

[0009] The objective of this invention can be achieved through the following technical solutions:

[0010] One aspect of the present invention provides a method for constructing heterogeneous human head models for multiphysics simulation, comprising the following steps:

[0011] T1-weighted magnetic resonance imaging data of the head were acquired, and three-dimensional image data volume was obtained through preprocessing;

[0012] Image segmentation is performed on the three-dimensional image data volume, and voxels are classified and labeled according to tissue type to obtain a three-dimensional segmentation mask with tissue labels;

[0013] Surface reconstruction is performed based on the three-dimensional segmentation mask to obtain a three-dimensional surface geometric model. A unique identifier is assigned to each tissue type to form a composite geometry with multiple nodes.

[0014] Identify the node coordinates, node connection relationships, and organizational classification information in the composite geometry, and generate a file that conforms to the NASTRAN standard;

[0015] Import the file into the physics simulation software, create selection sets corresponding to different tissue types, and assign corresponding material properties to each tissue type based on the selection sets to complete the construction of the heterogeneous human head simulation model.

[0016] As a preferred technical solution, the process of generating a file conforming to the NASTRAN standard includes the following steps:

[0017] Based on the unique identifier, read the mesh data of different tissue types in the composite geometry;

[0018] Number all nodes in the composite geometry and write them into the GRID field of the NASTRAN file;

[0019] Define the units corresponding to different tissue types as CTRIA3 or CQUAD4 units respectively, and associate the identifiers through the PSHELL attribute card;

[0020] Output node, cell, and attribute information as a text file according to the NASTRAN format specification.

[0021] As a preferred technical solution, the process of generating files conforming to the NASTRAN standard further includes the following steps:

[0022] The generated text file is checked for the continuity of node numbers, the connectivity of units, and the consistency of organizational identifiers.

[0023] As a preferred technical solution, the following steps are also included:

[0024] Based on the constructed heterogeneous human head simulation model, electromagnetic field simulation and absorption rate analysis of the radio frequency coil were performed using physical field simulation software.

[0025] As a preferred technical solution, the tissue types include white matter, gray matter, and cerebrospinal fluid.

[0026] As a preferred technical solution, the three-dimensional surface geometric model is generated through a format conversion program.

[0027] As a preferred technical solution, the preprocessing includes noise suppression, intensity normalization, and spatial registration.

[0028] As a preferred technical solution, the material properties include frequency-dependent conductivity and relative permittivity.

[0029] As a preferred technical solution, the FAST algorithm is used to achieve image segmentation of the three-dimensional image data volume.

[0030] Another aspect of the present invention provides a heterogeneous head model construction system for multiphysics simulation, used to implement the aforementioned heterogeneous head model construction method for multiphysics simulation, the system comprising:

[0031] The image processing module is used to acquire T1-weighted magnetic resonance imaging data of the head and obtain a three-dimensional image data volume through preprocessing.

[0032] The image segmentation module is used to segment the three-dimensional image data volume, classify and label voxels according to tissue type, and obtain a three-dimensional segmentation mask with tissue labels.

[0033] The geometric reconstruction and mapping module is used to reconstruct the surface based on the three-dimensional segmentation mask, obtain a three-dimensional surface geometric model, assign a unique identifier to each tissue type, and form a composite geometry with multiple nodes.

[0034] The format conversion module is used to identify the node coordinates, node connection relationships, and organization classification information in the composite geometry, and generate a file that conforms to the NASTRAN standard.

[0035] An integration module is used to import the file into a physics simulation software, create selection sets corresponding to different tissue types, and assign corresponding material properties to each tissue type based on the selection sets, thereby completing the construction of a heterogeneous human head simulation model.

[0036] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0037] (1) Overcoming the platform barrier between high-precision models and multiphysics software: In response to the problem that high-precision models are confined to specific software ecosystems in the existing technology, this invention successfully introduces high-fidelity heterogeneous models into physical field simulation software (such as the COMSOL Multiphysics platform) through medical image segmentation, NASTRAN format export, and physical field simulation software identification process.

[0038] (2) Significantly improves the anatomical realism and physical accuracy of the simulation model: Compared with the existing homogeneous or simple layered models in physical field simulation software, the model provided by this invention accurately distinguishes tissues such as white matter, gray matter and cerebrospinal fluid with significant differences in electrical properties, ensuring the biological rationality and reliability of the physical field, especially the electromagnetic field and thermal field calculation results from the source, and avoiding significant errors caused by the homogeneous assumption.

[0039] (3) The modeling process has been automated and standardized: Traditional methods require a lot of manual geometric repair and region selection work when importing complex models into physical field simulation software. This invention utilizes the "group" feature of the NASTRAN format to realize the automatic identification of the organization region and the automatic creation of the selection set, and automates the process of assigning material properties. This shortens the manual operation that originally took several days, significantly improves efficiency, and reduces human error. Attached Figure Description

[0040] Figure 1 This is a flowchart of a heterogeneous human head model construction method for multiphysics simulation in the embodiment.

[0041] Figure 2 This is a schematic diagram of the three-dimensional segmentation mask of the brain tissue obtained after segmentation in the embodiment;

[0042] Figure 3This is a schematic diagram of the three-dimensional surface geometry model after unified color mapping in the embodiment;

[0043] Figure 4 This is a partial schematic diagram of the NASTRAN format file generated in the embodiment;

[0044] Figure 5 The NASTRAN format file generated in this embodiment contains a schematic diagram of selection sets formed for different tissues;

[0045] Figure 6 This is a schematic diagram illustrating a specific application scenario of the NASTRAN format file generated in the embodiment;

[0046] Figure 7 The diagram shows the specific absorption rate (SAR value) and electric field value calculated based on the characteristics of human head tissue in the NASTRAN format file generated in the example. Compared with the homogenized model, it is more consistent with the histological characteristics of the real human body.

[0047] Figure 8 This is a schematic diagram of a heterogeneous human head model construction system for multiphysics simulation in the embodiment. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0049] Example 1

[0050] To address the problems existing in the aforementioned technologies, this embodiment provides a method for constructing heterogeneous human head models for multiphysics simulation. Based on medical imaging data, a head model that accurately reflects the heterogeneity of tissues such as brain white matter, gray matter, and cerebrospinal fluid is constructed. Furthermore, a self-developed format conversion tool is used to solve the compatibility problem between the high-precision model and the COMSOL Multiphysics simulation platform, achieving seamless model integration and automatic assignment of material properties, thereby improving the accuracy and efficiency of biomedical simulation.

[0051] This embodiment demonstrates the specific process of constructing a heterogeneous human head model using the method of this invention and applying it to COMSOL Multiphysics for RF coil simulation analysis. See also... Figure 1 The method includes the following steps:

[0052] Step S1: Acquire T1-weighted magnetic resonance imaging data of the head and preprocess the imaging data to obtain a three-dimensional image data volume.

[0053] A series of high-resolution T1-weighted magnetic resonance imaging (MRI) data of the human head were acquired. The original images were then subjected to noise suppression, intensity normalization, and spatial registration to obtain high-quality three-dimensional image data volumes.

[0054] Specifically, a United Imaging 3T Omega MRI scanner was used, employing a T1-weighted sequence (parameters: TR = 2530ms, TE = 2.98ms, TI = 1100ms, flip angle = 7°, voxel size = 1×1×1mm). 3 Head imaging data was collected from a healthy volunteer. On a Linux workstation, the raw DICOM data was anonymized using the fslanon command of FSL (FMRIB Software Library v6.0.3), and then the images were redirected to standard spatial orientation using the fslreorient2std command.

[0055] Step S2: The three-dimensional image data volume is segmented using an image segmentation algorithm. Voxels are classified and labeled as different biological tissue types, generating a three-dimensional segmentation mask with tissue labels. Tissue types include at least white matter, gray matter, and cerebrospinal fluid. See also... Figure 2 This is a schematic diagram of the three-dimensional segmentation mask of the brain tissue obtained after segmentation, where (a), (b), and (c) are the gray matter mask, cerebrospinal fluid mask, and white matter mask, respectively.

[0056] Preferably, the image segmentation algorithm is the FAST algorithm from the FSL software. The specific command is:

[0057] fast-T 1-n 3-H 0.1-I 4-l 20.0-Bbo output_prefix input_image.nii.gz

[0058] Parameter description: -T 1 specifies T1-weighted image, -n 3 specifies segmentation into 3 tissue types, -B option enables field correction, and -b option outputs binary segmentation results. After processing, probability maps and binary segmentation results of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) are obtained and saved in NIfTI format.

[0059] Step S3: Reconstruct the surface of the 3D segmentation mask to generate a 3D surface geometry model, and assign a unique identifier to each tissue type, forming a composite geometry carrying tissue classification information. See also Figure 3 This is a schematic diagram of the three-dimensional surface geometry model after unified color mapping, where (a), (b), and (c) are three representative cross-sections of brain tissue.

[0060] The developed C++ program reads the FSL segmentation results. The program first extracts the isosurfaces of each tissue and generates a smooth surface mesh using a self-compiled algorithm. Then, a unique attribute identifier is assigned to each tissue: white matter (ID=101), gray matter (ID=102), and cerebrospinal fluid (ID=103). The program outputs a standardized data file containing the mesh and attribute information for all tissues.

[0061] Step S4: Using a format conversion program, read the arrays of node coordinates, element connection relationships, and organization identification information of the composite geometry, and generate a file conforming to the NASTRAN standard. The connection relationships between nodes define the topology of the "element".

[0062] The preferred, self-developed format conversion program performs the following operations:

[0063] Step S41: Read grid data of different organizations based on unique identifiers.

[0064] Step S42: Number all nodes uniformly and write them into the GRID field of the NASTRAN file.

[0065] Step S43: Define the units corresponding to different organizations as CTRIA3 or CQUAD4 units respectively, and associate them with their organization identifiers through the PSHELL attribute card.

[0066] Step S44: Output the node, cell, and attribute information as a text file according to the NASTRAN format specification.

[0067] Step S45, data verification, is used to check the continuity of node numbers, cell connectivity, and consistency of organizational identification in the generated NASTRAN file.

[0068] See Figure 4 The diagram shows a partial view of the generated NASTRAN format file, where (a) is a human head model generated in NASTRAN format based on T1W, (b) is a 3D view, (c) is a top view, and (d) is a side view.

[0069] Step S5: Import the NASTRAN file into COMSOL Multiphysics software. The software automatically creates selection sets corresponding to different tissue regions based on the attribute information in the file, and assigns corresponding material properties to each tissue region based on the selection sets, thus completing the construction of the simulation model.

[0070] Preferably, the material properties include frequency-dependent conductivity and relative permittivity.

[0071] Preferably, the method further includes S6: using the constructed model for radio frequency simulation in COMSOL Multiphysics software, specifically including electromagnetic field simulation of the radio frequency coil and specific absorption rate analysis. Specifically, this includes the following sub-steps:

[0072] (1) Model import and validation:

[0073] Creating a new model in COMSOL Multiphysics (v6.2)

[0074] Select "Import" → "Mesh", and then select the generated head_model.nastran file.

[0075] The software automatically creates the corresponding selection sets: "ID=101" (white matter), "ID=102" (gray matter), and "ID=103" (cerebrospinal fluid).

[0076] See Figure 5 The images show the three selection sets after they have been selected. (a), (b), and (c) indicate that the three selection sets are displayed in blue after being selected. The creation of these selection sets facilitates the rapid definition of material properties for the selection sets in the future.

[0077] (2) Material property definition:

[0078] The electrical conductivity of brain white matter was set to 0.3 S / m, and the relative permittivity was set to 50 [dimensionless number].

[0079] The electrical conductivity of the gray matter was set to 1.2 S / m, and the relative permittivity was set to 70 [dimensionless number].

[0080] The cerebrospinal fluid conductivity was set to 2.5 S / m, and the relative permittivity was set to 97 [dimensionless number].

[0081] (3) RF simulation settings:

[0082] Add a "Radio Frequency Module".

[0083] Create a birdcage-shaped head coil model with a diameter of 300mm and a height of 300mm.

[0084] The operating frequency is set to 128MHz.

[0085] Define the port excitation and boundary conditions.

[0086] Place the human head model at the center of the coil. See also Figure 6 (a) is a three-dimensional view of the human head model placed at the center of the coil. (b) is a top view of the human head model placed at the center of the coil.

[0087] Set the mesh refinement parameters so that the maximum cell size is 1 / 8 of the wavelength.

[0088] (4) Solution and Analysis:

[0089] The calculation is performed using a frequency domain solver.

[0090] For analysis of specific SAR distributions, see [link to relevant documentation]. Figure 7 a.

[0091] To evaluate the distribution characteristics of the electromagnetic field within the head model, see [link / reference]. Figure 7 b.

[0092] The heterogeneous head model constructed using this method has been successfully applied to COMSOL RF simulation. Simulation results show that, due to accurate consideration of the differences in dielectric properties of different tissues, the model can realistically reflect the propagation characteristics of electromagnetic waves in head tissues. Compared with homogeneous models, the heterogeneous model constructed in this invention exhibits higher accuracy in SAR calculation and electric field distribution prediction, providing a more reliable simulation platform for RF safety assessment and RF coil design.

[0093] In summary, compared with the prior art, this application makes the following improvements:

[0094] (1) To address the issue that most general-purpose mesh conversion tools (such as Meshlab and Gmsh) only support the export of geometric and mesh data when outputting NASTRAN format, and cannot identify and retain the classification information of different biological tissues (such as white matter, gray matter, and cerebrospinal fluid) in the original data, this method reads a pre-assigned array of unique tissue identifiers and, when generating the NASTRAN file, uses PSHELL attribute cards to explicitly associate each cell with its corresponding tissue identifier (such as 101, 102, and 103). Ultimately, the generated NASTRAN file not only contains geometric information but also fully embeds tissue classification semantics, providing a crucial basis for material property allocation in subsequent multi-material, anisotropic biomechanical analysis.

[0095] (2) To address the issue that manually merging meshes from different structures after processing them separately can easily lead to duplicate or conflicting node numbers, causing elements to point to incorrect nodes and resulting in fatal errors such as undefined nodes when the NASTRAN solver reads the data, thus interrupting the simulation process, this method, in step S42, globally and uniformly renumbers the nodes of all structures. This ensures the uniqueness of each node ID within the entire composite geometry and maintains the consistency between the node coordinate array and the element connection relationship array as a whole. Ultimately, this guarantees the topological correctness of the composite geometry mesh, eliminates simulation errors caused by node ID conflicts, and significantly improves the reliability of model data and the automation level of the simulation process.

[0096] (3) Converted NASTRAN files typically rely on manual visual inspection by engineers. For biomedical models composed of hundreds of thousands or even millions of cells, this process is extremely tedious, inefficient, and prone to overlooking deeper issues such as cell connection errors and inconsistent attribute assignments. This method introduces an automated data verification process in step S45. Through program algorithms, it automatically checks the continuity of node numbers, cell connectivity (such as the existence of isolated nodes), and the consistency between cell organization identifiers and attribute cards. This achieves automatic quality control of the output file, enabling rapid and reliable detection and location of potential errors. It ensures the accuracy and integrity of model data used for critical simulations (such as medical analysis) and reduces the risk of simulation failure due to model errors.

[0097] (4) Currently, some simple converters use the method of directly generating hexahedral elements from voxel mapping. The resulting mesh surface is stepped, and the element distortion rate is high, which seriously affects the convergence and accuracy of finite element calculations. To address these issues, this method relies on the high-quality surface mesh generated in step S3 and prioritizes the use of CTRIA3 and CQUAD4 element types suitable for curved surfaces to characterize tissue interfaces during conversion, thereby more accurately conforming to real anatomical geometry. This method provides finite element meshes with higher geometric fidelity and better element quality, significantly improving the convergence of numerical calculations and the accuracy and reliability of biomechanical simulation results.

[0098] Example 2

[0099] Based on Example 1, this example provides a heterogeneous head model construction system for multiphysics simulation, used to implement the aforementioned heterogeneous head model construction method for multiphysics simulation. See [link to example]. Figure 8 The system includes:

[0100] (1) Image processing module, used to acquire T1-weighted magnetic resonance imaging data of the head and obtain three-dimensional image data volume through preprocessing.

[0101] (2) Image segmentation module, used to perform image segmentation on the three-dimensional image data volume, classify and label voxels according to tissue type, and obtain a three-dimensional segmentation mask with tissue label.

[0102] (3) Geometric reconstruction and mapping module, used to perform surface reconstruction based on the three-dimensional segmentation mask, obtain a three-dimensional surface geometric model, assign a unique identifier to each tissue type, and form a composite geometry with multiple nodes.

[0103] (4) Format conversion module, used to identify node coordinates, node connection relationships, and organizational classification information in the composite geometry, and generate files conforming to the NASTRAN standard. Specifically, this module uses a self-developed Python conversion tool (brain2nastran.py) to process the output results of the geometry reconstruction and mapping module. The main functional units of the tool include:

[0104] Data parsing unit: used to read node coordinates and facet connection information.

[0105] Topology check cell: Used to verify the continuity and closure of the mesh.

[0106] Format generation unit: Used to output files according to NASTRAN specifications.

[0107] (5) Integration module, used to import the file into the physical field simulation software, create selection sets corresponding to different tissue types, and assign corresponding material properties to each tissue type based on the selection sets to complete the construction of the heterogeneous human head simulation model.

[0108] Example 3

[0109] Based on the foregoing embodiments, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the heterogeneous human head model construction method for multiphysics simulation as described in Embodiment 1.

[0110] At the hardware level, this electronic device includes a processor, internal bus, network interface, memory, and non-volatile storage, and may also include other hardware required for the business logic. The processor reads the corresponding computer program from the non-volatile storage into memory and then executes it to achieve the above-mentioned functions. Figure 1 The method described herein. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0111] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0112] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0113] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing heterogeneous human head models for multiphysics simulation, characterized in that, Includes the following steps: T1-weighted magnetic resonance imaging data of the head were acquired, and three-dimensional image data volume was obtained through preprocessing; Image segmentation is performed on the three-dimensional image data volume, and voxels are classified and labeled according to tissue type to obtain a three-dimensional segmentation mask with tissue labels; Surface reconstruction is performed based on the three-dimensional segmentation mask to obtain a three-dimensional surface geometric model. A unique identifier is assigned to each tissue type to form a composite geometry with multiple nodes. Identify the node coordinates, node connection relationships, and organizational classification information in the composite geometry, and generate a file that conforms to the NASTRAN standard; Import the file into the physics simulation software, create selection sets corresponding to different tissue types, and assign corresponding material properties to each tissue type based on the selection sets to complete the construction of the heterogeneous human head simulation model.

2. The method for constructing a heterogeneous human head model for multiphysics simulation according to claim 1, characterized in that, The process of generating files conforming to the NASTRAN standard includes the following steps: Based on the unique identifier, read the mesh data of different tissue types in the composite geometry; Number all nodes in the composite geometry and write them into the GRID field of the NASTRAN file; Define the units corresponding to different tissue types as CTRIA3 or CQUAD4 units respectively, and associate the identifiers through the PSHELL attribute card; Output node, cell, and attribute information as a text file according to the NASTRAN format specification.

3. The method for constructing a heterogeneous human head model for multiphysics simulation according to claim 2, characterized in that, The process of generating files conforming to the NASTRAN standard also includes the following steps: The generated text file is checked for the continuity of node numbers, the connectivity of units, and the consistency of organizational identifiers.

4. The method for constructing a heterogeneous human head model for multiphysics simulation according to claim 1, characterized in that, It also includes the following steps: Based on the constructed heterogeneous human head simulation model, electromagnetic field simulation and absorption rate analysis of the radio frequency coil were performed using physical field simulation software.

5. The method for constructing a heterogeneous human head model for multiphysics simulation according to claim 1, characterized in that, The tissue types mentioned include white matter, gray matter, and cerebrospinal fluid.

6. The method for constructing a heterogeneous human head model for multiphysics simulation according to claim 1, characterized in that, The three-dimensional surface geometric model was generated using a format conversion program.

7. The method for constructing a heterogeneous human head model for multiphysics simulation according to claim 1, characterized in that, The preprocessing includes noise suppression, intensity normalization, and spatial registration.

8. The method for constructing a heterogeneous human head model for multiphysics simulation according to claim 1, characterized in that, The material properties mentioned include frequency-dependent conductivity and relative permittivity.

9. The method for constructing a heterogeneous human head model for multiphysics simulation according to claim 1, characterized in that, The FAST algorithm is used to perform image segmentation on the three-dimensional image data volume.

10. A heterogeneous human head model construction system for multiphysics simulation, characterized in that, For implementing the heterogeneous human head model construction method for multiphysics simulation as described in any one of claims 1-9, the system includes: The image processing module is used to acquire T1-weighted magnetic resonance imaging data of the head and obtain a three-dimensional image data volume through preprocessing. The image segmentation module is used to segment the three-dimensional image data volume, classify and label voxels according to tissue type, and obtain a three-dimensional segmentation mask with tissue labels. The geometric reconstruction and mapping module is used to reconstruct the surface based on the three-dimensional segmentation mask, obtain a three-dimensional surface geometric model, assign a unique identifier to each tissue type, and form a composite geometry with multiple nodes. The format conversion module is used to identify the node coordinates, node connection relationships, and organization classification information in the composite geometry, and generate a file that conforms to the NASTRAN standard. An integration module is used to import the file into a physics simulation software, create selection sets corresponding to different tissue types, and assign corresponding material properties to each tissue type based on the selection sets, thereby completing the construction of a heterogeneous human head simulation model.

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