Transcranial electrical stimulation finite element simulation method and related device

Abstract

The invention discloses a transcranial electrical stimulation finite element simulation method and a related device, and the method comprises the steps: carrying out the high-frequency conductivity imaging based on magnetic resonance, and obtaining the high-frequency conductivity; according to the high-frequency conductivity, reconstructing a low-frequency conductivity tensor based on a magnetic resonance diffusion tensor imaging technology; constructing an individual head finite element model with voxel-level conductivity according to the reconstructed low-frequency conductivity tensor; and performing electric field solution on the individual head finite element model by using a finite element method to obtain an electrical stimulation electric field distribution simulation result under the specified electrode. According to the method, the limitation of brain tissue segmentation errors and isotropic hypothesis is avoided, the accuracy and physiological credibility of conductivity modeling and simulation are remarkably improved, and a reliable basis can be provided for individualized optimization design and clinical application of transcranial electrical stimulation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the cross field of medical imaging, computational medicine and neuromodulation engineering, and relates to a transcranial electrical stimulation finite element simulation method and related device. BACKGROUND

[0002] With the rapid development of non-invasive neuromodulation technology, transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), time-interference electrical stimulation (TIs) and other transcranial electrical stimulation methods have been widely used in cognitive neuroscience and neurorehabilitation clinics. However, the conduction path of the applied current in the complex head tissue is significantly affected by the anatomical structure and the electrical properties of the tissue. If there is a lack of accurate brain tissue conductivity modeling, the prediction results of the electric field distribution using the finite element method will deviate significantly, making it difficult to individualize the optimization of clinical treatment parameters.

[0003] Currently, traditional transcranial electrical stimulation finite element simulation usually includes the following steps: first, automatic or manual tissue segmentation is performed from the magnetic resonance image (MRI) of the subject to obtain a three-dimensional model of the scalp, skull, cerebrospinal fluid, gray matter, white matter and other tissues; then, a finite element mesh is generated using a meshing tool to ensure sufficient mesh accuracy in the electrode area and tissue boundary; then, assign appropriate conductivity parameters to each tissue region, and define the electrode shape, size, position and boundary conditions of the injected current or voltage according to the experimental scheme; on this basis, establish the control equation, solve the electric potential and electric field intensity inside the head by the finite element method; finally, post-processing and visualization analysis of the results are performed to evaluate the electric field distribution characteristics and target area electric field intensity under different electrode arrangements and stimulation parameters, thereby providing a basis for optimizing the safety and effectiveness of transcranial electrical stimulation.

[0004] However, the above transcranial electrical stimulation finite element simulation process, although it can reconstruct the electric field distribution more finely, also has several drawbacks: first, the modeling process relies on high-quality MRI imaging and accurate tissue segmentation, which is often time-consuming and labor-intensive and is easily affected by segmentation errors; second, the conductivity of different tissues varies greatly between individuals and in different states, but the simulation usually uses the average value or simplified setting in the literature, ignoring the real physiological variability; third, the fine degree of finite element mesh division of multiple tissues has a great impact on the amount of calculation, making it difficult to meet the clinical needs of high precision and fast individualized prediction at the same time; finally, current transcranial electrical stimulation simulation is mostly based on the isotropic and segmented assumption of tissue conductivity, which cannot accurately reflect the anisotropic conductivity characteristics of brain tissue and the dynamic physiological environment, thereby limiting the authenticity of the results and the clinical promotion value. SUMMARY

[0005] The application aims to provide a transcranial electrical stimulation finite element simulation method and related device, and solve the problems of unclear individual conductivity, missing anisotropic conductivity and limited model resolution in the prior art.

[0006] To achieve the above-mentioned purpose, the application adopts the following technical solutions: A transcranial electrical stimulation finite element simulation method, comprising: Performing high-frequency conductivity imaging based on magnetic resonance to obtain high-frequency conductivity; According to the high-frequency conductivity, performing reconstruction of a low-frequency conductivity tensor based on magnetic resonance diffusion tensor imaging technology; According to the reconstructed low-frequency conductivity tensor, constructing an individual head finite element model with voxel-level conductivity; Solving an electric field of the individual head finite element model using a finite element method to obtain an electrical stimulation electric field distribution simulation result under a specified electrode.

[0007] Further, the method of high-frequency conductivity imaging based on magnetic resonance includes a method of solving a Helmholtz equation based on a magnetic resonance radio frequency field and a method of mapping conductivity based on brain tissue parameters of water content.

[0008] Further, the Helmholtz equation is:

[0009] wherein, is a magnetic resonance radio frequency field, is a vacuum magnetic permeability, is an angular frequency, is a high-frequency conductivity.

[0010] Further, in the method of mapping conductivity based on brain tissue parameters of water content, the relationship between the high-frequency conductivity and the water content is:

[0011] wherein, is a high-frequency conductivity, and is a fitting parameter, is water content.

[0012] Further, the low-frequency conductivity is:

[0013] wherein, is a low-frequency conductivity, is an extramembrane diffusion coefficient, is an intramembrane volume fraction, is an extramembrane average ion concentration.

[0014] Further, the method for constructing the individual head finite element model comprises the following steps: According to the magnetic resonance image of the head, tissue segmentation is performed, or according to the CT image, a skull mask is extracted to separate the scalp and brain tissue, and a scalp, skull and brain tissue mask is obtained; The scalp and skull are given an isotropic conductivity value, and the brain tissue is given an anisotropic brain tissue low-frequency conductivity tensor; The centroid and normal line of the head are calculated, the coordinates of the electrode system on the head model are predefined, and the electrode paste is filled in the gap between the electrode and the scalp to make it completely adhere to the scalp, so that an electrode and electrode paste mask is obtained; The electrode and electrode paste mask and the conductivity thereof are combined to obtain a head conductivity model; The head conductivity model is meshed using tetrahedral or hexahedral elements to discretize and construct an individual head finite element model.

[0015] Further, the process of using the finite element method to solve the electric field of the individual head finite element model comprises the following steps: The value of the head conductivity model is linearly interpolated using the corresponding voxel coordinates and the coordinates of the divided finite element grid nodes to obtain the voxel-level conductivity value of the finite element grid; Boundary conditions and current excitation are applied to the specified electrodes on the individual head finite element model, and then the control equation is solved by finite element discretization according to the voxel-level conductivity value of the finite element grid, so that the electric potential distribution is obtained, and the electric field distribution is further calculated.

[0016] A transcranial electrical stimulation finite element simulation system comprises: An imaging module for high-frequency conductivity imaging based on magnetic resonance to obtain high-frequency conductivity; A reconstruction module for reconstructing a low-frequency conductivity tensor based on magnetic resonance diffusion tensor imaging technology according to the high-frequency conductivity; A modeling module for constructing an individual head finite element model with voxel-level conductivity according to the reconstructed low-frequency conductivity tensor; A solving module for using the finite element method to solve the electric field of the individual head finite element model to obtain the electric stimulation electric field distribution simulation result under the specified electrodes.

[0017] A terminal device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method.

[0018] A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the steps of the method.

[0019] Compared with the prior art, the present application has the following beneficial effects: The present application provides a transcranial electrical stimulation finite element simulation method, which reconstructs the low-frequency electrical conductivity tensor based on magnetic resonance diffusion tensor imaging technology according to the high-frequency electrical conductivity based on magnetic resonance high-frequency electrical conductivity imaging. The magnetic resonance electrical conductivity imaging technology is combined with magnetic resonance diffusion tensor imaging (DTI), the high-frequency electrical conductivity is converted to the electrical stimulation frequency range by using the microstructure modeling technology, and the electrical conductivity tensor is calculated, so that the anisotropic brain tissue low-frequency electrical conductivity model is obtained. Then, the head electrical conductivity model is constructed according to the brain tissue low-frequency electrical conductivity tensor, which is used for subsequent transcranial electrical stimulation simulation. Finally, the head finite element model is constructed according to the previously calculated head electrical conductivity model, the individual head finite element model is numerically discretized and solved by using the finite element method, and the electrical field simulation results of the electrical stimulation under the specified electrode are calculated. The present application uses multi-modal magnetic resonance imaging technology, combines electromagnetic field theory and finite element numerical calculation, reconstructs the three-dimensional anisotropic electrical conductivity tensor of brain tissue under low-frequency electrical field, directly introduces the magnetic resonance electrical conductivity tensor imaging results into the transcranial electrical stimulation simulation process, avoids the mode of relying on tissue segmentation and then assigning uniform electrical conductivity in the traditional method, thereby reducing the cumulative error caused by segmentation error and electrical conductivity simplification, and realizes direct and accurate modeling of the electrical characteristics of brain tissue. The individualized head finite element model is constructed under the voxel-level precision electrical conductivity, the electrode and conductive paste are automatically configured, and the electrical potential and electrical field distribution are obtained by combining the finite element numerical solution, so that the effect prediction of different electrode arrangements and stimulation parameters is realized. Compared with the traditional method, the present application avoids the limitations of brain tissue segmentation error and isotropic assumption, significantly improves the accuracy and physiological credibility of electrical conductivity modeling and simulation, and can provide reliable basis for individualized optimization design and clinical application of transcranial electrical stimulation. At the same time, the present application uses the magnetic resonance instrument to non-invasively measure the electrical conductivity of the tissue, compared with the electrical impedance imaging (EIT) method which needs to wear electrodes to inject current, the imaging accuracy is improved while the discomfort of the subjects is greatly reduced. Compared with the traditional electrical stimulation finite element simulation method, the present application directly images the electrical conductivity of brain tissue without tissue segmentation, which greatly reduces the adverse effects of the accuracy of brain tissue segmentation. The present application fuses the voxel-level electrical conductivity tensor into the finite element simulation process, compared with using the traditional isotropic and uniform prior head model, the present application can use the real individual head model to realize more accurate electrical stimulation simulation, without significantly increasing the calculation time of simulation. The present application can not only be applied to the exploration of brain current distribution in basic research, but also can provide reference for individualized transcranial electrical stimulation parameter optimization in clinic, so as to improve the targeting and safety of stimulation, and has strong practical application value and popularization potential.

[0020] Further, the application establishes an anisotropic conductivity tensor model by combining DTI information with high-resolution conductivity images obtained under high-field MRI, effectively compensates for the deficiency of ignoring tissue anisotropy in traditional simulation, and thus more truly reflects the influence of white matter fiber orientation on current distribution, and improves the physiological credibility of simulation.

[0021] Further, the application adopts voxel-level finite element modeling, and maps the conductivity distribution to the grid through a linear interpolation method, breaks through the limitation of limited resolution of traditional finite element modeling, realizes high-precision simulation of complex brain tissue details and electrode-tissue interfaces, and can more accurately predict local electric field strength and distribution.

[0022] Further, the application can be combined with a standardized electrode system (such as 10-10, 10-20) or a self-defined electrode arrangement, support full-automatic configuration and boundary condition setting, greatly reduce the complexity of modeling and simulation operation, and improve the universality and portability of the method.

[0023] Further, the magnetic resonance conductivity imaging technology directly images the high-frequency conductivity of brain tissue of a target subject at a Larmor frequency (about 128 MHz under a 3T field strength) based on tissue parameter mapping or solving Helmholtz equation, and directly obtains a high-resolution conductivity image of brain tissue, without the need for segmenting brain tissue. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0025] Figure 1 The transcranial electrical stimulation finite element simulation method of the application is shown in the flowchart.

[0026] Figure 2 The process of directly solving high-frequency conductivity process images by magnetic resonance radio frequency field of the application is shown in the flowchart.

[0027] Figure 3 The process of calculating high-frequency conductivity process images by multi-level tissue parameter mapping of the application is shown in the flowchart.

[0028] Figure 4 The process of reconstructing low-frequency conductivity tensor based on DTI of the application is shown in the flowchart.

[0029] Figure 5 The process of constructing a head conductivity model of the application is shown in the flowchart.

[0030] Figure 6 The process diagram for constructing the head finite element model and solving the electric field of the application.

[0031] Figure 7 The transcranial electric stimulation finite element simulation system structure diagram of the preferred embodiment of the application.

[0032] Figure 8 The electronic device structure diagram of the preferred embodiment of the application. DETAILED DESCRIPTION

[0033] The exemplary embodiments of the present application are described herein with reference to the accompanying drawings in order to be able to understand various details of the embodiments of the present application and to implement the present application. It should be understood that the detailed description provided herein and the specific examples given to assist in understanding the present application are given by way of illustration only, and therefore various changes and modifications in form and details can be made without departing from the scope and spirit of the present application. Also, in the following description, descriptions of well-known functions and constructions are omitted in order to make the present application more clear and concise.

[0034] It is apparent that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0035] It should be noted that the terminal involved in the embodiments of the present application can include, but is not limited to, a mobile phone, a personal digital assistant (PDA), a wireless handheld device, a tablet computer, a personal computer (PC), an MP3 player, an MP4 player, a wearable device (for example, smart glasses, a smart watch, a smart bracelet, etc.), a smart home device, and the like.

[0036] In addition, the term "and / or" in this paper is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper generally represents an "or" relationship between the front and rear associated objects.

[0037] The application will be further described in detail below with reference to the accompanying drawings: Reference Figure 1The application provides a transcranial electrical stimulation finite element simulation method, which proposes an electrical stimulation simulation algorithm using a voxel-level conductivity tensor, and can measure the brain tissue conductivity of a subject by a non-invasive method and be used for electrical stimulation finite element simulation. The application includes three core modules of a magnetic resonance conductivity imaging method, a low-frequency conductivity tensor reconstruction method based on DTI, a voxel-level conductivity head finite element model construction and an electric field solving, and realizes the whole process from data acquisition to simulation calculation through systematic integration. Specifically, the following steps are included: Step 1, the magnetic resonance-based high-frequency conductivity imaging method mainly includes two different methods: a Helmholtz equation solving method based on a magnetic resonance radio frequency field (B1 + ) and a brain tissue parameter mapping conductivity method based on water content, both of which can obtain the high-frequency conductivity of brain tissue, and the tissue parameter mapping imaging method has clear brain tissue structure; and the conductivity error of the Helmholtz method is small and the imaging is fast. The application can select any one of the two imaging methods as needed, and the two methods respectively include the following specific steps: Step 1.1, as shown in Figure 2 , the Helmholtz equation solving method based on the magnetic resonance radio frequency field B1 + , the in-vivo electric field distribution formed by the radio frequency field B1 + includes its amplitude and phase, which usually needs to be measured respectively, and the application uses a phase-only solving method without measuring the amplitude, while the B1 + phase currently has no direct measurement method, and there are various estimation methods for the B1 + phase, the application measures the transmit-receive phase of magnetic resonance through an SE (spin-echo) or mGRE (multi-echo gradient echo) sequence, and estimates the phase of B1 + as half of the transmit-receive phase according to the transmit-receive phase assumption. For the solving of the Helmholtz equation, the convection reaction method using only the phase is used in the application, which is based on the Maxwell equation and derives the relationship between the conductivity and the B1 + phase. Specifically, under the high-frequency approximation, the relationship between the electrical property parameters of the tissue and the radio frequency field B1+ can be written in the following Helmholtz equation form:

[0038] Among them, is the magnetic resonance radio frequency field, is the vacuum permeability, is the angular frequency, is the high-frequency conductivity. In order to stabilize the solving of the equation, the is introduced into the equation, wherein D is the artificial diffusion coefficient, usually determined empirically, effectively suppresses the noise amplification and improves the reconstruction stability, and finally the high-frequency conductivity of the brain tissue can be inversed by solving the above equation .

[0039] Step 1.2, as shown in Figure 3 , for the tissue parameter mapping method, refers to taking some measurable parameters of the tissue as the basis to establish the relationship between the known tissue parameters and the conductivity to map the conductivity value, and in the present application, the water content of the measurable brain tissue is taken as an example. Since the cell membrane is an electrical insulator at low frequencies, it will hinder the flow of ions inside and outside the cells, and at high frequencies (MHz), the capacitive effect of the membrane structure begins to work, showing penetration, which can pass through the membrane structure, so that the current can enter the membrane. This method is based on this physiological basis, and the size of the water content in the tissue at high frequencies can reflect the flow capacity of ions, that is, the size of the conductivity. The relationship between the high-frequency conductivity of the tissue and the water content can be approximately expressed as:

[0040] wherein, and are fitting parameters, which can be calibrated by in vitro experiments or fitted by reference values from literature. The water content of the tissue has various measurement methods, including proton density weighted, T1 / T2 weighted imaging methods, which can be measured by various magnetic resonance sequences. The present application does not directly measure the T1 value of the tissue, uses a spin echo (SE) sequence, adopts a method of measuring twice the repetition time (TR) , calculates the image ratio of the two measurements, and combines the signal equation of the SE sequence to obtain:

[0041] wherein, is the longitudinal relaxation time, and are the angles of the excitation and refocusing pulses, is the signal gain at different echo times. This equation directly integrates the T1 information of the tissue without the need for T1 measurement, and the water content of the tissue can be directly obtained by the following formula: wherein, and are fitting parameters, which can be calibrated by experiments. Through the above two mappings, the high-frequency conductivity can be calculated by the tissue mapping method.

[0042] Step 2, since electrical stimulation usually uses relatively low frequency current to stimulate, the present application is based on the magnetic resonance diffusion tensor imaging (DTI) technology, and the low frequency conductivity tensor is reconstructed from the high frequency conductivity. The tissue conductivity in the low frequency range is closely related to the microscopic water molecule diffusion characteristics, and the conductivity tensor is highly related to the diffusion tensor of water. Based on the above relationship, the low frequency conductivity tensor can be reconstructed using DTI data. The present application uses the magnetic resonance EPI scanning sequence to perform multi-b-value DTI imaging to realize subsequent microscopic structure modeling. The acquired original DTI needs to be preprocessed, including image denoising, eddy current effect correction image distortion, etc., to ensure the accuracy of the subsequent calculation steps. The above preprocessing can be completed by various open source Toolboxes. The present application uses the DTI microscopic structure imaging technology to reconstruct the low frequency conductivity, which can be approximately expressed as the product of the average ion concentration inside and outside the membrane and the intrinsic diffusion coefficient of the biological tissue. The low frequency conductivity can be obtained by extrapolating inside and outside the membrane as follows:

[0043] wherein, is the intracellular volume fraction, and are the extracellular ion concentration and extracellular diffusion coefficient, respectively. Since the membrane structure of the tissue has high impedance at low frequency, it is difficult for the current to enter the cell, so it is assumed that the current of the brain tissue under low frequency electrical stimulation is mainly in the extracellular space, and the low frequency conductivity can be expressed as follows:

[0044] Since the extracellular diffusion coefficient and , and and have the following relationship:

[0045]

[0046] By substituting into the expression, the low frequency conductivity can be calculated. The preprocessed DTI data can be used to model the microscopic structure by using tools such as Dmipy, and the diffusion characteristics of water molecules in the brain tissue can be analyzed by using a two-compartment model to estimate the microscopic structure parameters, so that the intracellular volume fraction , the intrinsic diffusion coefficient , and the high frequency conductivity calculated in step 1 can be combined to calculate the low frequency conductivity of the tissue Further, the diffusion tensor provided by DTI is used to obtain the anisotropic low-frequency conductivity tensor , assuming that the conductivity tensor is highly correlated with the water diffusion tensor in spatial distribution .

[0047] Step 3, the present application constructs the individual head finite element model with voxel-level conductivity, and can automatically configure the electrode or customize the electrode position, and finally uses the finite element method to solve the electric field, so as to obtain the simulation result of the electric stimulation electric field distribution under the specified electrode.

[0048] First, tissue segmentation is performed according to the T1 image of the head, such as an automatic method of neural network, SPM, or the scalp and brain tissue are separated by extracting the skull mask from the CT image, to obtain the scalp, skull and brain tissue masks. Due to the complex structure of the scalp, containing uneven water, fat, muscle and other components, it is difficult to accurately measure the distribution of its conductivity, while the skull structure is relatively simple but thin, and the conductivity imaging is difficult to capture its structure, so the scalp and skull are endowed with isotropic conductivity values, and the brain tissue is endowed with the anisotropic brain tissue low-frequency conductivity tensor calculated in step 2. Then calculate the head centroid and normal, you can predefine the 10-10, 10-20 electrode system, place it on the head model, or define a number of electrode pairs on the head coordinates and place them on the scalp, fill the gap between the electrode and the scalp with conductive paste to make it completely adhere to the scalp, and obtain the electrode and conductive paste mask, wherein the conductivity of the electrode and the conductive paste is set to a fixed isotropic conductivity. Combine all the masks and their conductivities to obtain the final head conductivity model. The mask composed of the scalp, electrode and conductive paste is filled using methods such as flood fill algorithm to build a head finite element model, and then the filled head is discretized into a grid composed of a finite number of elements, which can be tetrahedron, hexahedron or other suitable calculation element form. To ensure the accuracy and efficiency of numerical calculation, denser grid division can be used in areas with geometric details or large physical field gradient changes, while sparser grid division can be used in areas with relatively uniform field distribution. The final finite element grid can reduce the amount of calculation while ensuring the accuracy of the calculation, providing a basis for subsequent material attribute assignment and electric field numerical solution. Then, the value of the head conductivity model is linearly interpolated using the corresponding voxel coordinates and the coordinates of the grid nodes to obtain the voxel-level conductivity value of the finite element grid, which is used for subsequent finite element solution. Then apply boundary conditions and current excitation to the specified electrodes on the model, and then solve the control equation by finite element discretization to obtain the potential distribution and further obtain the electric field distribution. In specific implementation, the above process can be realized by using the open source finite element solver GETDP, that is, the model structure is defined by the geometry modeling and grid division file, the material properties and boundary conditions are set by the problem definition file, and the built-in solver is called to complete the numerical calculation of the electric field. This method can accurately reflect the electric field distribution in the brain tissue, and has the advantages of simple implementation, high calculation efficiency and wide application range.

[0049] Step 4: After completing the finite element method, the method of this invention can output the electrical distribution parameters of the head under the target current for a specified electrode, including the potential distribution and electric field intensity distribution. These results can be visualized in three-dimensional space to reflect the effects of electrical stimulation on different tissue structures. Simultaneously, quantitative indicators of specific regions of interest (ROIs), such as the average electric field intensity, peak current density, or field gradient of the target area, can be extracted for further analysis and optimization. Based on the calculation results, this invention can compare and analyze individualized models. For example, it can evaluate the influence of different electrode arrangements and stimulation parameters on the electric field distribution in the target area, thereby selecting the optimal parameter combination that achieves the desired electric field intensity within the target area while reducing overstimulation in non-target areas. This method can also be used for studies of differences between individuals, enabling quantitative evaluation of the individualized effects of electrical stimulation. Through the above output and analysis, this invention can accurately predict the effects of electrical stimulation under individualized conductivity distributions, ensuring that the simulation results are more consistent with actual physiological conditions, thus providing a reliable basis for the optimized design of clinical electrical stimulation protocols.

[0050] The present invention will be further described in detail below through specific embodiments: Example 1: In this embodiment, the finite element simulation of electrical stimulation based on low-frequency conductivity imaging of magnetic resonance includes four steps: high-frequency conductivity reconstruction, low-frequency conductivity reconstruction, construction of head conductivity model, finite element modeling and discrete solution.

[0051] (1) High-frequency conductivity reconstruction: In this embodiment, the signal ratio is calculated using two spin echo images through the multi-level mapping relationship of brain tissue parameters. The water content of the tissue is mapped through the ratio relationship, and finally the high-frequency conductivity value is mapped from the water content.

[0052] (2) Low-frequency conductivity reconstruction: such as Figure 4 As shown, DTI images acquired from magnetic resonance EPI sequences are first preprocessed, such as image denoising and eddy current correction, to improve the signal noise and distortion of the images. Then, the microstructure is modeled to calculate the ion concentration and diffusion coefficient. Combined with its own diffusion tensor and the calculated high-frequency conductivity, after all of them are registered, the low-frequency conductivity of brain tissue can be calculated.

[0053] (3) Construct a head conductivity model: such as Figure 5As shown, tissue segmentation was performed on the T1-weighted images acquired by magnetic resonance imaging (MRI), and masks of the scalp, skull, and brain tissue were extracted. Simultaneously, the centroid of the head was calculated, and electrodes were automatically positioned onto the scalp using normals. The gaps between the electrodes and the scalp were filled with conductive paste, resulting in electrode and conductive paste masks. Then, the calculated anisotropic low-frequency conductivity tensor was assigned to the brain tissue, and the corresponding isotropic conductivity was assigned to the other masks: 0.465 S / m for the scalp, 0.02 S / m for the skull, and 5.9 × 10⁻⁶ for the electrodes. 7 With a conductivity of S / m and a conductive paste of 0.3 S / m, the head conductivity model can be obtained by combining all the masks.

[0054] (4) Finite element modeling and discrete solution: such as Figure 6 As shown, the scalp, electrodes, and conductive ointment mask are first combined, and the interior is completely closed using a flood-fill algorithm. Then, tetrahedral elements are used to discretize it into a finite element mesh. Next, based on the voxel coordinates of the head conductivity model and the node coordinates of the finite element mesh, the conductivity tensor is linearly interpolated onto the head finite element model to obtain a voxel-level conductivity mesh. Then, boundary conditions are set, and the processed finite element mesh is discretized and solved. Finally, the calculated electric field is visualized in three dimensions to obtain the three-dimensional electric field of the head under electrical stimulation.

[0055] Example 2: This invention also provides a transcranial electrical stimulation finite element simulation system, such as... Figure 7 As shown, the system includes: an imaging module, a reconstruction module, a modeling module, and a solution module.

[0056] The imaging module is used to perform high-frequency conductivity imaging based on magnetic resonance to obtain high-frequency conductivity. The reconstruction module is used to reconstruct the low-frequency conductivity tensor based on magnetic resonance diffusion tensor imaging technology, according to the high-frequency conductivity. The modeling module is used to construct a finite element model of an individual head with voxel-level conductivity based on the reconstructed low-frequency conductivity tensor. The solver module is used to solve the electric field of an individual head finite element model using the finite element method, and obtain the simulation results of the electric field distribution under the specified electrodes.

[0057] It is understood that the transcranial electrical stimulation finite element simulation system provided by the present invention corresponds to the transcranial electrical stimulation finite element simulation method provided in the foregoing embodiments. The relevant technical features of the transcranial electrical stimulation finite element simulation system can be referred to the relevant technical features of the transcranial electrical stimulation finite element simulation method, and will not be repeated here.

[0058] Another object of the present invention is to provide an electronic device, such as... Figure 8As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor performing the steps of the transcranial electrical stimulation finite element simulation method.

[0059] The transcranial electrical stimulation finite element simulation method includes the following steps: High-frequency conductivity is obtained by performing high-frequency conductivity imaging based on magnetic resonance. Based on high-frequency conductivity, low-frequency conductivity tensors are reconstructed using magnetic resonance diffusion tensor imaging technology. Based on the reconstructed low-frequency conductivity tensor, a finite element model of an individual head with voxel-level conductivity is constructed. The electric field of an individual head finite element model was solved using the finite element method, and the simulation results of the electric field distribution under the specified electrodes were obtained.

[0060] A fourth objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the transcranial electrical stimulation finite element simulation method.

[0061] The transcranial electrical stimulation finite element simulation method includes the following steps: High-frequency conductivity is obtained by performing high-frequency conductivity imaging based on magnetic resonance. Based on high-frequency conductivity, low-frequency conductivity tensors are reconstructed using magnetic resonance diffusion tensor imaging technology. Based on the reconstructed low-frequency conductivity tensor, a finite element model of an individual head with voxel-level conductivity is constructed. The electric field of an individual head finite element model was solved using the finite element method, and the simulation results of the electric field distribution under the specified electrodes were obtained.

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A transcranial electrical stimulation finite element simulation method, characterized by, The method comprises the following steps: Based on magnetic resonance, high-frequency conductivity imaging is performed to obtain high-frequency conductivity; Based on the high-frequency conductivity, the low-frequency conductivity tensor is reconstructed based on the magnetic resonance diffusion tensor imaging technology; According to the reconstructed low-frequency conductivity tensor, an individual head finite element model with voxel-level conductivity is constructed; The finite element method is used to solve the electric field of the individual head finite element model to obtain the simulation result of the electric stimulation electric field distribution under the specified electrode.

2. The transcranial electrostimulation finite element simulation method of claim 1, wherein, The method of high-frequency conductivity imaging based on magnetic resonance includes a Helmholtz equation method based on magnetic resonance radio frequency field and a brain tissue parameter mapping conductivity method based on water content.

3. The method of claim 2, wherein, The Helmholtz equation is: wherein is the magnetic resonance radio frequency field, is the vacuum permeability, is the angular frequency, is the high frequency conductivity.

4. The method of claim 2, wherein, In the brain tissue parameter mapping conductivity method based on water content, the relationship between high-frequency conductivity and water content is: wherein is the high frequency conductivity, and is the fitting parameter, is the water content.

5. The method of claim 1, wherein, The low-frequency conductivity is: wherein, is the low frequency conductivity, is the membrane outer diffusion coefficient, is the membrane inner volume fraction, is the membrane outer average ion concentration.

6. The method of claim 1, wherein, The construction method of the individual head finite element model is: According to the magnetic resonance image of the head, the tissue is segmented, or according to the CT image, the skull mask is extracted to separate the scalp and brain tissue, and the scalp, skull and brain tissue masks are obtained; The isotropic conductivity value is assigned to the scalp and skull, and the anisotropic brain tissue low-frequency conductivity tensor is assigned to the brain tissue; The head centroid and normal are calculated, the coordinates of the electrode system on the head model are predefined, the conductive paste is filled in the gap between the electrode and the scalp to make it completely fit the scalp, and the electrode and conductive paste masks are obtained; The head conductivity model is obtained by combining all the electrode and conductive paste masks and their conductivities. The head conductivity model is meshed with tetrahedral or hexahedral elements to discretize and construct the individual head finite element model.

7. The method of transcranial electrostimulation finite element simulation according to claim 6, wherein, The process of using the finite element method to solve the electric field of the individual head finite element model includes: The value of the head conductivity model is linearly interpolated using the corresponding voxel coordinates and the coordinates of the divided finite element grid nodes to obtain the voxel-level conductivity value of the finite element grid; The boundary conditions and current excitation are applied to the specified electrode on the individual head finite element model, and then the control equation is solved by finite element discretization according to the voxel-level conductivity value of the finite element grid, so as to obtain the potential distribution and further calculate the electric field distribution.

8. A transcranial electrical stimulation finite element simulation system, comprising: The method comprises the following steps: An imaging module is used to perform high-frequency conductivity imaging based on magnetic resonance to obtain high-frequency conductivity; A reconstruction module is used to reconstruct the low-frequency conductivity tensor based on the high-frequency conductivity and the magnetic resonance diffusion tensor imaging technology; A modeling module is used to construct an individual head finite element model with voxel-level conductivity according to the reconstructed low-frequency conductivity tensor; A solving module is used to solve the electric field of the individual head finite element model using the finite element method to obtain the simulation result of the electric stimulation electric field distribution under the specified electrode.

9. A terminal device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the method of any one of claims 1-7.

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