A finite element modeling method and device for calculating electric field strength in a targeted target range

Abstract

The present application discloses a finite element modeling method and device for calculating the electric field strength of a target range. The scheme is: obtaining the imaging information of the target range, the target range includes the target target; extracting the boundary layer information of the target range from the imaging information, and the finite element software uses the boundary layer information to construct a geometric shell model of the target range; determining the distribution position of the electrode sheet, and constructing a geometric shell model with the electrode sheet according to the distribution position; modifying the imaging information according to the physical and electrical parameters of the target range, loading the imaging information containing the physical and electrical parameters into the geometric shell model with the electrode sheet, and meshing the loaded geometric shell model; setting the boundary conditions according to the preset voltage of the electrode sheet, and the finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet, and the physical and electrical parameters to solve and obtain the electric field strength of the target range. Simplify the modeling process and improve modeling efficiency.

Description

Technical Field

[0001] The present application relates to the field of computer simulation, and in particular to a finite element modeling method and device for calculating electric field strength in a targeted target range. Background Art

[0002] At present, a new non-invasive in vivo tumor treatment method is to use an electric field of a certain frequency and intensity to treat solid tumors. The specific implementation method is to fix two sets of electrodes on the superficial skin corresponding to the diseased tissue area, apply a voltage of a certain frequency to the electrode, so as to form a certain distribution of electric fields in the diseased tissue area of ​​the human body, and achieve treatment of specific tumors by changing the voltage peak, frequency and position of the electrode. In this process, it is necessary to obtain the electric field distribution form in the diseased tissue of the human body. The existing method mainly obtains the electric field distribution of the in vivo tissue (the diseased tissue area of ​​the human body) through simulation calculation through finite element modeling. Taking brain tumors as an example, the human head structure mainly includes scalp, skull, cerebrospinal fluid, white matter and gray matter. The geometric entities of each of the aforementioned in vivo tissues are constructed in turn, and finally combined into a complete finite element model based on the diseased tissue.

[0003] However, the existing modeling methods have many drawbacks. Due to the complex structure of brain tissue itself, for example, the existence of grooves and folds in white matter and gray matter leads to many tiny curved surfaces. Therefore, there will be large errors in the restoration of the corresponding microstructure when establishing the corresponding geometric model. In addition, the error will be further amplified if the mesh is not detailed enough in the finite element mesh division. Therefore, the disadvantages of this method are low accuracy, complex modeling process, and high requirements on computer performance. Summary of the invention

[0004] The embodiment of the present application provides a finite element modeling method and device for calculating electric field strength in a targeted target range to solve the problems existing in the related art. The technical solution is as follows:

[0005] In a first aspect, an embodiment of the present application provides a finite element modeling method for calculating electric field strength in a targeted target range, comprising:

[0006] Acquiring imaging information of a targeted target range, wherein the targeted target range includes the targeted target;

[0007] Extracting boundary layer information of the target target range from the imaging information, and using finite element software to construct a geometric shell model of the target target range using the boundary layer information;

[0008] Determining the distribution positions of the electrode sheets, and constructing a geometric shell model with the electrode sheets according to the distribution positions;

[0009] Modifying the imaging information according to the physical and electrical parameters of the target range, loading the imaging information including the physical and electrical parameters into the geometric shell model with the electrode sheet, and meshing the loaded geometric shell model;

[0010] The boundary conditions are set according to the preset voltage of the electrode sheet, and the finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet, and the physical and electrical parameters to solve and obtain the electric field strength in the targeted target range.

[0011] In one embodiment, it further includes:

[0012] Segmenting the target target range according to the imaging information of the target target range;

[0013] A corresponding calibration code is assigned to each tissue component, and a three-dimensional space matrix of the targeted target range is constructed according to each calibration code.

[0014] In one embodiment, boundary layer information of the target target range is extracted from the imaging information, and finite element software uses the boundary layer information to construct a geometric shell model of the target target range, including:

[0015] In the three-dimensional space matrix, extracting boundary layer information of the targeted target range, the boundary layer information including three-dimensional coordinate values ​​of a plurality of boundary points;

[0016] The three-dimensional coordinate values ​​of the plurality of boundary points are stored in a first text file, and the finite element software uses the first text file to perform boundary fitting to obtain a geometric shell model of the targeted target range.

[0017] In one embodiment, determining the distribution positions of the electrode sheets and constructing a geometric shell model with the electrode sheets according to the distribution positions includes:

[0018] According to the relative position relationship between the geometric shell model and the target object, the distribution position of the electrode sheet is calculated using a neural network algorithm of error back propagation;

[0019] Storing the three-dimensional coordinate values ​​corresponding to the distribution positions of the electrode sheets into a second text file;

[0020] In the finite element software, the second text file is loaded into the geometric shell model to obtain the geometric shell model with the electrode sheet.

[0021] In one embodiment, modifying the imaging information according to the physical and electrical parameters of the target range includes:

[0022] Calculating and processing the three-dimensional space matrix of the targeted target range, and replacing the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;

[0023] The calibration code corresponding to each tissue component is replaced with the corresponding conductivity, and the physical and electrical parameters include the relative dielectric constant and the conductivity.

[0024] In one embodiment, loading the image information containing the physical and electrical parameters into the geometric shell model with the electrode sheet comprises:

[0025] storing the three-dimensional space matrix of the relative dielectric constant and the conductivity into a third text file;

[0026] In the finite element software, the third text file is loaded into the geometric shell model with the electrode sheet.

[0027] In one embodiment, it further includes:

[0028] The relative dielectric constant and the electrical conductivity are set according to the material of the electrode sheet and various environmental factors.

[0029] In a second aspect, a finite element modeling device for calculating electric field strength in a targeted target range comprises:

[0030] An imaging information acquisition module, used to acquire imaging information of a targeted target range, wherein the targeted target range includes the targeted target;

[0031] A geometric shell model construction module is used to extract boundary layer information of the target target range from the imaging information, and the finite element software uses the boundary layer information to construct a geometric shell model of the target target range;

[0032] A model building module with electrode sheets, used to determine the distribution positions of the electrode sheets and to build a geometric shell model with the electrode sheets according to the distribution positions;

[0033] A meshing module, used for modifying the imaging information according to the physical and electrical parameters of the target range, loading the imaging information including the physical and electrical parameters into the geometric shell model with the electrode sheet, and meshing the loaded geometric shell model;

[0034] The electric field strength calculation module is used to set boundary conditions according to the preset voltage of the electrode sheet. The finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet and the physical and electrical parameters to solve the electric field strength of the targeted target range.

[0035] In one embodiment, it further includes:

[0036] A tissue component segmentation module, used to perform tissue component segmentation on the target target range according to the imaging information of the target target range;

[0037] The spatial matrix construction module is used to assign a corresponding calibration code to each tissue component, and to construct a three-dimensional spatial matrix of the targeted target range according to each calibration code.

[0038] In one embodiment, the geometric shell model building module includes:

[0039] A boundary layer information extraction submodule, used to extract boundary layer information of the targeted target range in the three-dimensional space matrix, wherein the boundary layer information includes three-dimensional coordinate values ​​of a plurality of boundary points;

[0040] The geometric shell model construction submodule is used to store the three-dimensional coordinate values ​​of the multiple boundary points in a first text file, and the finite element software uses the first text file to perform boundary fitting to obtain the geometric shell model of the targeted target range.

[0041] In one embodiment, the model building module with electrode sheets comprises:

[0042] An electrode sheet position determination submodule, for calculating the distribution position of the electrode sheet using a neural network algorithm of error back propagation based on the relative position relationship between the geometric shell model and the target object;

[0043] The model building submodule with electrode sheets is used to store the three-dimensional coordinate values ​​corresponding to the distribution positions of the electrode sheets into a second text file; in the finite element software, the second text file is loaded into the geometric shell model to obtain the geometric shell model with electrode sheets.

[0044] In one embodiment, the grid division module includes:

[0045] A relative dielectric constant modification module is used to calculate and process the three-dimensional space matrix of the target target range, and replace the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;

[0046] The conductivity modification module is used to replace the calibration code corresponding to each tissue component with the corresponding conductivity, and the physical and electrical parameters include the relative dielectric constant and the conductivity.

[0047] In one embodiment, the grid division module includes:

[0048] The text loading submodule is used to store the three-dimensional space matrix of the relative dielectric constant and the conductivity into a third text file; in the finite element software, the third text file is loaded into the geometric shell model with the electrode sheet.

[0049] In one embodiment, it further includes:

[0050] The electrode sheet parameter setting module is used to set the relative dielectric constant and the conductivity according to the material of the electrode sheet and various environmental factors.

[0051] In a third aspect, an electronic device is provided, including:

[0052] at least one processor; and a memory communicatively coupled to the at least one processor;

[0053] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute any of the above methods.

[0054] In a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute any of the above methods.

[0055] One embodiment of the above application has the following advantages or beneficial effects: by extracting the boundary layer information of the imaging information of the target range, digitalizing it, converting it into a file format, connecting the data points into lines, connecting the lines into surfaces, and constructing a geometric shell model of the target range in the finite element software, and then constructing a geometric shell model with an electrode sheet according to the distribution position of the electrode sheet. Then, the imaging information modified by the physical and electrical parameters is loaded into the geometric shell model with the electrode sheet, and the loaded geometric shell model is meshed. Finally, the boundary conditions are set according to the preset voltage of the electrode sheet, and the finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet, and the physical and electrical parameters to solve the electric field intensity of the target range. The modeling process of the finite element software is effectively simplified, the modeling efficiency is improved, and the speed of calculating the electric field intensity is improved. The problem of low calculation accuracy, high calculation time cost, and high computer performance requirements in the existing geometric modeling method is solved.

[0056] Other effects of the above optional manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present application.

[0058] Figure 1 is a schematic diagram of a finite element modeling method for calculating electric field strength in a targeted target range according to an embodiment of the present application;

[0059] Figure 2 is an enhanced magnetic resonance imaging image of a patient's head according to an embodiment of the present application;

[0060] Figure 3 It is a schematic diagram of a three-dimensional space matrix represented by calibration codes based on nuclear magnetic resonance images according to an embodiment of the present application;

[0061] Figure 4 is a schematic diagram of a geometric shell model constructed at the outermost layer of a patient's skull according to an embodiment of the present application;

[0062] Figure 5 is a schematic diagram of a geometric shell model with an electrode sheet according to an embodiment of the present application;

[0063] Figure 6 is a schematic diagram of a three-dimensional space matrix represented by a relative dielectric constant based on a nuclear magnetic resonance image according to an embodiment of the present application;

[0064] Figure 7 is a schematic diagram of a three-dimensional space matrix represented based on the conductivity of a nuclear magnetic resonance image according to another embodiment of the present application;

[0065] Figure 8 is a cross-sectional cloud diagram of the electrical potential distribution of the patient's brain tissue according to an embodiment of the present application;

[0066] Fig. 9 is a cross-sectional cloud diagram of the electric field intensity distribution of the patient's brain tissue according to an embodiment of the present application;

[0067] Fig.10 is a two-dimensional slice cloud image of brain tissue near a patient's tumor according to another embodiment of the present application;

[0068] Fig.11 is a schematic diagram of a finite element modeling device for calculating electric field strength in a targeted target range according to another embodiment of the present application;

[0069] Fig.12 It is a block diagram of an electronic device used to implement a finite element modeling method for calculating electric field strength of a target targeting range according to an embodiment of the present application. DETAILED DESCRIPTION

[0070] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0071] like Figure 1As shown, in a specific embodiment, a finite element modeling method for calculating electric field strength in a targeted target range is provided, comprising the following steps:

[0072] Step S110: acquiring imaging information of a target range, where the target range includes the target;

[0073] Step S120: extracting boundary layer information of the target target range from the imaging information, and using the boundary layer information to construct a geometric shell model of the target target range using finite element software;

[0074] Step S130: determining the distribution positions of the electrode sheets, and constructing a geometric shell model with the electrode sheets according to the distribution positions;

[0075] Step S140: modifying the imaging information according to the physical and electrical parameters of the target range, loading the imaging information including the physical and electrical parameters into the geometric shell model with the electrode sheet, and meshing the loaded geometric shell model;

[0076] Step S150: Boundary conditions are set according to the preset voltage of the electrode sheet. The finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet, and the physical and electrical parameters to solve and obtain the electric field strength of the targeted target range.

[0077] In one example, the finite element modeling method for calculating the electric field strength of the target range can be applied to the target range of the lesion area of ​​the human body and animal body tissue, for example, the head, gastrointestinal tract, liver, lung, pancreas, ovary, etc. where the lesion occurs. The target range includes the target target, that is, the lesion location, for example, a tumor within the target range of the head, gastrointestinal tract, liver, lung, pancreas, ovary, etc., as the target target. Finite element modeling is mainly performed using finite element software, and the electric field strength distribution or potential distribution of the target range is obtained by simulation calculation. Of course, the definition of the target range and the target target is not limited to the above description, and can be adaptively adjusted according to actual conditions, all within the protection scope of this embodiment. This embodiment is described with the target range as the human body and animal body tissue. The method of obtaining the imaging information of the target range can adopt imaging technology, such as various sequences of nuclear magnetic resonance technology, PET-CT / MRI, CT, etc., to obtain the imaging information of the diseased part of the human body and animal body tissue, so as to determine the lesion area and the lesion location. For example, a brain tumor patient undergoes a head enhanced MRI to obtain a MRI image of the patient's head. Take one of the MRI images as an example. Figure 2 Of course, the method of obtaining imaging information may be different for different types of target ranges.

[0078] When extracting boundary layer information, i.e., imaging boundary, the imaging information can be first stored in a three-dimensional space matrix, and each image pixel corresponds to a three-dimensional coordinate value. Extracting boundary layer information is to extract the three-dimensional coordinate values ​​of the outermost image pixel points of the target range. The three-dimensional coordinate values ​​of the outermost image pixel points are stored in a text file and then imported into the finite element software for boundary fitting, and a geometric shell model of the target range can be obtained. According to the relative position relationship between the geometric shell model of the target range and the target, the number and optimal distribution position of the electrode sheet can be determined using a neural network algorithm of error back propagation. In the three-dimensional space matrix, the three-dimensional coordinate values ​​of the electrode sheet are extracted, and the three-dimensional coordinate values ​​of the electrode sheet are stored in a text file and imported into the geometric shell model to establish a geometric model of the electrode sheet, and a geometric shell model with an electrode sheet is constructed.

[0079] Different internal tissues can be distinguished based on imaging information, for example, brain tissue includes multiple different component tissues. Physical and electrical parameters exist objectively, for example, the relative node constant and conductivity of brain tissue. Modify imaging information based on the physical and electrical parameters of the target target range. In the three-dimensional space matrix of the target target range, all three-dimensional coordinate values ​​can be stored in text and imported into the finite element software. The modified imaging information is further loaded into the geometric shell model with electrode sheets, and the loaded geometric shell model is meshed. Boundary conditions are set according to the preset voltage of the electrode sheet, for example, the peak value and frequency of the applied alternating voltage are defined in the electrode sheet group on the geometric shell model, and the initial value of the simulation is set. The finite element software uses the boundary conditions, the meshed geometric shell model with electrode sheets, and the physical and electrical parameters to solve the electric field strength of the target target range, and finally extracts the finite element calculation results and draws the graph.

[0080] In the finite element software, the imaging information of the lesion area is not directly imported to build a model of the lesion area, but the information of the outermost layer of the lesion area is extracted to build a geometric shell model, and at the same time, a geometric model of the medical device set in the lesion area is built to obtain a geometric shell model with an electrode sheet. The whole process can effectively simplify the existing modeling process, and through finite element simulation calculation and analysis, the electric field distribution morphology of the lesion area (near the tumor) can be effectively and quickly obtained. Since this method is not subject to finite element constraints, it can be used for complex external and internal situations, such as human brain electric field simulation, which simplifies the model building process and improves work efficiency.

[0081] In this embodiment, by extracting the boundary layer information of the imaging information of the target range, digital entity is performed, converted into a file format, the data points are connected into lines, and the lines are connected into surfaces, and a geometric shell model of the target range is constructed in the finite element software, and then a geometric shell model with an electrode sheet is constructed according to the distribution position of the electrode sheet. Then, the imaging information modified by the physical and electrical parameters is loaded into the geometric shell model with the electrode sheet, and the loaded geometric shell model is meshed. Finally, the boundary conditions are set according to the preset voltage of the electrode sheet, and the finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet, and the physical and electrical parameters to solve the electric field intensity of the target range. The modeling process of the finite element software is effectively simplified, the modeling efficiency is improved, and the speed of calculating the electric field intensity is improved. The problems of low calculation accuracy, high calculation time cost, and high computer performance requirements in the existing geometric modeling method are solved.

[0082] In one implementation, after step S110, the method further includes:

[0083] Step S111: segmenting the target target range into tissue components according to the imaging information of the target target range;

[0084] Step S112: assigning a corresponding calibration code to each tissue component, and constructing a three-dimensional space matrix of the targeted target range according to each calibration code.

[0085] In one example, different tissues in the lesion area (target target range) are distinguished based on the MRI image, stored in a three-dimensional space matrix in the form of digital characters, and different characters are used to mark different tissues and tumors (target targets). For example, based on the enhanced MRI image of the skull, different tissues in the skull are distinguished, using code 0 to represent the outside air part, code 1 to represent the scalp part, code 2 to represent the skull part, code 3 to represent the spinal fluid part in the body, code 4 to represent the gray matter part, code 5 to represent the white matter part, and code 6 to represent the tumor part. Figure 3 shown.

[0086] In one implementation, step S120 includes:

[0087] Step S121: extracting boundary layer information of the target range in the three-dimensional space matrix, where the boundary layer information includes three-dimensional coordinate values ​​of multiple boundary points;

[0088] Step S122: storing the three-dimensional coordinate values ​​of the plurality of boundary points in a first text file, and performing boundary fitting on the first text file using finite element software to obtain a geometric shell model of the target range.

[0089] In one example, the target range is a three-dimensional space matrix of the brain, and the boundary layer information of the air domain and the skull tissue area can be extracted, including the three-dimensional coordinate values ​​of multiple boundary points, that is, the coordinate values ​​of the outermost layer of the scalp. The extracted coordinate values ​​are saved as a binary file, and the file is imported into the COMSOL finite element software for boundary fitting, and a geometric shell model of the brain can be obtained. The model does not contain the geometric structure of the brain, such as Figure 4 As shown in the figure, the outermost geometric model required for simulation is established by directly extracting the spatial position data of the outermost layer of the human tissue image and avoiding the complex internal geometry. The whole method is not subject to finite element constraints and can be used in situations with complex shapes and complex internal structures, such as human brain electric field simulation.

[0090] In one implementation, step S130 includes:

[0091] Step S131: according to the relative position relationship between the geometric shell model and the target, the distribution position of the electrode sheet is calculated using a neural network algorithm of error back propagation;

[0092] Step S132: storing the three-dimensional coordinate values ​​corresponding to the distribution positions of the electrode sheets into a second text file;

[0093] Step S133: In the finite element software, the second text file is loaded into the geometric shell model to obtain a geometric shell model with an electrode sheet.

[0094] In one example, the optimal position of the electrode patch distribution is calculated by the BP error back-propagation training (BP network) neural network. The electrode patches are distributed as a pair in front, back, left, and right. Each pair of electrode patches consists of 18 small electrodes. The electrode patch geometric model is added to the established skull shell model to obtain a geometric shell model with electrode patches, such as Figure 5 shown.

[0095] In one implementation, step S140 includes:

[0096] Step S141: Calculate and process the three-dimensional space matrix of the target range, and replace the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;

[0097] Step S142: replacing the calibration code corresponding to each tissue component with the corresponding conductivity, and the physical and electrical parameters include the relative dielectric constant and the conductivity.

[0098] In one example, a computer program, such as C language, Python language, Matlab, etc., is used to calculate and process each tissue component in the three-dimensional space matrix obtained in the previous step, and the calibration code corresponding to each tissue component is replaced with the corresponding relative dielectric constant, and the calibration code corresponding to each tissue component is replaced with the corresponding conductivity to obtain the replaced three-dimensional space matrix.

[0099] For example, the calibration codes corresponding to different tissues and tumors are replaced, and all calibration codes are replaced with corresponding relative dielectric constants, and all calibration codes are replaced with corresponding conductivity. According to the following table, the calibration codes of each brain tissue are replaced with corresponding relative dielectric constants one by one. The three-dimensional space matrix obtained after replacement is as follows: Figure 6 As shown, the replaced three-dimensional space matrix is ​​saved as a text document for easy calling by finite element software.

[0100]

[0101] According to the table below, replace the calibration code of each tissue component with the corresponding conductivity one by one. The three-dimensional space matrix obtained after replacement is as follows: Figure 7 As shown, the replaced data is saved as a text file for finite element calling.

[0102]

[0103] In this embodiment, the three-dimensional space matrix carrying the imaging information of the lesion area is simplified into a three-dimensional space matrix of relative dielectric constant and conductivity, and a script file is established and imported into the finite element software for modeling, thereby avoiding the use of imaging information to directly construct a geometric model of a complex structure, effectively simplifying the model building process and improving the model building efficiency.

[0104] In one implementation, step S140 further includes:

[0105] Step S143: storing the three-dimensional space matrix of relative dielectric constant and conductivity into a third text file;

[0106] Step S144: In the finite element software, the third text file is loaded into the geometric shell model with the electrode sheet;

[0107] Step S145: meshing the loaded geometric shell model.

[0108] In one example, a text document obtained from the three-dimensional space matrix of relative dielectric constant and conductivity is imported into the finite element software and loaded onto a skull-shaped shell model with electrodes. The skull shell model with electrodes is meshed. Since there are no geometric solid elements in the skull, the entire model can uniformly use an extremely fine tetrahedral mesh. The mesh division does not have to be consistent with the pixels, and the mesh type, mesh size, density distribution, etc. can be selected at will, which has obvious operating space for optimization calculations.

[0109] In the electrode group on the skull shell model, the peak value of the applied alternating voltage is defined as 59V and the frequency is 200kHz, and the initial value of the simulation is set to 0. In the finite element software, the electric potential distribution and electric field intensity distribution inside the entire brain tissue can be finally obtained by solving the current conservation equation through Ohm's law. Extract the finite element calculation results and draw the cross-sectional cloud map of the brain tissue potential distribution as shown in Figure 8 As shown, the cross-sectional cloud diagram of the electric field intensity distribution of brain tissue is drawn as follows Fig. 9 As shown, a two-dimensional cloud map of brain tissue slices near the tumor is drawn as follows Fig.10 shown.

[0110] In one embodiment, it further includes:

[0111] The relative dielectric constant and conductivity are set according to the material of the electrode sheet and various environmental factors.

[0112] In one example, the relative dielectric constant and conductivity may be set according to the material of the electrode sheet and the environmental factors may include temperature, humidity and other environmental factors, all of which are within the protection scope of this embodiment.

[0113] In another specific embodiment, Fig.11 As shown, a finite element modeling device for calculating electric field strength in a targeted target range is provided, comprising:

[0114] An imaging information acquisition module 110 is used to acquire imaging information of a target range, wherein the target range includes the target target;

[0115] A geometric shell model building module 120 is used to extract boundary layer information of the target target range from the imaging information, and the finite element software uses the boundary layer information to build a geometric shell model of the target target range;

[0116] A model building module 130 with electrode sheets is used to determine the distribution positions of the electrode sheets and build a geometric shell model with the electrode sheets according to the distribution positions;

[0117] A meshing module 140 is used to modify the imaging information according to the physical and electrical parameters of the target range, load the imaging information including the physical and electrical parameters into the geometric shell model with the electrode sheet, and mesh the loaded geometric shell model;

[0118] The electric field strength calculation module 150 is used to set boundary conditions according to the preset voltage of the electrode sheet. The finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet and the physical and electrical parameters to solve the electric field strength of the targeted target range.

[0119] In one embodiment, it further includes:

[0120] A tissue component segmentation module, used to perform tissue component segmentation on the target target range according to the imaging information of the target target range;

[0121] The spatial matrix construction module is used to assign a corresponding calibration code to each tissue component, and to construct a three-dimensional spatial matrix of the targeted target range according to each calibration code.

[0122] In one embodiment, the geometric shell model building module includes:

[0123] A boundary layer information extraction submodule, used to extract boundary layer information of the targeted target range in the three-dimensional space matrix, wherein the boundary layer information includes three-dimensional coordinate values ​​of a plurality of boundary points;

[0124] The geometric shell model construction submodule is used to store the three-dimensional coordinate values ​​of the multiple boundary points in a first text file, and the finite element software uses the first text file to perform boundary fitting to obtain the geometric shell model of the targeted target range.

[0125] In one embodiment, the model building module with electrode sheets comprises:

[0126] An electrode sheet position determination submodule, for calculating the distribution position of the electrode sheet using a neural network algorithm of error back propagation based on the relative position relationship between the geometric shell model and the target object;

[0127] The model building submodule with electrode sheets is used to store the three-dimensional coordinate values ​​corresponding to the distribution positions of the electrode sheets into a second text file; in the finite element software, the second text file is loaded into the geometric shell model to obtain the geometric shell model with electrode sheets.

[0128] In one embodiment, the grid division module includes:

[0129] A relative dielectric constant modification module is used to calculate and process the three-dimensional space matrix of the target target range, and replace the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;

[0130] The conductivity modification module is used to replace the calibration code corresponding to each tissue component with the corresponding conductivity, and the physical and electrical parameters include the relative dielectric constant and the conductivity.

[0131] In one embodiment, the grid division module includes:

[0132] The text loading submodule is used to store the three-dimensional space matrix of the relative dielectric constant and the conductivity into a third text file; in the finite element software, the third text file is loaded into the geometric shell model with the electrode sheet.

[0133] In one embodiment, it further includes:

[0134] The electrode sheet parameter setting module is used to set the relative dielectric constant and the conductivity according to the material of the electrode sheet and various environmental factors.

[0135] The functions of each module in each device in the embodiments of the present application can be found in the corresponding description in the above method, and will not be repeated here.

[0136] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0137] like Fig.12 As shown, it is a block diagram of an electronic device according to a finite element modeling method for calculating electric field strength of a target range according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0138] like Fig.12As shown, the electronic device includes: one or more processors 1201, a memory 1202, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of a graphical user interface (GUI) on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.12 A processor 1201 is taken as an example.

[0139] The memory 1202 is a non-transient computer-readable storage medium provided in the present application. The memory stores instructions executable by at least one processor to enable at least one processor to execute a finite element modeling method for calculating electric field strength in a target range provided in the present application. The non-transient computer-readable storage medium of the present application stores computer instructions, which are used to enable a computer to execute a finite element modeling method for calculating electric field strength in a target range provided in the present application.

[0140] The memory 1202, as a non-transient computer-readable storage medium, can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to a finite element modeling method for calculating electric field strength in a targeted target range in an embodiment of the present application. The processor 1201 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1202, that is, implements a finite element modeling method for calculating electric field strength in a targeted target range in the above method embodiment.

[0141] The memory 1202 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of an electronic device according to a finite element modeling method for calculating electric field strength in a targeted target range, etc. In addition, the memory 1202 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1202 may optionally include a memory remotely arranged relative to the processor 1201, and these remote memories may be connected to the above-mentioned electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0142] The electronic device may further include: an input device 1203 and an output device 1204. The processor 1201, the memory 1202, the input device 1203 and the output device 1204 may be connected via a bus or other means. Fig.12 The example of connecting through bus is taken in the following.

[0143] The input device 1203 can receive input digital or character information, and generate key signal input related to the user settings and function control of the above electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1204 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0144] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0145] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or means (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0146] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0147] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

[0148] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0149] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0150] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A finite element modeling method for calculating electric field strength in a targeted target range, characterized in that: include: Acquiring imaging information of a targeted target range, wherein the targeted target range includes the targeted target; Constructing a three-dimensional space matrix of the targeted target range according to the imaging information of the targeted target range; Extracting boundary layer information of the target target range from the imaging information, wherein the boundary layer information includes three-dimensional coordinate values ​​of a plurality of boundary points, and constructing a geometric shell model of the target target range using the boundary layer information by finite element software; Determining the distribution positions of the electrode sheets, and constructing a geometric shell model with the electrode sheets according to the distribution positions; Modifying the imaging information according to the physical and electrical parameters of the target range, loading the imaging information including the physical and electrical parameters into the geometric shell model with the electrode sheet, and meshing the loaded geometric shell model; The boundary conditions are set according to the preset voltage of the electrode sheet, and the finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet, and the physical and electrical parameters to solve and obtain the electric field strength in the targeted target range.

2. The method according to claim 1, characterized in that Also includes: Segmenting the target target range according to the imaging information of the target target range; A corresponding calibration code is assigned to each tissue component, and a three-dimensional space matrix of the targeted target range is constructed according to each calibration code.

3. The method according to claim 2, characterized in that Extracting boundary layer information of the target target range from the imaging information, and using the boundary layer information to construct a geometric shell model of the target target range using finite element software, including: In the three-dimensional space matrix, extracting boundary layer information of the targeted target range, the boundary layer information including three-dimensional coordinate values ​​of a plurality of boundary points; The three-dimensional coordinate values ​​of the plurality of boundary points are stored in a first text file, and the finite element software uses the first text file to perform boundary fitting to obtain a geometric shell model of the targeted target range.

4. The method according to claim 3, characterized in that Determining the distribution position of the electrode sheet and constructing a geometric shell model with the electrode sheet according to the distribution position, including: According to the relative position relationship between the geometric shell model and the target object, the distribution position of the electrode sheet is calculated using a neural network algorithm of error back propagation; Storing the three-dimensional coordinate values ​​corresponding to the distribution positions of the electrode sheets into a second text file; In the finite element software, the second text file is loaded into the geometric shell model to obtain the geometric shell model with the electrode sheet.

5. The method according to claim 2, characterized in that: Modifying the imaging information according to the physical and electrical parameters of the targeted target range includes: Calculating and processing the three-dimensional space matrix of the targeted target range, and replacing the calibration code corresponding to each tissue component with the corresponding relative dielectric constant; The calibration code corresponding to each tissue component is replaced with the corresponding conductivity, and the physical and electrical parameters include the relative dielectric constant and the conductivity.

6. The method according to claim 5, characterized in that Loading the image information containing the physical and electrical parameters into the geometric shell model with the electrode sheet includes: storing the three-dimensional space matrix of the relative dielectric constant and the conductivity into a third text file; In the finite element software, the third text file is loaded into the geometric shell model with the electrode sheet.

7. The method according to claim 5, characterized in that Also includes: The relative dielectric constant and the electrical conductivity are set according to the material of the electrode sheet and various environmental factors.

8. A finite element modeling device for calculating electric field strength in a targeted target range, characterized in that: include: An imaging information acquisition module, used to acquire imaging information of a targeted target range, wherein the targeted target range includes the targeted target; A geometric shell model construction module is used to extract boundary layer information of the target target range from the imaging information, and the finite element software uses the boundary layer information to construct a geometric shell model of the target target range; A model building module with electrode sheets, used to determine the distribution positions of the electrode sheets and to build a geometric shell model with the electrode sheets according to the distribution positions; A meshing module, used for modifying the imaging information according to the physical and electrical parameters of the target range, loading the imaging information including the physical and electrical parameters into the geometric shell model with the electrode sheet, and meshing the loaded geometric shell model; The electric field strength calculation module is used to set boundary conditions according to the preset voltage of the electrode sheet. The finite element software uses the boundary conditions, the meshed geometric shell model with the electrode sheet and the physical and electrical parameters to solve the electric field strength of the targeted target range.

9. The device according to claim 8, characterized in that Also includes: A tissue component segmentation module, used to perform tissue component segmentation on the target target range according to the imaging information of the target target range; The spatial matrix construction module is used to assign a corresponding calibration code to each tissue component, and to construct a three-dimensional spatial matrix of the targeted target range according to each calibration code.

10. The device according to claim 9, characterized in that The geometric shell model building module comprises: A boundary layer information extraction submodule, used to extract boundary layer information of the targeted target range in the three-dimensional space matrix, wherein the boundary layer information includes three-dimensional coordinate values ​​of a plurality of boundary points; The geometric shell model construction submodule is used to store the three-dimensional coordinate values ​​of the multiple boundary points in a first text file, and the finite element software uses the first text file to perform boundary fitting to obtain the geometric shell model of the targeted target range.

11. The device according to claim 10, characterized in that The model building module with the electrode sheet comprises: An electrode sheet position determination submodule, for calculating the distribution position of the electrode sheet using a neural network algorithm of error back propagation based on the relative position relationship between the geometric shell model and the target object; The model building submodule with electrode sheets is used to store the three-dimensional coordinate values ​​corresponding to the distribution positions of the electrode sheets into a second text file; in the finite element software, the second text file is loaded into the geometric shell model to obtain the geometric shell model with electrode sheets.

12. The device according to claim 9, characterized in that The grid division module comprises: A relative dielectric constant modification module is used to calculate and process the three-dimensional space matrix of the target target range, and replace the calibration code corresponding to each tissue component with the corresponding relative dielectric constant; The conductivity modification module is used to replace the calibration code corresponding to each tissue component with the corresponding conductivity, and the physical and electrical parameters include the relative dielectric constant and the conductivity.

13. The device according to claim 12, characterized in that The grid division module comprises: The text loading submodule is used to store the three-dimensional space matrix of the relative dielectric constant and the conductivity into a third text file; in the finite element software, the third text file is loaded into the geometric shell model with the electrode sheet.

14. The device according to claim 12, characterized in that Also includes: The electrode sheet parameter setting module is used to set the relative dielectric constant and the conductivity according to the material of the electrode sheet and various environmental factors.

15. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 7.

16. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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