CN113096765A - Finite element modeling method and device for calculating electric field intensity of target range - Google Patents
Finite element modeling method and device for calculating electric field intensity of target range Download PDFInfo
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Abstract
The application discloses a finite element modeling method and a finite element modeling device for calculating electric field intensity of a target range. The scheme is as follows: acquiring the imaging information of a target range, wherein the target range comprises a target; extracting boundary layer information of a target range from the imaging information, and constructing a geometric shell model of the target range by finite element software by using the boundary layer information; determining the distribution positions of the electrode plates, and constructing a geometric shell model with the electrode plates according to the distribution positions; modifying the imaging information according to the bioelectricity parameters of the target range, loading the imaging information containing the bioelectricity parameters to a geometric shell model with an electrode slice, and performing mesh division on the loaded geometric shell model; and setting boundary conditions according to the preset voltage of the electrode plate, and solving by using the boundary conditions, the geometric shell model with the electrode plate and the bioelectricity parameters, which are divided into grids, by the finite element software to obtain the electric field strength of the target range. The modeling process is simplified, and the modeling efficiency is improved.
Description
Technical Field
The application relates to the field of computer simulation, in particular to a finite element modeling method and a finite element modeling device for calculating electric field intensity in a target range.
Background
Currently, a new method of noninvasive in vivo tumor therapy is to use an electric field with a certain frequency and intensity to treat solid tumors. The specific implementation mode is that two groups of electrode plates are fixed on superficial skin corresponding to a lesion tissue area, voltage with certain frequency is applied on the electrode plates, so that an electric field with certain distribution is formed in the lesion tissue area of a human body, and the treatment of specific tumors is realized by changing the voltage peak value, the frequency and the positions of the electrode plates. In the process, an electric field distribution form in the human body pathological tissue needs to be acquired, and the existing method mainly obtains the electric field distribution of the human body tissue (human body pathological tissue region) by carrying out simulation calculation through finite element modeling. Taking a brain tumor as an example, the human head structure mainly comprises a scalp, a skull, cerebrospinal fluid, white matter and gray matter, the geometric solid construction is carried out on the tissues in each individual in sequence, and finally a complete finite element model based on diseased tissues is combined.
However, the existing modeling method has many disadvantages, because the brain tissue itself has a complex structure, for example, white matter and gray matter have sulcus and refolding surfaces, resulting in many micro curved surfaces, so there is a large error in the reduction of the corresponding microstructure when building the corresponding geometric model, and the error will be further enlarged if the mesh is not fine enough in the finite element mesh division, so the method has the disadvantages of low precision, complex modeling process, and high performance requirement on the computer.
Disclosure of Invention
The embodiment of the application provides a finite element modeling method and a finite element modeling device for calculating electric field intensity in a target range, which are used for solving the problems in the related art, and the technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a finite element modeling method for calculating an electric field strength of a target range, including:
acquiring the imaging information of a target range, wherein the target range comprises a target;
extracting boundary layer information of the target range from the imaging information, and constructing a geometric shell model of the target range by finite element software by using the boundary layer information;
determining the distribution positions of the electrode plates, and constructing a geometric shell model with the electrode plates according to the distribution positions;
modifying the imaging information according to the physical and electrical parameters of a target range, loading the imaging information containing the physical and electrical parameters to the geometric shell model with the electrode slice, and meshing the loaded geometric shell model;
and setting boundary conditions according to the preset voltage of the electrode plate, and solving by the finite element software by using the boundary conditions, the geometric shell model with the electrode plate and divided grids and the bioelectricity parameters to obtain the electric field strength of the target range.
In one embodiment, the method further comprises:
according to the imaging information of the target range, carrying out tissue component segmentation on the target range;
and giving a calibration code corresponding to each tissue component, and constructing a three-dimensional space matrix of the target range according to each calibration code.
In one embodiment, extracting boundary layer information of the target range from the imaging information, and using the boundary layer information, the finite element software constructs a geometric shell model of the target range, including:
extracting boundary layer information of the target range in the three-dimensional space matrix, wherein the boundary layer information comprises three-dimensional coordinate values of a plurality of boundary points;
and storing the three-dimensional coordinate values of the boundary points into a first text file, and carrying out boundary fitting by the finite element software by utilizing the first text file to obtain a geometric shell model of the target range.
In one embodiment, determining the distribution positions of the electrode slices and constructing a geometric shell model with the electrode slices according to the distribution positions comprises:
calculating the distribution position of the electrode slice by utilizing a neural network algorithm of error back propagation aiming at the relative position relation between the geometric shell model and the target;
storing the three-dimensional coordinate values corresponding to the distribution positions of the electrode slices into a second text file;
and in the finite element software, loading the second text file to the geometric shell model to obtain the geometric shell model with the electrode slice.
In one embodiment, modifying the imaging information according to a target range of bioelectrical parameters comprises:
calculating the three-dimensional space matrix of the target range, and replacing the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;
replacing the calibration code corresponding to each of the tissue elements with a corresponding conductivity, the bioelectrical parameter comprising the relative permittivity and the conductivity.
In one embodiment, loading image information including the bioelectrical parameter to the geometric shell model with electrode patch 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 to the geometric shell model with the electrode slice.
In one embodiment, the method further comprises:
the relative dielectric constant and the conductivity are set according to the material of the electrode sheet and various environmental factors.
In a second aspect, a finite element modeling apparatus for targeted target range electric field strength calculation, comprises:
the imaging information acquisition module is used for acquiring imaging information of a target range, and the target range comprises a target;
the geometric shell model building module is used for extracting boundary layer information of the target range from the imaging information, and finite element software builds a geometric shell model of the target range by using the boundary layer information;
the model building module with the electrode slice is used for determining the distribution position of the electrode slice and building a geometric shell model with the electrode slice according to the distribution position;
the mesh division module is used for 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 to the geometric shell model with the electrode slice, and carrying out mesh division on the loaded geometric shell model;
and the electric field intensity calculation module is used for setting boundary conditions according to the preset voltage of the electrode plate, and the finite element software is used for solving and obtaining the electric field intensity of the target range by utilizing the boundary conditions, the geometric shell model with the electrode plate and divided grids and the bioelectricity parameters.
In one embodiment, the method further comprises:
the tissue component segmentation module is used for carrying out tissue component segmentation on the target range according to the imaging information of the target range;
and the space matrix construction module is used for endowing the tissue components with corresponding calibration codes and constructing the three-dimensional space matrix of the target range according to the calibration codes.
In one embodiment, the geometric shell model building module comprises:
the boundary layer information extraction submodule is used for extracting boundary layer information of the target range in the three-dimensional space matrix, and the boundary layer information comprises three-dimensional coordinate values of a plurality of boundary points;
and the geometric shell model construction submodule is used for storing the three-dimensional coordinate values of the boundary points into a first text file, and the finite element software carries out boundary fitting by utilizing the first text file to obtain the geometric shell model of the target range.
In one embodiment, the model building module with electrode slices includes:
the electrode plate position determining submodule is used for calculating the distribution position of the electrode plate by utilizing a neural network algorithm of error back propagation according to the relative position relation between the geometric shell model and the target;
the model building submodule with the electrode plates is used for storing the three-dimensional coordinate values corresponding to the distribution positions of the electrode plates into a second text file; and in the finite element software, loading the second text file to the geometric shell model to obtain the geometric shell model with the electrode slice.
In one embodiment, the meshing module includes:
the relative dielectric constant modification module is used for calculating the three-dimensional space matrix of the target range and replacing the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;
and the conductivity modification module is used for replacing the calibration code corresponding to each tissue component with the corresponding conductivity, and the bioelectrical parameters comprise the relative dielectric constant and the conductivity.
In one embodiment, the meshing module includes:
the text loading submodule is used for 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 to the geometric shell model with the electrode slice.
In one embodiment, the method further comprises:
and the electrode plate parameter setting module is used for setting the relative dielectric constant and the conductivity according to the material of the electrode plate and various environmental factors.
In a third aspect, an electronic device is provided, including:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the methods described above.
In a fourth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any of the above.
One embodiment in the above application has the following advantages or benefits: the boundary layer information of the imaging information of the target range is extracted, digital materialization is carried out, the boundary layer information is converted into a file format, data points are connected into lines, the lines are connected into planes, a geometric shell model of the target range is built in finite element software, and then the geometric shell model with the electrode plates is built according to the distribution positions of the electrode plates. And then, loading the imaging information after the modification of the physical and electrical parameters to a geometric shell model with an electrode slice, and meshing the loaded geometric shell model. And finally, setting boundary conditions according to the preset voltage of the electrode plate, and solving by the finite element software by using the boundary conditions, the grid-divided geometric shell model with the electrode plate and the bioelectricity parameters to obtain the electric field strength 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 method solves the problems of low calculation precision, high calculation time cost and high requirement on computer performance in the existing geometric modeling method.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram of a finite element modeling method for targeted target range electric field strength calculation according to an embodiment of the present application;
FIG. 2 is an enhanced magnetic resonance image of a patient's skull according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a three-dimensional space matrix based on a calibration code number representation of a magnetic resonance image according to an embodiment of the present application;
FIG. 4 is a schematic view of a geometric shell model constructed of the outermost layer of a patient's skull, according to an embodiment of the present application;
FIG. 5 is a schematic view of a geometric shell model with electrode pads according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a three-dimensional spatial matrix based on a relative permittivity representation of a magnetic resonance image according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a three-dimensional space matrix based on a conductivity representation of a magnetic resonance image according to another embodiment of the present application;
FIG. 8 is a sectional cloud view of a patient's brain tissue potential distribution, according to an embodiment of the present application;
FIG. 9 is a sectional cloud of the distribution of the electric field intensity of the brain tissue of a patient according to an embodiment of the present application;
FIG. 10 is a cloud of two-dimensional slices of brain tissue near a tumor in a patient according to another embodiment of the present application;
FIG. 11 is a schematic diagram of a finite element modeling apparatus for targeted target range electric field strength calculation according to another embodiment of the present application;
FIG. 12 is a block diagram of an electronic device for implementing a finite element modeling method for targeted target range electric field strength calculation according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In one embodiment, as shown in FIG. 1, a finite element modeling method for targeted target range electric field strength calculation is provided, comprising the steps of:
step S110: acquiring the imaging information of a target range, wherein the target range comprises a target;
step S120: extracting boundary layer information of a target range from the imaging information, and constructing a geometric shell model of the target range by finite element software by using the boundary layer information;
step S130: determining the distribution positions of the electrode plates, and constructing a geometric shell model with the electrode plates according to the distribution positions;
step S140: modifying the imaging information according to the bioelectricity parameters of the target range, loading the imaging information containing the bioelectricity parameters to a geometric shell model with an electrode slice, and performing mesh division on the loaded geometric shell model;
step S150: and setting boundary conditions according to the preset voltage of the electrode plate, and solving by using the boundary conditions, the geometric shell model with the electrode plate and the bioelectricity parameters, which are divided into grids, by the finite element software to obtain the electric field strength of the target range.
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 a diseased region of tissue in a human body or an animal body, such as a head, a gastrointestinal part, a liver, a lung, a pancreas, an ovary and the like with a diseased region. The target range includes a target, i.e., a lesion position, for example, a tumor in a target range of the head, the gastrointestinal portion, the liver, the lung, the pancreas, the ovary, and the like, as a target. Finite element modeling is mainly carried out by using finite element software, and electric field intensity distribution or electric potential distribution in a target range is obtained through simulation calculation and solution. Of course, the definition of the target range and the target is not limited to the foregoing description, and may be adapted according to the actual situation, and all are within the protection scope of the present embodiment. The description of this example is made with respect to targeting a target range for tissue in a human or animal body. The imaging information of the diseased part of the tissue in the human body and the animal body can be obtained by adopting an imaging technology, such as nuclear magnetic resonance technology of various sequences, PET-CT/MRI, CT and the like, so as to determine the diseased region and the position of the lesion. For example, one of the magnetic resonance images is shown in fig. 2, in which a head-enhanced magnetic resonance is performed on a patient with a certain brain tumor to obtain a magnetic resonance image of the head of the patient. Of course, the manner of acquiring the imaging information may also be different for different types of target ranges.
When boundary layer information, namely an iconography boundary, is extracted, the iconography information can be stored into a three-dimensional space matrix, and each image pixel point corresponds to a three-dimensional coordinate value. And extracting boundary layer information is extracting the three-dimensional coordinate value of the outermost image pixel point in the target range. And storing the three-dimensional coordinate values of the outermost image pixel points into a text file, and then importing the three-dimensional coordinate values into finite element software for boundary fitting to obtain a geometric shell model of the target range. According to the relative position relation between the geometric shell model of the target range and the target, the number and the optimal distribution position of the electrode slices can be determined by utilizing a neural network algorithm of error back propagation. And extracting the three-dimensional coordinate values of the electrode slice in the three-dimensional space matrix, storing the three-dimensional coordinate values of the electrode slice in a text file, importing the three-dimensional coordinate values of the electrode slice into a geometric shell model, establishing the geometric shell model with the electrode slice, and establishing the geometric shell model with the electrode slice.
Different internal tissues can be distinguished from the imaging information, for example, brain tissue comprising a plurality of different component tissues. The bioelectrical parameters are objectively present, for example, the relative node constants and conductivities of brain tissue. And modifying the imaging information according to the bioelectrical parameters of the target range. In the three-dimensional space matrix of the target range, all three-dimensional coordinate values can be stored in a text and imported into finite element software, modified iconography information is further loaded into a geometric shell model with an electrode slice, and the loaded geometric shell model is subjected to grid division. Boundary conditions are set according to preset voltages of the electrode slices, for example, the peak value and the frequency of the applied alternating voltage are defined in the electrode slice group on the geometric shell model, and simulation initial values are set. And the finite element software utilizes the boundary conditions, the geometric shell model with the electrode plates and the physical and electrical parameters, which are divided into grids, to solve and obtain the electric field strength of the target range, and finally extracts and draws the finite element calculation result.
In finite element software, rather than directly importing the imaging information of a lesion area to further construct a model of the lesion area, the information of the outermost layer of the lesion area is extracted to construct a geometric shell model, and meanwhile, a geometric model of a medical instrument arranged in the lesion area is established to obtain the geometric shell model with an electrode plate. The method is not restricted by finite elements, and can be used for the conditions of complex appearance and complex interior, such as human brain electric field simulation, thereby simplifying the model construction process and improving the working efficiency.
In the embodiment, boundary layer information of the imaging information of the target range is extracted, digital materialization is carried out, the boundary layer information is converted into a file format, data points are connected into lines, the lines are connected into planes, a geometric shell model of the target range is built in finite element software, and then a geometric shell model with electrode plates is built according to the distribution positions of the electrode plates. And then, loading the imaging information after the modification of the physical and electrical parameters to a geometric shell model with an electrode slice, and meshing the loaded geometric shell model. And finally, setting boundary conditions according to the preset voltage of the electrode plate, and solving by the finite element software by using the boundary conditions, the grid-divided geometric shell model with the electrode plate and the bioelectricity parameters to obtain the electric field strength 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 method solves the problems of low calculation precision, high calculation time cost and high requirement on computer performance in the existing geometric modeling method.
In one embodiment, after step S110, the method further includes:
step S111: according to the imaging information of the target range, carrying out tissue component segmentation on the target range;
step S112: and giving a calibration code corresponding to each tissue component, and constructing a three-dimensional space matrix of a target range according to each calibration code.
In one example, different tissues in a lesion region (target range) are distinguished according to a magnetic resonance image, stored in a three-dimensional space matrix in the form of numeric characters, and different tissues and tumors (target) are marked with different characters. For example, based on an enhanced magnetic resonance image of the skull, different tissues in the skull are distinguished, and the reference numeral 0 is used to represent an outside air part, the reference numeral 1 is used to represent a scalp part, the reference numeral 2 is used to represent a skull part, the reference numeral 3 is used to represent a spinal fluid part, the reference numeral 4 is used to represent a gray matter part, the reference numeral 5 is used to represent a white matter part, and the reference numeral 6 is used to represent a tumor part, as shown in fig. 3.
In one embodiment, step S120 includes:
step S121: extracting boundary layer information of a target range in a three-dimensional space matrix, wherein the boundary layer information comprises three-dimensional coordinate values of a plurality of boundary points;
step S122: and storing the three-dimensional coordinate values of the boundary points into a first text file, and performing boundary fitting on the first text file by using finite element software to obtain a geometric shell model of the target range.
In one example, the target range is a three-dimensional spatial matrix of the brain, and boundary layer information of the air domain and the skull tissue region may be extracted, including three-dimensional coordinate values of a plurality of boundary points, i.e., coordinate values of the outermost layer data of the scalp. The extracted coordinate values are stored as binary documents, and the documents are imported into COMSOL finite element software for boundary fitting, so that a geometric shell model of the brain can be obtained, wherein the model does not contain the geometric structure of the brain, as shown in FIG. 4. The outermost layer geometric model required by simulation is established and the internal complex geometry is avoided by directly extracting the spatial position data of the outermost layer of the human tissue image, the whole method is not limited by finite elements, and the method can be used for the conditions of complex appearance and complex internal, such as human brain electric field simulation.
In one embodiment, step S130 includes:
step S131: calculating the distribution position of the electrode slice by utilizing a neural network algorithm of error back propagation aiming at the relative position relation between the geometric shell model and the target;
step S132: storing the three-dimensional coordinate values corresponding to the distribution positions of the electrode plates into a second text file;
step S133: and in the finite element software, loading the second text file to the geometric shell model to obtain the geometric shell model with the electrode slice.
In one example, the optimal position of the electrode slice distribution is calculated by a BP Error Back-propagation (BP) neural network. The electrode plates are distributed in a pair respectively at the front, the back, the left and the right, each pair of electrode plates consists of 18 small electrodes, and a geometric model of the electrode plates is added on the established skull-shaped shell model to obtain the geometric shell model with the electrode plates, as shown in figure 5.
In one embodiment, step S140 includes:
step S141: calculating a three-dimensional space matrix of a target range, and replacing the corresponding calibration code of each tissue component with the corresponding relative dielectric constant;
step S142: and replacing the corresponding calibration code of each tissue component with the corresponding conductivity, wherein the bioelectrical parameters comprise the relative dielectric constant and the conductivity.
In one example, the computer program utilizes programs such as C language, Python language, Matlab, etc. to perform calculation processing on each tissue component in the three-dimensional space matrix obtained in the previous step, replace the calibration code corresponding to each tissue component with a corresponding relative permittivity, and replace the calibration code corresponding to each tissue component with a corresponding conductivity, so as to obtain a replaced three-dimensional space matrix.
For example, different tissues and tumors have been substituted with the corresponding calibration symbols, replacing all calibration symbols one by one with the corresponding relative permittivity, and replacing all calibration symbols one by one with the corresponding conductivity. The calibration code of each brain tissue is replaced by the corresponding relative dielectric constant one by one according to the following table, the three-dimensional space matrix obtained after the replacement is shown in fig. 6, and the three-dimensional space matrix after the replacement is stored as a text document so as to be convenient for calling finite element software.
The calibration code of each tissue component is replaced one by the corresponding conductivity according to the following table, the three-dimensional space matrix obtained after replacement is shown in fig. 7, and the data after replacement is saved as a text document for finite element calling.
In the embodiment, the three-dimensional space matrix bearing the imaging information of the lesion area is simplified into the three-dimensional space matrix of the relative dielectric constant and the conductivity, and the script file is established and imported into the finite element software for modeling, so that the situation that a geometric model with a complex structure is directly constructed by using the imaging information is avoided, the model construction process is effectively simplified, and the model construction efficiency is improved.
In one embodiment, step S140 further includes:
step S143: storing the three-dimensional space matrix of the relative dielectric constant and the conductivity into a third text file;
step S144: in the finite element software, loading a third text file to a geometric shell model with an electrode slice;
step S145: and carrying out mesh division on the loaded geometric shell model.
In one example, a text document derived from a three-dimensional spatial matrix of relative permittivity and conductivity is imported into finite element software and loaded onto a skull-shaped shell model with electrode patches. And (4) carrying out grid division on the skull shell model with the electrode slice. Because of geometric solid elements that are not available in the cranium, the entire model may uniformly use extremely fine tetrahedral meshes. The grid division does not need to be consistent with the pixels, the grid type, the grid size, the density distribution and the like can be selected at will, and an obvious operation space is provided for optimization calculation.
The peak 59V of the applied alternating voltage and the frequency 200kHz were defined in the electrode pad set on the skull shell model and the simulation initial value was set to 0. And solving a current conservation equation in finite element software through ohm's law to finally obtain the potential distribution and the electric field intensity distribution in the whole brain tissue. Extracting finite element calculation results, drawing a cloud picture of a brain tissue potential distribution section as shown in fig. 8, drawing a cloud picture of a brain tissue electric field intensity distribution section as shown in fig. 9, and drawing a cloud picture of a two-dimensional slice of brain tissue near a tumor as shown in fig. 10.
In one embodiment, the method further comprises:
the relative dielectric constant and the conductivity are set according to the material of the electrode sheet and various environmental factors.
In one example, depending on the material of the electrode sheet, the environmental factors may include temperature, humidity, and other environmental factors, such as the relative dielectric constant and the conductivity, which are within the protection scope of the present embodiment.
In another embodiment, as shown in FIG. 11, there is provided a finite element modeling apparatus for electric field strength calculation of a target range, comprising:
an imaging information acquisition module 110, configured to acquire imaging information of a target range, where the target range includes a target;
a geometric shell model building module 120, configured to extract boundary layer information of the target range from the imaging information, where finite element software uses the boundary layer information to build a geometric shell model of the target range;
the model building module with electrode slices 130 is used for determining the distribution positions of the electrode slices and building a geometric shell model with the electrode slices according to the distribution positions;
the mesh division module 140 is configured to modify the imaging information according to the bioelectrical parameter of the target range, load the imaging information including the bioelectrical parameter to the geometric shell model with the electrode slice, and perform mesh division on the loaded geometric shell model;
and the electric field intensity calculation module 150 is used for setting boundary conditions according to the preset voltage of the electrode slice, and the finite element software is used for solving and obtaining the electric field intensity of the target range by utilizing the boundary conditions, the geometric shell model with the electrode slice and divided grids and the bioelectricity parameters.
In one embodiment, the method further comprises:
the tissue component segmentation module is used for carrying out tissue component segmentation on the target range according to the imaging information of the target range;
and the space matrix construction module is used for endowing the tissue components with corresponding calibration codes and constructing the three-dimensional space matrix of the target range according to the calibration codes.
In one embodiment, the geometric shell model building module comprises:
the boundary layer information extraction submodule is used for extracting boundary layer information of the target range in the three-dimensional space matrix, and the boundary layer information comprises three-dimensional coordinate values of a plurality of boundary points;
and the geometric shell model construction submodule is used for storing the three-dimensional coordinate values of the boundary points into a first text file, and the finite element software carries out boundary fitting by utilizing the first text file to obtain the geometric shell model of the target range.
In one embodiment, the model building module with electrode slices includes:
the electrode plate position determining submodule is used for calculating the distribution position of the electrode plate by utilizing a neural network algorithm of error back propagation according to the relative position relation between the geometric shell model and the target;
the model building submodule with the electrode plates is used for storing the three-dimensional coordinate values corresponding to the distribution positions of the electrode plates into a second text file; and in the finite element software, loading the second text file to the geometric shell model to obtain the geometric shell model with the electrode slice.
In one embodiment, the meshing module includes:
the relative dielectric constant modification module is used for calculating the three-dimensional space matrix of the target range and replacing the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;
and the conductivity modification module is used for replacing the calibration code corresponding to each tissue component with the corresponding conductivity, and the bioelectrical parameters comprise the relative dielectric constant and the conductivity.
In one embodiment, the meshing module includes:
the text loading submodule is used for 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 to the geometric shell model with the electrode slice.
In one embodiment, the method further comprises:
and the electrode plate parameter setting module is used for setting the relative dielectric constant and the conductivity according to the material of the electrode plate and various environmental factors.
The functions of each module in each apparatus in the embodiment of the present application may refer to corresponding descriptions in the above method, and are not described herein again.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
FIG. 12 is a block diagram of an electronic device of a finite element modeling method for targeted target range electric field strength calculation according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may 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 meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 12, the electronic apparatus includes: one or more processors 1201, memory 1202, and interfaces for connecting the various components, including a high speed interface and a low speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display Graphical information for a Graphical User Interface (GUI) on an external input/output device, such as a display device coupled to the Interface. In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 12 illustrates an example of one processor 1201.
The memory 1202 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of an electronic device of a finite element modeling method for targeted target range electric field strength calculation, and the like. Further, the memory 1202 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 1202 may optionally include memory located remotely from the processor 1201, which may be coupled to the electronic devices via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
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 by a bus or other means, and the bus connection is exemplified in fig. 12.
The input device 1203 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic apparatus as described above, for example, a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 1204 may include a display device, auxiliary lighting devices (e.g., LEDs), tactile feedback devices (e.g., vibrating motors), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD) such as a Cr12sta display 12, a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, Integrated circuitry, Application Specific Integrated Circuits (ASICs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may 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, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here 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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, 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 may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, 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.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (16)
1. A finite element modeling method for targeted target range electric field strength calculation, comprising:
acquiring the imaging information of a target range, wherein the target range comprises a target;
extracting boundary layer information of the target range from the imaging information, and constructing a geometric shell model of the target range by finite element software by using the boundary layer information;
determining the distribution positions of the electrode plates, and constructing a geometric shell model with the electrode plates according to the distribution positions;
modifying the imaging information according to the physical and electrical parameters of a target range, loading the imaging information containing the physical and electrical parameters to the geometric shell model with the electrode slice, and meshing the loaded geometric shell model;
and setting boundary conditions according to the preset voltage of the electrode plate, and solving by the finite element software by using the boundary conditions, the geometric shell model with the electrode plate and divided grids and the bioelectricity parameters to obtain the electric field strength of the target range.
2. The method of claim 1, further comprising:
according to the imaging information of the target range, carrying out tissue component segmentation on the target range;
and giving a calibration code corresponding to each tissue component, and constructing a three-dimensional space matrix of the target range according to each calibration code.
3. The method of claim 2, wherein boundary layer information of the target object range is extracted from the imaging information, and finite element software constructs a geometric shell model of the target object range using the boundary layer information, comprising:
extracting boundary layer information of the target range in the three-dimensional space matrix, wherein the boundary layer information comprises three-dimensional coordinate values of a plurality of boundary points;
and storing the three-dimensional coordinate values of the boundary points into a first text file, and carrying out boundary fitting by the finite element software by utilizing the first text file to obtain a geometric shell model of the target range.
4. The method of claim 3, wherein determining distribution positions of the electrode slices and constructing a geometric shell model with the electrode slices according to the distribution positions comprises:
calculating the distribution position of the electrode slice by utilizing a neural network algorithm of error back propagation aiming at the relative position relation between the geometric shell model and the target;
storing the three-dimensional coordinate values corresponding to the distribution positions of the electrode slices into a second text file;
and in the finite element software, loading the second text file to the geometric shell model to obtain the geometric shell model with the electrode slice.
5. The method of claim 2, wherein modifying the imaging information according to the bioelectrical parameter of the target area comprises:
calculating the three-dimensional space matrix of the target range, and replacing the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;
replacing the calibration code corresponding to each of the tissue elements with a corresponding conductivity, the bioelectrical parameter comprising the relative permittivity and the conductivity.
6. The method of claim 5, wherein loading image information containing the bioelectrical parameters into the geometric shell model with electrode patches comprises:
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 to the geometric shell model with the electrode slice.
7. The method of claim 5, further comprising:
the relative dielectric constant and the conductivity are set according to the material of the electrode sheet and various environmental factors.
8. A finite element modeling apparatus for targeted target range electric field strength calculation, comprising:
the imaging information acquisition module is used for acquiring imaging information of a target range, and the target range comprises a target;
the geometric shell model building module is used for extracting boundary layer information of the target range from the imaging information, and finite element software builds a geometric shell model of the target range by using the boundary layer information;
the model building module with the electrode slice is used for determining the distribution position of the electrode slice and building a geometric shell model with the electrode slice according to the distribution position;
the mesh division module is used for 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 to the geometric shell model with the electrode slice, and carrying out mesh division on the loaded geometric shell model;
and the electric field intensity calculation module is used for setting boundary conditions according to the preset voltage of the electrode plate, and the finite element software is used for solving and obtaining the electric field intensity of the target range by utilizing the boundary conditions, the geometric shell model with the electrode plate and divided grids and the bioelectricity parameters.
9. The apparatus of claim 8, further comprising:
the tissue component segmentation module is used for carrying out tissue component segmentation on the target range according to the imaging information of the target range;
and the space matrix construction module is used for endowing the tissue components with corresponding calibration codes and constructing the three-dimensional space matrix of the target range according to the calibration codes.
10. The apparatus of claim 9, wherein the geometric shell model building module comprises:
the boundary layer information extraction submodule is used for extracting boundary layer information of the target range in the three-dimensional space matrix, and the boundary layer information comprises three-dimensional coordinate values of a plurality of boundary points;
and the geometric shell model construction submodule is used for storing the three-dimensional coordinate values of the boundary points into a first text file, and the finite element software carries out boundary fitting by utilizing the first text file to obtain the geometric shell model of the target range.
11. The apparatus of claim 10, wherein the model building module with electrode tiles comprises:
the electrode plate position determining submodule is used for calculating the distribution position of the electrode plate by utilizing a neural network algorithm of error back propagation according to the relative position relation between the geometric shell model and the target;
the model building submodule with the electrode plates is used for storing the three-dimensional coordinate values corresponding to the distribution positions of the electrode plates into a second text file; and in the finite element software, loading the second text file to the geometric shell model to obtain the geometric shell model with the electrode slice.
12. The apparatus of claim 9, wherein the meshing module comprises:
the relative dielectric constant modification module is used for calculating the three-dimensional space matrix of the target range and replacing the calibration code corresponding to each tissue component with the corresponding relative dielectric constant;
and the conductivity modification module is used for replacing the calibration code corresponding to each tissue component with the corresponding conductivity, and the bioelectrical parameters comprise the relative dielectric constant and the conductivity.
13. The apparatus of claim 12, wherein the meshing module comprises:
the text loading submodule is used for 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 to the geometric shell model with the electrode slice.
14. The apparatus of claim 12, further comprising:
and the electrode plate parameter setting module is used for setting the relative dielectric constant and the conductivity according to the material of the electrode plate and various environmental factors.
15. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116542115A (en) * | 2023-07-06 | 2023-08-04 | 湖南安泰康成生物科技有限公司 | Method and device for determining electric field intensity mode of organism target area and electronic equipment |
| CN117438039A (en) * | 2023-12-19 | 2024-01-23 | 湖南安泰康成生物科技有限公司 | Method and device for determining application distribution of electrode plates |
| CN117789922A (en) * | 2024-02-23 | 2024-03-29 | 湖南安泰康成生物科技有限公司 | Electrode slice application scheme determining method and device, equipment and storage medium |
| CN117954050A (en) * | 2024-03-25 | 2024-04-30 | 湖南安泰康成生物科技有限公司 | Electrode slice application scheme determining method and device, equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106345056A (en) * | 2016-09-21 | 2017-01-25 | 天津大学 | Deep brain stimulation electrode array optimizing system based on machine learning |
| US20170120041A1 (en) * | 2015-10-28 | 2017-05-04 | Novocure Limited | TTField Treatment with Optimization of Electrode Positions on the Head Based on MRI-Based Conductivity Measurements |
| US20190355476A1 (en) * | 2016-12-06 | 2019-11-21 | The Regents Of The University Of California | Optimal multi-electrode transcutaneous stimulation with high focality and intensity |
-
2021
- 2021-04-16 CN CN202110413523.9A patent/CN113096765A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170120041A1 (en) * | 2015-10-28 | 2017-05-04 | Novocure Limited | TTField Treatment with Optimization of Electrode Positions on the Head Based on MRI-Based Conductivity Measurements |
| CN106345056A (en) * | 2016-09-21 | 2017-01-25 | 天津大学 | Deep brain stimulation electrode array optimizing system based on machine learning |
| US20190355476A1 (en) * | 2016-12-06 | 2019-11-21 | The Regents Of The University Of California | Optimal multi-electrode transcutaneous stimulation with high focality and intensity |
Non-Patent Citations (3)
| Title |
|---|
| 孙伟铭;董香丽;: "颅去骨瓣减压术后tDCS电极参数对人脑内部电场影响的有限元仿真", 医疗卫生装备, no. 10, 15 October 2018 (2018-10-15), pages 10 - 13 * |
| 帅万钧;薛丽波;晁勇;: "基于CT图像提取人体组织边界建立的有限元模型", 中国组织工程研究与临床康复, no. 48, 26 November 2009 (2009-11-26), pages 9463 - 9466 * |
| 翟伟兵: "经颅直流电刺激人脑中电场分布的聚焦度研究", 《兰州交通大学硕士学位论文》, 28 April 2017 (2017-04-28), pages 8 - 13 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116542115A (en) * | 2023-07-06 | 2023-08-04 | 湖南安泰康成生物科技有限公司 | Method and device for determining electric field intensity mode of organism target area and electronic equipment |
| CN116542115B (en) * | 2023-07-06 | 2023-10-20 | 湖南安泰康成生物科技有限公司 | Method and device for determining electric field intensity mode of organism target area and electronic equipment |
| CN117438039A (en) * | 2023-12-19 | 2024-01-23 | 湖南安泰康成生物科技有限公司 | Method and device for determining application distribution of electrode plates |
| CN117438039B (en) * | 2023-12-19 | 2024-03-22 | 湖南安泰康成生物科技有限公司 | Method and device for determining application distribution of electrode plates |
| CN117789922A (en) * | 2024-02-23 | 2024-03-29 | 湖南安泰康成生物科技有限公司 | Electrode slice application scheme determining method and device, equipment and storage medium |
| CN117789922B (en) * | 2024-02-23 | 2024-05-17 | 湖南安泰康成生物科技有限公司 | Electrode slice application scheme determining method and device, equipment and storage medium |
| CN117954050A (en) * | 2024-03-25 | 2024-04-30 | 湖南安泰康成生物科技有限公司 | Electrode slice application scheme determining method and device, equipment and storage medium |
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