Coil detection method and device for transcranial magnetic stimulation, electronic equipment and storage medium

Abstract

The invention discloses a coil detection method and device for transcranial magnetic stimulation, electronic equipment and a storage medium. The method comprises the following steps: acquiring brain map data; respectively determining three-dimensional image models corresponding to a plurality of brain components based on the brain atlas data, wherein the brain components comprise a plurality of brain regions, scalp, skull and cerebrospinal fluid; constructing a finite element model based on the plurality of three-dimensional image models; stimulation is applied to a target brain region in the multiple brain regions of the finite element model based on a coil, and induced electric field distribution data corresponding to the multiple brain components are obtained; based on the induced electric field distribution data corresponding to the target brain region and the induced electric field distribution data corresponding to the brain region adjacent to the target brain region, determining stimulation index data corresponding to the coil; and when the stimulation index data does not meet the preset condition, the coil parameters of the coil are adjusted, so that the stimulation index data meet the preset condition, detection of the coil for transcranial magnetic stimulation is realized, and the accuracy of coil detection is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a coil detection method and device for transcranial magnetic stimulation, an electronic device and a storage medium. BACKGROUND

[0002] Transcranial magnetic stimulation (TMS) as a non-invasive neural regulation technology is widely used in the treatment of neurological diseases and brain science research.

[0003] In the prior art, a subject's individual head model is constructed by collecting magnetic resonance imaging data / computed tomography data of the subject, and a coil is used to stimulate a preselected brain region. Due to the differences between the heads of the subjects, the individual head models differ, and thus the focusing degree of the stimulation of the coil is reduced. SUMMARY

[0004] The present application provides a coil detection method and device for transcranial magnetic stimulation, an electronic device and a storage medium, to realize the detection of the stimulation focusing degree of the coil and achieve the effect of improving the accuracy of the coil detection.

[0005] According to an aspect of the present application, a coil detection method for transcranial magnetic stimulation is provided, which comprises:

[0006] Obtaining brain atlas data, the brain atlas data being expected image data constructed based on brain magnetic resonance imaging data of a plurality of first reference objects;

[0007] Determining a plurality of three-dimensional image models corresponding to a plurality of brain components based on the brain atlas data, the brain components including a plurality of brain regions, a scalp, a skull and cerebrospinal fluid;

[0008] Constructing a finite element model based on the plurality of three-dimensional image models, the finite element model including three-dimensional coordinate information corresponding to the plurality of brain components;

[0009] Applying stimulation to a target brain region in the plurality of brain regions of the finite element model based on the coil, to obtain induced electric field distribution data corresponding to the plurality of brain components respectively;

[0010] Determining stimulation index data corresponding to the coil based on the induced electric field distribution data corresponding to the target brain region and the induced electric field distribution data corresponding to the brain region adjacent to the target brain region, the stimulation index data being used to indicate the stimulation focusing degree of the coil;

[0011] In the case where the stimulation index data does not satisfy a preset condition, adjusting the coil parameters of the coil to make the stimulation index data satisfy the preset condition.

[0012] According to another aspect of the present application, there is provided a coil detection device for transcranial magnetic stimulation, the device comprising:

[0013] a brain atlas data obtaining module configured to obtain brain atlas data, the brain atlas data being expected image data constructed based on magnetic resonance imaging data of a plurality of first reference subjects;

[0014] a three-dimensional image model determining module configured to determine, based on the brain atlas data, a three-dimensional image model corresponding to each of a plurality of brain components, the brain components including a plurality of brain regions, a scalp, a skull, and cerebrospinal fluid;

[0015] a finite element model constructing module configured to construct a finite element model based on the plurality of three-dimensional image models, the finite element model including three-dimensional coordinate information corresponding to the plurality of brain components;

[0016] an induced electric field distribution data determining module configured to, based on the coil applying stimulation to a target brain region in the plurality of brain regions of the finite element model, obtain induced electric field distribution data corresponding to each of the plurality of brain components;

[0017] a stimulation index data determining module configured to, based on the induced electric field distribution data corresponding to the target brain region and the induced electric field distribution data corresponding to a brain region adjacent to the target brain region, determine stimulation index data corresponding to the coil, the stimulation index data being used to indicate a stimulation focusing degree of the coil;

[0018] a coil parameter adjusting module configured to, in a case where the stimulation index data does not satisfy a preset condition, adjust a coil parameter of the coil so as to make the stimulation index data satisfy the preset condition.

[0019] According to another aspect of the present application, there is provided an electronic device, the electronic device comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein

[0022] the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a coil detection method for transcranial magnetic stimulation according to any one of the embodiments of the present application.

[0023] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to perform a coil detection method for transcranial magnetic stimulation according to any one of the embodiments of the present application when executed by the processor.

[0024] According to another aspect of the present application, the embodiment of the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements a coil detection method for transcranial magnetic stimulation according to any of the embodiments of the present application.

[0025] The technical scheme of the embodiment of the present application, by acquiring brain atlas data, since the brain atlas data is expected image data constructed based on the brain magnetic resonance imaging data of a plurality of first reference objects, can provide standardized data support for subsequent analysis, and improve the universality of the analysis result; based on the brain atlas data, three-dimensional image models corresponding to a plurality of brain components are determined, which provides accurate spatial coordinates for the detection of the coil, thereby facilitating the improvement of the accuracy of the coil detection; based on the plurality of three-dimensional image models, a finite element model is constructed, the finite element model comprises three-dimensional coordinate information corresponding to the plurality of brain components, the construction of the finite element model is realized, and model support is provided for subsequent analysis and processing; based on the coil, a target brain area in the plurality of brain areas of the finite element model is stimulated, and a plurality of brain component corresponding induced electric field distribution data are obtained, the determination of the induced electric field distribution data is realized, and data support is provided for the detection of the coil; based on the target brain area corresponding induced electric field distribution data and the brain area adjacent to the target brain area corresponding induced electric field distribution data, the stimulation index data corresponding to the coil is determined, the stimulation index data is used to indicate the stimulation focusing degree of the coil, the accurate calculation of the stimulation index data of the coil is realized, and a basis is provided for subsequent adjustment of the coil; in the case that the stimulation index data does not satisfy the preset condition, the coil parameters of the coil are adjusted to make the stimulation index data satisfy the preset condition, the problem of reduced stimulation focusing degree of the coil caused by individual differences in the prior art is solved, and the accurate detection and flexible adjustment of the coil are realized.

[0026] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0028] Figure 1 is a flowchart of a coil detection method for transcranial magnetic stimulation provided by the first embodiment of the present application;

[0029] Figure 2is a structural schematic diagram of a finite element model provided by an embodiment of the present application;

[0030] Figure 3 is a schematic diagram of induced electric field distribution data of the gray matter provided by an embodiment of the present application;

[0031] Figure 4 is a schematic diagram of induced electric field distribution data of the cerebrospinal fluid provided by an embodiment of the present application;

[0032] Figure 5 is a schematic diagram of induced electric field distribution data of the skull provided by an embodiment of the present application;

[0033] Figure 6 is a schematic diagram of induced electric field distribution data of the scalp provided by an embodiment of the present application;

[0034] Figure 7 is a schematic diagram of the principle of magnetic stimulation provided by an embodiment of the present application;

[0035] Figure 8 is a flowchart of a coil detection method for transcranial magnetic stimulation provided by the second embodiment of the present application;

[0036] Figure 9 is a schematic diagram of coil placement provided by an embodiment of the present application;

[0037] Figure 10 is a structural schematic diagram of a coil detection device for transcranial magnetic stimulation provided by the third embodiment of the present application;

[0038] Figure 11 is a structural schematic diagram of an electronic device provided by the fourth embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the protection scope of the present application.

[0040] It should be noted that the terms "first", "second", and the like in the description and claims of the application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0041] Embodiment one

[0042] Figure 1 is a flowchart of a coil detection method for transcranial magnetic stimulation provided by an embodiment of the application. The embodiment can be applied to detect the stimulation focusing degree of a coil for transcranial magnetic stimulation. The method can be executed by a coil detection device for transcranial magnetic stimulation, which can be realized in the form of hardware and / or software. The coil detection device for transcranial magnetic stimulation can be configured in an electronic device provided by an embodiment of the application. The electronic device can be a server, a computer or a mobile terminal, etc. For example, the mobile terminal can be a mobile phone, a tablet computer, etc. As shown in the figure, the method comprises: Figure 1

[0043] S110, acquiring brain atlas data, the brain atlas data being expected image data constructed based on brain magnetic resonance imaging data of a plurality of first reference objects.

[0044] The brain atlas data is image data for fine division and description of brain structure and function. The brain atlas data includes fine division of different regions of the brain and connection information between the regions. The brain atlas data is three-dimensional image data. The first reference object is a subject requiring transcranial magnetic stimulation. The brain magnetic resonance imaging data is image data obtained by scanning the brain of the first reference object by a magnetic resonance scanning device. The brain atlas data can be constructed according to the brain magnetic resonance imaging data of a plurality of first reference objects. For example, the brain magnetic resonance imaging data of a plurality of first reference objects is input into a brain atlas data construction model for processing to obtain the brain atlas data. The brain atlas data can also be obtained from a brain atlas database. The brain atlas database stores a plurality of brain atlas data. The brain atlas data is obtained by matching in the brain atlas database according to requirements. Optionally, the brain atlas data can include the human brain network group atlas published by the Institute of Automation, Chinese Academy of Sciences.

[0045] ​Specifically, the brain magnetic resonance imaging data of the plurality of first reference objects is input into a brain atlas data construction model for processing to obtain brain atlas data, and a universal brain atlas data is constructed to provide standardized data support for subsequent analysis.

[0046] In S120, a three-dimensional image model corresponding to each of the plurality of brain components is determined based on the brain atlas data. The brain components include a plurality of brain regions, a scalp, a skull, and cerebrospinal fluid.

[0047] The brain components are obtained by dividing the brain from an anatomical perspective. The brain components include a plurality of brain regions, a scalp, a skull, and cerebrospinal fluid. The plurality of brain regions include, but are not limited to, frontal lobe, parietal lobe, occipital lobe, temporal lobe, hippocampus, and amygdala. The three-dimensional image model is a three-dimensional digital model reflecting the spatial form, positional relationship, and structural details of the brain components. The three-dimensional image model can be determined based on the brain atlas data. For example, the brain atlas data can be input into a pre-trained three-dimensional image construction model for processing to obtain a three-dimensional image model corresponding to each of the plurality of brain components. The three-dimensional image construction model includes, but is not limited to, a transformer model. Optionally, the three-dimensional image model can include three-dimensional coordinate information corresponding to each of the plurality of brain components.

[0048] Specifically, the brain atlas data is input into the three-dimensional image construction model for processing to obtain a three-dimensional image model corresponding to each of the plurality of brain components, which provides accurate spatial coordinates for the coil detection, thereby facilitating improvement of the accuracy of the coil detection.

[0049] Optionally, the brain components include a plurality of brain regions. The three-dimensional image model corresponding to each of the plurality of brain components is determined based on the brain atlas data, including: performing segmentation processing on the brain atlas data based on a first image segmentation algorithm to obtain a first segmentation image corresponding to each of the plurality of brain regions; and performing image reconstruction on the first segmentation image based on a first image reconstruction algorithm to obtain a three-dimensional image model corresponding to each of the plurality of brain regions.

[0050] The first image segmentation algorithm is an algorithm for dividing the brain atlas data to obtain independent regions corresponding to a plurality of brain regions. The first image segmentation algorithm includes but is not limited to a threshold-based image segmentation algorithm, an edge detection-based image segmentation algorithm, and a morphology-based image segmentation algorithm. The first image segmentation algorithm can be selected according to requirements, and is not limited herein. The first segmented image is image data representing the spatial range corresponding to a plurality of brain regions. The first image reconstruction algorithm is an algorithm for converting a three-dimensional segmentation result into a three-dimensional virtual model through computer graphics technology. The first image reconstruction algorithm includes but is not limited to a Fourier transform-based image reconstruction algorithm, a filter back projection-based image reconstruction algorithm, and a three-dimensional isosurface-based image reconstruction algorithm. The first image reconstruction algorithm can be selected according to requirements, and is not limited herein.

[0051] Specifically, the first image segmentation algorithm is used to segment the brain atlas data to obtain the first segmented image corresponding to a plurality of brain regions. The first image reconstruction algorithm is used to reconstruct the first segmented image to obtain a three-dimensional image model corresponding to a plurality of brain regions. The precise reconstruction of the three-dimensional image model corresponding to a plurality of brain regions is achieved, accurate spatial coordinates for coil detection are provided, and the accuracy of coil detection is improved.

[0052] Optionally, the brain components include a scalp, a skull, and cerebrospinal fluid. Based on the brain atlas data, three-dimensional image models corresponding to a plurality of brain components are determined based on the brain atlas data, including: performing registration processing on the brain atlas data based on a pre-set registration template to obtain registered brain atlas data; the registration template is an expected registration template constructed based on T1-weighted MRI scan images of a plurality of second reference objects; performing segmentation processing on the registered brain atlas data based on a second image segmentation algorithm to obtain second segmented images corresponding to the scalp, the skull, and the cerebrospinal fluid; and performing image reconstruction on the second segmented images based on a second image reconstruction algorithm to obtain three-dimensional image models corresponding to the scalp, the skull, and the cerebrospinal fluid.

[0053] The registration template is a standardized brain space template constructed by averaging and standardizing T1-weighted MRI scans of a plurality of second reference objects. The registration template is used to unify the brain atlas data to the same spatial coordinate system. The registration template can also include a Montreal Neurological Institute (MNI) template and an International Consortium for Brain Mapping (ICBM) template. The second image segmentation algorithm is an algorithm for the registered brain atlas data, which respectively identifies the image features of the scalp, skull and cerebrospinal fluid, and divides the scalp, skull and cerebrospinal fluid from the whole image. The second image segmentation algorithm includes but is not limited to a template-based image segmentation algorithm and a region growing and connectivity-based image segmentation algorithm, and the second image segmentation algorithm can be selected according to the requirements, which is not limited here. The first image segmentation algorithm and the second image segmentation algorithm can be the same or different. The second image reconstruction algorithm is an algorithm for the second segmented image corresponding to the scalp, skull and cerebrospinal fluid, which converts the three-dimensional segmentation result into a three-dimensional virtual model by computer graphics technology. The second image reconstruction algorithm includes but is not limited to a moving tetrahedron algorithm and a maximum density projection algorithm, and the second image reconstruction algorithm can be selected according to the requirements, which is not limited here. The first image reconstruction algorithm and the second image reconstruction algorithm can be the same or different.

[0054] Specifically, the registration template is used to perform registration processing on the brain atlas data corresponding to the scalp, skull and cerebrospinal fluid, respectively, to obtain registered brain atlas data. The second image segmentation algorithm is used to perform segmentation processing on the registered brain atlas data to obtain second segmented images corresponding to the scalp, skull and cerebrospinal fluid, respectively. The second image reconstruction algorithm is used to perform image reconstruction on the second segmented images to obtain three-dimensional image models corresponding to the scalp, skull and cerebrospinal fluid, respectively, thereby realizing accurate reconstruction of the three-dimensional image models corresponding to the scalp, skull and cerebrospinal fluid, respectively, and providing accurate spatial coordinates for coil detection, thereby facilitating improvement of the accuracy of coil detection.

[0055] S130, constructing a finite element model based on a plurality of three-dimensional image models, the finite element model including three-dimensional coordinate information corresponding to a plurality of brain components.

[0056] The finite element model is a model constructed based on a finite element analysis method. For example, the three-dimensional image models corresponding to a plurality of brain regions, scalp, skull and cerebrospinal fluid can be input into a finite element analysis software for processing to obtain a finite element model. The finite element model includes three-dimensional coordinate information corresponding to a plurality of brain regions, scalp, skull and cerebrospinal fluid. The three-dimensional coordinate information is spatial position information corresponding to a plurality of brain components, respectively. For example, seeFigure 2 , Figure 2 is a structural schematic diagram of a finite element model provided by an embodiment of the application.

[0057] Specifically, the three-dimensional image models corresponding to the plurality of brain regions, the scalp, the skull and the cerebrospinal fluid are input into a finite element analysis software for processing to obtain the finite element model, thereby realizing the construction of the finite element model and providing model support for subsequent analysis and processing.

[0058] In S140, the coil is used to stimulate a target brain region in the plurality of brain regions in the finite element model, and the induced electric field distribution data corresponding to the plurality of brain components are obtained.

[0059] The target brain region is a brain region to which stimulation is needed. The target brain region can be pre-set. The target brain region can also be determined in response to a selection operation on the plurality of brain regions in the finite element model, for example, in response to the selection operation on the plurality of brain regions in the finite element model, the selected brain region is taken as the target brain region. The induced electric field distribution data is quantitative information representing the distribution rule of the electric field generated by the coil in space. The induced electric field distribution data includes but is not limited to the electric field intensity and the electric field direction.

[0060] Specifically, in response to the selection operation on the plurality of brain regions in the finite element model, the selected brain region is taken as the target brain region, the coil is used to stimulate the target brain region, and the induced electric field distribution data corresponding to the plurality of brain components are obtained, thereby realizing the determination of the induced electric field distribution data and providing data support for the detection of the coil.

[0061] For example, Figures 3-6 . Figure 3 is a schematic diagram of the induced electric field distribution data of the gray matter provided by an embodiment of the application. Figure 4 is a schematic diagram of the induced electric field distribution data of the cerebrospinal fluid provided by an embodiment of the application. Figure 5 is a schematic diagram of the induced electric field distribution data of the skull provided by an embodiment of the application. Figure 6 is a schematic diagram of the induced electric field distribution data of the scalp provided by an embodiment of the application.

[0062] Optionally, the target brain region in the plurality of brain regions in the finite element model is stimulated based on the coil to obtain induced electric field distribution data corresponding to the plurality of brain components respectively, including: determining the resistive-inductive parameter based on the pre-set coil current rate of change and the coil parameter of the coil, and determining the current information of the coil based on the coil current rate of change and the resistive-inductive parameter; the resistive-inductive parameter includes the resistance information and the inductance information of the coil; determining the current density information based on the current information and the coil parameter, determining the magnetic field strength based on the current density information, and determining the magnetic induction intensity based on the magnetic field strength; the current density information, the magnetic field strength and the magnetic induction intensity are substituted into the time-domain Maxwell equations to obtain the induced electric field distribution data corresponding to the plurality of brain components respectively.

[0063] The coil current rate of change can be set as dI / dT = 1 x 106A / s, and the coil current rate of change is set according to requirements, which is not limited here. The coil parameters include but are not limited to coil material, electrical conductivity, coil diameter, number of turns and cross-sectional area of wire. The resistive-inductive parameter is a parameter representing the electromagnetic characteristics of the coil. The resistive-inductive parameter includes the resistance information and the inductance information of the coil. The current information is information describing the size, direction and time-varying law of the current flowing through the coil. For example, the coil current rate of change and the resistive-inductive parameter can be input into the finite element analysis software for processing to determine the current information of the coil. For another example, the current information of the coil can be calculated according to the magnetic stimulation principle. For example, see Figure 7 , Figure 7 is a schematic diagram of a magnetic stimulation principle provided by an embodiment of the application. Wherein V represents output voltage, and C represents circuit capacitance. The current information of the coil can be calculated by the following formula:

[0064]

[0065] Wherein ω1 represents the attenuation coefficient; and ω2 represents the oscillation angular frequency.

[0066] The current density information is data describing the spatial distribution characteristics of the current in the coil, specifically referring to the current flowing through the unit cross-sectional area of the coil. For example, the ratio between the current information of the coil and the cross-sectional area of the wire can be calculated, and the above ratio is taken as the current density information. The magnetic field strength is a physical quantity describing the strength of the current information of the coil. The magnetic field strength can be determined according to the current density information, for example, the current density information is input into the finite element analysis software for processing to determine the magnetic field strength. The magnetic induction intensity is a physical quantity describing the strength of the force of the magnetic field on the coil. The magnetic induction intensity can be determined according to the magnetic field strength. For example, the magnetic induction intensity can be calculated according to the following formula:

[0067] B = μ0H;

[0068] Wherein, B represents magnetic induction intensity, μ0 represents vacuum permeability, H represents magnetic field intensity. Time domain Maxwell equations are as follows:

[0069]

[0070] D=ε0E+P;

[0071] Wherein, Divergence factor is represented;H represents magnetic field intensity;J represents current density information;D represents electric displacement vector; Rotational factor is represented;E represents electric field intensity;B represents magnetic induction intensity;ε0 represents vacuum dielectric constant;P represents polarization intensity.

[0072] Specifically, the coil current change rate and the coil parameter are set in advance, the coil current change rate and the coil parameter are input into the finite element analysis software for processing, the current information of the coil is determined, the ratio between the current information of the coil and the cross-sectional area of the wire is calculated, the ratio is taken as the current density information, the current density information is input into the finite element analysis software for processing, the magnetic field intensity is determined, the magnetic induction intensity is determined according to the magnetic induction intensity calculation formula, the current density information, the magnetic field intensity and the magnetic induction intensity are substituted into the time domain Maxwell equations, the induced electric field distribution data corresponding to the plurality of brain parts are obtained, and the accurate determination of the induced electric field distribution data corresponding to the plurality of brain parts is realized, thereby providing comprehensive and accurate data support for the detection of the coil.

[0073] On the basis of the above embodiment, the method further includes determining a magnetic field interface, setting a constraint condition of the magnetic field, determining a solver and a solving time range. Wherein, the constraint condition can include a magnetic insulation boundary, a magnetic potential constraint and an impedance boundary condition. For example, the magnetic insulation boundary can be n x H = 0;The magnetic potential constraint can be a preset magnetic potential value;The impedance boundary condition can be to simulate far-field radiation;The solver can be a time domain solver;The solving time range can be 0-4 milliseconds.

[0074] S150, based on the induced electric field distribution data corresponding to the target brain area and the induced electric field distribution data corresponding to the brain area adjacent to the target brain area, determine the stimulation index data corresponding to the coil, and the stimulation index data is used to indicate the stimulation focusing degree of the coil.

[0075] The stimulation index data is used to quantify the spatial focusing of the coil on the target brain area, and reflects the distribution concentration of the magnetic field energy in the target area. The stimulation index data can be determined according to the induced electric field distribution data corresponding to the target brain area and the induced electric field distribution data corresponding to the brain area adjacent to the target brain area. For example, the induced electric field distribution data corresponding to the target brain area and the induced electric field distribution data corresponding to the brain area adjacent to the target brain area can be input into a stimulation index data calculation model to obtain the stimulation index data.

[0076] Specifically, the induced electric field distribution data corresponding to the target brain area and the induced electric field distribution data corresponding to the brain area adjacent to the target brain area are input into a stimulation index data calculation model to obtain the stimulation index data, which realizes accurate calculation of the stimulation index data of the coil and provides a basis for subsequent adjustment of the coil.

[0077] S160, in the case that the stimulation index data does not satisfy the preset condition, adjusting the coil parameter of the coil to make the stimulation index data satisfy the preset condition.

[0078] The preset condition can be that the stimulation index data is greater than or equal to a preset threshold. Adjusting the coil parameter of the coil includes but is not limited to adjusting the number of turns of the coil and the diameter of the coil.

[0079] Specifically, when the stimulation index data is less than the preset threshold, it indicates that the stimulation index data does not satisfy the preset condition, and the coil needs to be adjusted. The number of turns of the coil and the diameter of the coil are adjusted, the target brain area is stimulated according to the adjusted coil parameter, and the induced electric field distribution data corresponding to each of the plurality of brain components and the stimulation index data are updated until the stimulation index data satisfies the preset condition, which realizes accurate detection of the coil and provides accurate basis for subsequent treatment.

[0080] The technical scheme of the embodiment, by acquiring brain atlas data, since the brain atlas data is expected image data constructed based on the brain magnetic resonance imaging data of a plurality of first reference objects, can provide standardized data support for subsequent analysis, and improve the universality of the analysis result; based on the brain atlas data, three-dimensional image models corresponding to a plurality of brain components are determined respectively, which provides accurate spatial coordinates for the detection of the coil, thereby facilitating the improvement of the accuracy of the coil detection; based on a plurality of three-dimensional image models, a finite element model is constructed, the finite element model includes three-dimensional coordinate information corresponding to a plurality of brain components, the construction of the finite element model is realized, and model support is provided for subsequent analysis and processing; based on the coil, a target brain area in the plurality of brain areas of the finite element model is stimulated, and a plurality of brain components respectively corresponding to the induced electric field distribution data are obtained, the determination of the induced electric field distribution data is realized, and data support is provided for the detection of the coil; based on the induced electric field distribution data corresponding to the target brain area and the induced electric field distribution data corresponding to the brain area adjacent to the target brain area, the stimulation index data corresponding to the coil is determined, the stimulation index data is used to indicate the stimulation focusing degree of the coil, the accurate calculation of the stimulation index data of the coil is realized, and a basis is provided for subsequent adjustment of the coil; in the case that the stimulation index data does not satisfy the preset condition, the coil parameters of the coil are adjusted to make the stimulation index data satisfy the preset condition, and the accurate detection and flexible adjustment of the coil are realized.

[0081] Embodiment two

[0082] Figure 8 is a flowchart of a coil detection method for transcranial magnetic stimulation provided by the second embodiment of the application. The embodiment is a refinement of the above-mentioned embodiment, and on the basis of the foregoing embodiment, the determination of the stimulation index data corresponding to the target brain area based on the induced electric field distribution data corresponding to the target brain area and the induced electric field distribution data corresponding to the brain area adjacent to the target brain area is refined, and the specific implementation can be referred to the technical scheme of the embodiment. Among them, the same or corresponding technical terms as the above-mentioned embodiment will not be described here. As shown in the embodiment, the method comprises: Figure 8

[0083] S210, acquiring brain atlas data, the brain atlas data being expected image data constructed based on the brain magnetic resonance imaging data of a plurality of first reference objects.

[0084] S220, based on the brain atlas data, three-dimensional image models corresponding to a plurality of brain components are determined respectively, the brain components including a plurality of brain areas, a scalp, a skull and cerebrospinal fluid.

[0085] S230, based on a plurality of three-dimensional image models, a finite element model is constructed, the finite element model including three-dimensional coordinate information corresponding to a plurality of brain components.

[0086] ​S240, based on the coil pair finite element model, a target brain area in the plurality of brain areas is stimulated to obtain a plurality of brain components corresponding to the induced electric field distribution data.

[0087] Optionally, before the coil pair finite element model is applied to stimulate the target brain area in the plurality of brain areas, the method further comprises: determining the target brain area in the plurality of brain areas in the finite element model, based on the three-dimensional coordinate information corresponding to the target brain area and the three-dimensional coordinate information corresponding to the scalp, determining the first position of the target brain area, and placing the center of the coil at the first position; the first position is the position closest to the scalp in the target brain area.

[0088] The first position is the position closest to the scalp in the target brain area. The first position can be determined according to the three-dimensional coordinate information corresponding to the target brain area and the three-dimensional coordinate information corresponding to the scalp. For example, the Euclidean distance between each three-dimensional coordinate information in the target brain area and each three-dimensional coordinate information corresponding to the scalp is calculated, and the three-dimensional coordinate information corresponding to the smallest Euclidean distance is selected as the first position.

[0089] Specifically, the Euclidean distance between each three-dimensional coordinate information in the target brain area and each three-dimensional coordinate information corresponding to the scalp is calculated, and the three-dimensional coordinate information corresponding to the smallest Euclidean distance is selected as the first position. The center of the coil is placed at the first position, which realizes the accurate placement of the coil, and further improves the accuracy of the stimulation index data.

[0090] For example, see Figure 9 , Figure 9 is a schematic diagram of the coil placement provided by the embodiment of the application. The center of the coil is located at the first position of the target brain area.

[0091] Optionally, the method further comprises: based on the first grid information of the brain components in the finite element model, the brain components in the finite element model are meshed to obtain a first grid model; based on the material attribute information of the coil and the second grid information, the coil is meshed to obtain a second grid model.

[0092] The first grid information is a parameter for grid division of the brain component in the finite element model. The first grid information includes but is not limited to a grid type, a maximum grid unit, a minimum grid unit, a maximum grid unit growth rate, a curvature factor, and a narrow region resolution. For example, the grid type can be a regular grid, the maximum grid unit can be 30.3 millimeters, the minimum grid unit can be 5.46 millimeters, the maximum grid unit growth rate can be 1.5, the curvature factor can be 0.6, and the narrow region resolution can be 0.5. The first grid model is a discretization model corresponding to the brain component in the finite element model. The material attribute information is a parameter for describing the physical characteristics of the coil in electromagnetic calculation. The material attribute information includes but is not limited to the material, the electrical conductivity, and the magnetic permeability of the coil. The second grid information is a parameter for grid division of the coil. The second grid information includes but is not limited to a grid type, a maximum grid unit, a minimum grid unit, a maximum grid unit growth rate, a curvature factor, and a narrow region resolution. For example, the grid type can be a fine grid, the maximum grid unit can be 16.7 millimeters, the minimum grid unit can be 1.21 millimeters, the maximum grid unit growth rate can be 1.4, the curvature factor can be 0.4, and the narrow region resolution can be 0.7. The second grid model is a discretization model corresponding to the coil.

[0093] Specifically, the grid type, the maximum grid unit, the minimum grid unit, the maximum grid unit growth rate, the curvature factor, and the narrow region resolution corresponding to the brain component in the finite element model are input into a grid division area in the finite element analysis software to obtain the first grid model. The grid type, the maximum grid unit, the minimum grid unit, the maximum grid unit growth rate, the curvature factor, and the narrow region resolution corresponding to the coil are input into a grid division area in the finite element analysis software to obtain the second grid model. Subsequent analysis and processing are performed based on the first grid model and the second grid model.

[0094] S250, based on the induced electric field distribution data corresponding to the target brain region, determining first average electric field intensity information corresponding to the target brain region; based on the induced electric field distribution data corresponding to the brain region adjacent to the target brain region, determining second average electric field intensity information corresponding to the brain region adjacent to the target brain region; based on the first average electric field intensity information and the second average electric field intensity information, determining stimulation index data corresponding to the target brain region.

[0095] The first average electric field intensity information is the average induced electric field intensity of the target brain area, and represents the intensity of the stimulation of the target brain area. The first average electric field intensity information can be determined according to the induced electric field distribution data corresponding to the target brain area. For example, the target brain area in the finite element model includes a plurality of spatial units, the electric field intensity information corresponding to each spatial unit is determined according to the induced electric field distribution data, the average value of the electric field intensity information of the plurality of spatial units is calculated, and the average value is taken as the first average electric field intensity information corresponding to the target brain area. The second average electric field intensity information is the average induced electric field intensity of the brain area adjacent to the target brain area, and represents the intensity of the stimulation of the brain area adjacent to the target brain area. The second average electric field intensity information can be determined according to the induced electric field distribution data corresponding to the brain area adjacent to the target brain area. For example, the brain area adjacent to the target brain area in the finite element model includes a plurality of spatial units, the electric field intensity information corresponding to each spatial unit is determined according to the induced electric field distribution data, the average value of the electric field intensity information of the plurality of spatial units is calculated, and the average value is taken as the second average electric field intensity information corresponding to the brain area adjacent to the target brain area. The stimulation index data can be determined according to the first average electric field intensity information and the second average electric field intensity information. For example, the ratio between the first average electric field intensity information and the second average electric field intensity information is calculated, and the ratio is taken as the stimulation index data. The greater the stimulation index data, the better the stimulation focusing degree of the coil.

[0096] Specifically, the target brain area in the finite element model includes a plurality of spatial units, the electric field intensity information corresponding to each spatial unit is determined according to the induced electric field distribution data, the average value of the electric field intensity information of the plurality of spatial units is calculated, and the average value is taken as the first average electric field intensity information corresponding to the target brain area; the brain area adjacent to the target brain area in the finite element model includes a plurality of spatial units, the electric field intensity information corresponding to each spatial unit is determined according to the induced electric field distribution data, the average value of the electric field intensity information of the plurality of spatial units is calculated, and the average value is taken as the second average electric field intensity information corresponding to the brain area adjacent to the target brain area; the ratio between the first average electric field intensity information and the second average electric field intensity information is calculated, and the ratio is taken as the stimulation index data, thereby realizing accurate calculation of the stimulation index data and providing a basis for adjustment of the coil.

[0097] S260, in the case where the stimulation index data does not satisfy the preset condition, adjusting the coil parameter of the coil to make the stimulation index data satisfy the preset condition.

[0098] The technical solution of this embodiment acquires brain atlas data. Since brain atlas data is expected image data constructed based on brain magnetic resonance imaging data of multiple first reference subjects, it can provide standardized data support for subsequent analysis and improve the universality of the analysis results. Based on the brain atlas data, three-dimensional image models corresponding to multiple brain components are determined, providing accurate spatial coordinates for coil detection, thereby improving the accuracy of coil detection. A finite element model is constructed based on multiple three-dimensional image models. The finite element model includes the three-dimensional coordinate information corresponding to multiple brain components, realizing the construction of the finite element model and providing model support for subsequent analysis and processing. Based on the coil, stimulation is applied to the target brain regions in multiple brain regions of the finite element model to obtain the values ​​of multiple brain components. The system identifies the corresponding induced electric field distribution data, providing data support for coil detection. Based on the induced electric field distribution data corresponding to the target brain region, it determines the first average electric field strength information for that target brain region. Based on the induced electric field distribution data corresponding to the adjacent brain regions, it determines the second average electric field strength information for those adjacent brain regions. Based on the first and second average electric field strength information, it determines the stimulation index data corresponding to the target brain region, enabling precise calculation of the stimulation index data and providing a basis for coil adjustment. If the stimulation index data does not meet the preset conditions, the coil parameters are adjusted to make the stimulation index data meet the preset conditions, achieving precise detection and flexible adjustment of the coil.

[0099] Example 3

[0100] Figure 10 This is a schematic diagram of a coil detection device for transcranial magnetic stimulation provided in Embodiment 3 of the present invention. Figure 10 As shown, the device includes a brain mapping data acquisition module 310, a three-dimensional image model determination module 320, a finite element model construction module 330, an induced electric field distribution data determination module 340, a stimulation index data determination module 350, and a coil parameter adjustment module 360.

[0101] The brain atlas data acquisition module 310 is configured to acquire brain atlas data, the brain atlas data being expected image data constructed based on brain magnetic resonance imaging data of a plurality of first reference objects; the three-dimensional image model determination module 320 is configured to determine a three-dimensional image model corresponding to each of a plurality of brain components based on the brain atlas data, the brain components including a plurality of brain regions, a scalp, a skull, and cerebrospinal fluid; the finite element model construction module 330 is configured to construct a finite element model based on the plurality of three-dimensional image models, the finite element model including three-dimensional coordinate information corresponding to the plurality of brain components; the induced electric field distribution data determination module 340 is configured to apply stimulation to a target brain region in the plurality of brain regions of the finite element model by using the coil to obtain induced electric field distribution data corresponding to each of the plurality of brain components; the stimulation index data determination module 350 is configured to determine stimulation index data corresponding to the coil based on the induced electric field distribution data of the target brain region and the induced electric field distribution data of a brain region adjacent to the target brain region, the stimulation index data being used to indicate a stimulation focusing degree of the coil; and the coil parameter adjustment module 360 is configured to adjust a coil parameter of the coil when the stimulation index data does not satisfy a preset condition, so that the stimulation index data satisfies the preset condition.

[0102] The technical scheme of the embodiment, through the brain atlas data acquisition module 310, acquires brain atlas data, since the brain atlas data is expected image data constructed based on brain magnetic resonance imaging data of a plurality of first reference objects, can provide standardized data support for subsequent analysis, and improve the universality of the analysis result; through the three-dimensional image model determination module 320, determines a three-dimensional image model corresponding to each of a plurality of brain components based on the brain atlas data, provides precise spatial coordinates for detection of the coil, and thus is conducive to improving the accuracy of the coil detection; through the finite element model construction module 330, constructs a finite element model based on the plurality of three-dimensional image models, the finite element model including three-dimensional coordinate information corresponding to the plurality of brain components, realizes construction of the finite element model, and provides model support for subsequent analysis and processing; through the induced electric field distribution data determination module 340, applies stimulation to a target brain region in the plurality of brain regions of the finite element model by using the coil to obtain induced electric field distribution data corresponding to each of the plurality of brain components, realizes determination of the induced electric field distribution data, and provides data support for detection of the coil; through the stimulation index data determination module 350, determines stimulation index data corresponding to the coil based on the induced electric field distribution data of the target brain region and the induced electric field distribution data of a brain region adjacent to the target brain region, the stimulation index data being used to indicate a stimulation focusing degree of the coil, realizes precise calculation of the stimulation index data of the coil, and provides a basis for subsequent adjustment of the coil; through the coil parameter adjustment module 360, adjusts a coil parameter of the coil when the stimulation index data does not satisfy a preset condition, so that the stimulation index data satisfies the preset condition, realizes precise detection and flexible adjustment of the coil.

[0103] Based on the above-mentioned embodiments, optionally, the stimulation index data determination module 350 is further configured to: determine first average electric field intensity information corresponding to the target brain area based on the induced electric field distribution data corresponding to the target brain area; determine second average electric field intensity information corresponding to the brain area adjacent to the target brain area based on the induced electric field distribution data corresponding to the brain area adjacent to the target brain area; and determine the stimulation index data corresponding to the target brain area based on the first average electric field intensity information and the second average electric field intensity information.

[0104] Optionally, the brain components include a plurality of brain areas; and the three-dimensional image model determination module 320 is further configured to: segment the brain atlas data based on a first image segmentation algorithm to obtain a plurality of first segmented images respectively corresponding to the plurality of brain areas; and reconstruct the first segmented images based on a first image reconstruction algorithm to obtain a plurality of three-dimensional image models respectively corresponding to the plurality of brain areas.

[0105] Optionally, the brain components include a scalp, a skull and cerebrospinal fluid; and the three-dimensional image model determination module 320 is further configured to: perform registration processing on the brain atlas data based on a pre-set registration template to obtain registered brain atlas data; the registration template is an expected registration template constructed based on T1-weighted MRI scan images of a plurality of second reference objects; segment the registered brain atlas data based on a second image segmentation algorithm to obtain a plurality of second segmented images respectively corresponding to the scalp, the skull and the cerebrospinal fluid; and reconstruct the second segmented images based on a second image reconstruction algorithm to obtain a plurality of three-dimensional image models respectively corresponding to the scalp, the skull and the cerebrospinal fluid.

[0106] Optionally, the induced electric field distribution data determination module 340 is further configured to: determine a resistive-inductive parameter based on a pre-set coil current rate of change and coil parameters of the coil, determine current information of the coil based on the coil current rate of change and the resistive-inductive parameter; the resistive-inductive parameter includes resistance information and inductance information of the coil; determine current density information based on the current information and the coil parameters, determine magnetic field strength based on the current density information, and determine magnetic induction intensity based on the magnetic field strength; and substitute the current density information, the magnetic field strength and the magnetic induction intensity into a time-domain Maxwell equation set to obtain induced electric field distribution data respectively corresponding to the plurality of brain components.

[0107] Optionally, the device further includes a first position determination module configured to: determine a target brain area in a plurality of brain areas of a finite element model before applying stimulation to the target brain area in the plurality of brain areas of the finite element model based on the coil, determine a first position of the target brain area based on three-dimensional coordinate information corresponding to the target brain area and three-dimensional coordinate information corresponding to the scalp, and place a center of the coil at the first position; the first position is a position closest to the scalp in the target brain area.

[0108] Optionally, the device further includes a second mesh model determination module, used to: mesh the brain components in the finite element model based on the first mesh information of the brain components in the finite element model to obtain a first mesh model; and mesh the coil based on the material property information and second mesh information of the coil to obtain a second mesh model.

[0109] The coil detection device for transcranial magnetic stimulation provided in this embodiment of the invention can execute the coil detection method for transcranial magnetic stimulation provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0110] Example 4

[0111] Figure 11 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0112] like Figure 11 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The ROM 12 may also store various programs and data required for the operation of the electronic device 10. The processor 11, the ROM 12, and the ROM 12 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0113] A plurality of components in the electronic device 10 are connected to an input / output (I / O) interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0114] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as a coil detection method for transcranial magnetic stimulation.

[0115] In some embodiments, a coil detection method for transcranial magnetic stimulation can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the read-only memory (ROM) 12 and / or the communication unit 19. When the computer program is loaded onto the read-only memory (ROM) 12 and executed by the processor 11, one or more steps of a coil detection method for transcranial magnetic stimulation described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform a coil detection method for transcranial magnetic stimulation by any other appropriate means, such as by means of firmware.

[0116] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0117] A computer program for implementing a coil detection method for transcranial magnetic stimulation of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor, implements the functions / operations specified in the flow diagrams and / or block diagrams. The computer program can be executed in whole on the machine, partially on the machine, partially on the machine as a stand-alone software package, partially on the machine and partially on a remote machine or entirely on a remote machine or server.

[0118] Embodiment five

[0119] Embodiment five of the present application also provides a computer readable storage medium, the computer readable storage medium stores computer instructions, the computer instructions are used to make a processor execute a coil detection method for transcranial magnetic stimulation, the method comprises:

[0120] Obtaining brain atlas data, the brain atlas data is expected image data constructed based on brain magnetic resonance imaging data of a plurality of first reference objects; determining a plurality of three-dimensional image models corresponding to a plurality of brain components based on the brain atlas data, the brain components including a plurality of brain regions, a scalp, a skull and cerebrospinal fluid; constructing a finite element model based on the plurality of three-dimensional image models, the finite element model including three-dimensional coordinate information corresponding to the plurality of brain components; applying stimulation to a target brain region in the plurality of brain regions of the finite element model based on the coil to obtain induced electric field distribution data corresponding to the plurality of brain components respectively; determining stimulation index data corresponding to the coil based on the induced electric field distribution data corresponding to the target brain region and the induced electric field distribution data corresponding to the brain region adjacent to the target brain region, the stimulation index data being used to indicate a stimulation focusing degree of the coil; and adjusting a coil parameter of the coil in a case where the stimulation index data does not satisfy a preset condition, so as to make the stimulation index data satisfy the preset condition.

[0121] In the context of the present application, the computer readable storage medium can be a tangible medium that can contain or store the computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. Alternatively, the computer readable storage medium can be a machine readable signal medium. More specific examples of the machine readable storage medium will include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0122] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; 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 can be received in any form, including acoustic, speech, or tactile input.

[0123] 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 a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0124] The computing system can 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 is generally established by computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0125] In particular, the processes described above with reference to the flowcharts can be implemented as computer software programs in accordance with embodiments of the application. For example, embodiments of the application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program comprising program code for executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the ROM 12. When the computer program is executed by the processor 11, the above-described functions defined in the methods of embodiments of the application are executed.

[0126] It should be understood that the various forms of flow shown above can be used to reorder, add, or delete steps. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0127] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A coil detection method for transcranial magnetic stimulation, characterized in that, include: Obtain brain atlas data, which is expected image data constructed based on brain magnetic resonance imaging data of multiple first reference subjects; Based on the brain atlas data, three-dimensional image models corresponding to multiple brain components are determined, including multiple brain regions, scalp, skull, and cerebrospinal fluid. A finite element model is constructed based on multiple three-dimensional image models, and the finite element model includes three-dimensional coordinate information corresponding to multiple brain components; By applying stimulation to target brain regions in multiple brain regions of the finite element model using coils, induced electric field distribution data corresponding to multiple brain components are obtained. Based on the induced electric field distribution data corresponding to the target brain region and the induced electric field distribution data corresponding to the brain regions adjacent to the target brain region, the stimulation index data corresponding to the coil is determined, and the stimulation index data is used to indicate the degree of stimulation focus of the coil. If the stimulation index data does not meet the preset conditions, the coil parameters of the coil are adjusted so that the stimulation index data meets the preset conditions.

2. The method according to claim 1, characterized in that, The determination of stimulation index data corresponding to the target brain region based on the induced electric field distribution data corresponding to the target brain region and the induced electric field distribution data corresponding to the brain regions adjacent to the target brain region includes: Based on the induced electric field distribution data corresponding to the target brain region, the first average electric field strength information corresponding to the target brain region is determined; Based on the induced electric field distribution data of the brain regions adjacent to the target brain region, the second average electric field strength information of the brain regions adjacent to the target brain region is determined. Based on the first average electric field strength information and the second average electric field strength information, the stimulation index data corresponding to the target brain region is determined.

3. The method according to claim 1, characterized in that, The brain components include multiple brain regions; the step of determining the three-dimensional image models corresponding to the multiple brain components based on the brain atlas data includes: The brain atlas data is segmented based on the first image segmentation algorithm to obtain first segmented images corresponding to multiple brain regions respectively; Based on the first image reconstruction algorithm, the first segmented image is reconstructed to obtain the three-dimensional image models corresponding to the multiple brain regions.

4. The method according to claim 1, characterized in that, The brain components include the scalp, the skull, and the cerebrospinal fluid; the step of determining three-dimensional image models corresponding to multiple brain components based on the brain atlas data includes: The brain atlas data is registered based on a pre-set registration template to obtain the registered brain atlas data; the registration template is a desired registration template constructed based on T1-weighted MRI scan images of multiple second reference objects; The registered brain atlas data is segmented based on the second image segmentation algorithm to obtain second segmented images corresponding to the scalp, the skull and the cerebrospinal fluid, respectively. Based on the second image reconstruction algorithm, the second segmented image is reconstructed to obtain the three-dimensional image models corresponding to the scalp, the skull, and the cerebrospinal fluid, respectively.

5. The method according to claim 1, characterized in that, The method involves applying stimulation to target brain regions within multiple brain regions of the finite element model using coils to obtain induced electric field distribution data corresponding to each of the multiple brain components, including: The resistance and inductance parameters are determined based on the pre-set coil current change rate and the coil parameters, and the current information of the coil is determined based on the coil current change rate and the resistance and inductance parameters; the resistance and inductance parameters include the resistance information and inductance information of the coil. The current density information is determined based on the current information and the coil parameters; the magnetic field strength is determined based on the current density information; and the magnetic induction intensity is determined based on the magnetic field strength. Substituting the current density information, the magnetic field strength, and the magnetic induction intensity into the time-domain Maxwell's equations, we obtain the induced electric field distribution data corresponding to each of the brain components.

6. The method according to claim 1, characterized in that, Before applying stimulation to the target brain region among the multiple brain regions of the finite element model based on the coil, the method further includes: The target brain region among the multiple brain regions in the finite element model is determined. Based on the three-dimensional coordinate information corresponding to the target brain region and the three-dimensional coordinate information corresponding to the scalp, the first position of the target brain region is determined, and the center of the coil is placed at the first position. The first position is the position in the target brain region that is closest to the scalp.

7. The method according to claim 1, characterized in that, The method further includes: Based on the first mesh information of the brain components in the finite element model, the brain components in the finite element model are meshed to obtain the first mesh model; Based on the material properties of the coil and the second mesh information, the coil is meshed to obtain a second mesh model.

8. A coil detection device for transcranial magnetic stimulation, characterized in that, include: The brain mapping data acquisition module is used to acquire brain mapping data, which is expected image data constructed based on brain magnetic resonance imaging data of multiple first reference objects. The three-dimensional image model determination module is used to determine three-dimensional image models corresponding to multiple brain components based on the brain atlas data. The brain components include multiple brain regions, scalp, skull, and cerebrospinal fluid. A finite element model construction module is used to construct a finite element model based on multiple three-dimensional image models, wherein the finite element model includes three-dimensional coordinate information corresponding to multiple brain components; The induced electric field distribution data determination module is used to apply stimulation to target brain regions in multiple brain regions of the finite element model based on a coil, and obtain induced electric field distribution data corresponding to each of the multiple brain components. The stimulation index data determination module is used to determine the stimulation index data corresponding to the coil based on the induced electric field distribution data corresponding to the target brain region and the induced electric field distribution data corresponding to the brain region adjacent to the target brain region. The stimulation index data is used to indicate the degree of stimulation focus of the coil. The coil parameter adjustment module is used to adjust the coil parameters of the coil so that the stimulation index data meets the preset conditions when the stimulation index data does not meet the preset conditions.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform a coil detection method for transcranial magnetic stimulation according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute and implement the coil detection method for transcranial magnetic stimulation according to any one of claims 1-7.

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