Craniocerebral low-frequency conductivity tensor modeling method and system, medium and electronic equipment

Abstract

The invention provides a brain low-frequency conductivity tensor modeling method, a brain low-frequency conductivity tensor modeling system, a medium and electronic equipment. The method comprises the following steps: acquiring a phase image of a fast spin echo sequence of a brain; acquiring a high-frequency conductivity image of the brain based on the phase image; collecting a diffusion weighted amplitude image of the brain based on a multi-excitation diffusion weighted sequence; based on the diffusion weighted amplitude image, using a multi-interlayer model to obtain diffusion microstructure parameters of the brain; generating a low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameters; generating a digital head model based on the brain magnetization preparation fast gradient echo sequence; and generating a craniocerebral low-frequency conductivity tensor model based on the low-frequency conductivity tensor image and the digital head model. According to the brain low-frequency conductivity tensor modeling method and system, the medium and the electronic equipment, more accurate target point planning is provided for the tTIS.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical engineering, and in particular to a method, system, medium and electronic equipment for modeling low-frequency conductivity tensor of cranial brain. Background Art

[0002] The conductivity of biological tissue is an important biophysical parameter of biological tissue. High-frequency conductivity plays an important role in estimating the specific absorption ratio (SAR). Low-frequency conductivity plays a very important role in neuromodulation. For example, an accurate low-frequency conductivity model of the brain can improve source localization and improve the accuracy of transcranial electrical stimulation electric field calculation.

[0003] Traditional transcranial electrical stimulation, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), can usually only stimulate the superficial area of ​​the brain and has no focusing function, so whether or not to use an individualized cranial conductivity model has no significant impact on accuracy.

[0004] Transcranial Temporal Interference Stimulation (tTIS) is a new type of non-invasive transcranial neuroelectric regulation method. This method injects high-frequency sine waves with small frequency differences into the brain through multiple pairs of electrodes, which will produce low-frequency amplitude modulation waves in the target area of ​​the brain. Therefore, this technology has strong focusing. Different conductivity models will significantly affect the location of the target and the selection of electrode positions. Its effect in treating neurological diseases is very dependent on the accuracy of the stimulation target area. The construction of the subject's brain conductivity model is required to locate the target stimulation nucleus and estimate the electric field strength in the target area. However, to date, all tTIS use empirical brain conductivity, that is, all subjects use the conductivity values ​​recommended by the literature, without considering individual differences in conductivity. Studies have shown that brain conductivity changes with physiology and pathology. Therefore, developing an accurate individualized low-frequency conductivity tensor imaging method to construct a brain conductivity model is of great significance for the accurate positioning of the tTIS target area and subsequent treatment of neurological diseases.

[0005] Conductivity tensor imaging (CTI) is a method that can reconstruct the anisotropic low-frequency conductivity tensor of biological tissues. Its basic principle is as follows: high-frequency current can ignore the permeability of the cell membrane, so high-frequency current can penetrate the cell, so high-frequency current is contributed by both the conductivity inside and outside the cell; while low-frequency current will be hindered by the cell membrane, so it mainly flows in the extracellular space, so low-frequency conductivity is mainly contributed by the extracellular conductivity. Conductivity tensor imaging is an imaging method that decomposes high-frequency conductivity based on the difference in volume fraction inside and outside the cell to obtain low-frequency conductivity. In the prior art, magnetic resonance electrical property imaging can measure high-frequency conductivity, which is based on the B1 field inversion of high-frequency conductivity, while diffusion microstructure imaging can measure the volume fraction, diffusivity and fiber direction of cells. Conductivity tensor imaging combines the two methods to obtain a low-frequency conductivity tensor by decomposing high-frequency conductivity. Studies have shown that volume fraction plays a major role in the decomposition process. Therefore, using diffusion microstructure imaging to extract accurate intracellular and extracellular volume fractions is a difficulty in the CTI field. It also determines the accuracy of the tTIS cranial conductivity model, thereby affecting the accuracy of subsequent target planning and ultimately affecting the effectiveness of tTIS treatment.

[0006] In addition to conductivity tensor imaging, magnetic resonance electrical impedance imaging is a method of injecting current into the target body, using magnetic resonance to measure the phase disturbance caused by the injected current, and finally reconstructing the low-frequency conductivity tensor. However, this method requires multiple additional external devices, including magnetic resonance-compatible transcranial electrical stimulation devices, and it is also necessary to develop a special sequence to synchronize the magnetic resonance and electrical stimulation devices. This greatly increases the cost of use and operation time, and the imaging signal-to-noise ratio is low, and image reconstruction is an ill-posed inverse problem, so its clinical practicality is very limited. Compared with magnetic resonance electrical impedance imaging, conductivity tensor imaging does not require any external additional equipment. It only needs a commercial magnetic resonance sequence to collect all the information required for imaging, and its clinical practicality is very high.

[0007] The traditional CTI method has a large error in measuring the cell volume fraction, especially in the gray matter area. Specifically, the traditional method uses the biophysical model stick, airship and ball model to extract the information of intraneurite, extraneurite and free water in brain tissue respectively. The intraneurite includes dendrites and axons with high anisotropy, while the extraneurite includes highly isotropic neurons, glial cells and extracellular space. Therefore, the extraneurite space still contains a large number of intracellular components, and the traditional method cannot accurately distinguish the cellular components from the extracellular components in the extraneurite space. Therefore, when establishing the model, only the intraneurite volume fraction is approximately regarded as the intracellular volume fraction, which causes the established model to have serious deviations in the gray matter area. For example, the traditional method has an intraneurite volume fraction of about 0.30 in the gray matter area. This shows that nearly 70% of the volume in the gray matter is extracellular water, which is not in line with common sense and is seriously contrary to existing physiological and anatomical evidence. The traditional CTI modeling method further affects the accuracy of the downstream tTIS brain conductivity model, and ultimately affects the accuracy of tTIS neural regulation.

[0008] In 2021, some researchers proposed to use a more advanced biophysical diffusion model (Apparent Soma and Neurite Imaging, SANDI) to extract the intracellular and extracellular volume fractions. However, this reconstruction method has high requirements for measuring instruments and measurement data, requires the magnetic resonance equipment to have a high gradient magnetic field, and the imaging time is very long, which limits its clinical application. Summary of the invention

[0009] In view of the above problems, the purpose of the present invention is to provide a method, system, medium, and electronic equipment for modeling a low-frequency conductivity tensor of the cranial brain, which can accurately construct a personalized low-frequency conductivity tensor model of the cranial brain, thereby providing more accurate target planning for tTIS.

[0010] In a first aspect, the present invention provides a method for modeling low-frequency conductivity tensor of the brain, the method comprising the following steps: acquiring a phase image of a fast spin echo sequence of the brain; acquiring a high-frequency conductivity image of the brain based on the phase image; acquiring a diffusion-weighted amplitude image of the brain based on a multi-excitation diffusion-weighted sequence; based on the diffusion-weighted amplitude image, using a multi-layer model to acquire diffusion microstructure parameters of the brain; generating a low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameters; generating a digital skull model based on a magnetization-prepared fast gradient echo sequence of the brain; and generating a low-frequency conductivity tensor model of the brain based on the low-frequency conductivity tensor image and the digital skull model.

[0011] In an implementation of the first aspect, acquiring the high-frequency conductivity image of the brain based on the phase image includes:

[0012] Based on the phase image, the phase convection-diffusion reaction equation is used. Acquire the high frequency conductivity image Where ρ represents the resistivity, Indicates the transmit and receive phase, represents the Hamiltonian operator, ω0 represents the precession angular frequency of the magnetic resonance; μ0 represents the magnetic permeability constant in vacuum, and c represents the regularization parameter.

[0013] In an implementation of the first aspect, based on the diffusion weighted amplitude image, acquiring the diffusion microstructure parameters of the brain using a multi-layer model comprises the following steps:

[0014] The diffusion weighted amplitude image is fitted by using a multi-compartment model to obtain the diffusion microstructure parameters, which include intracellular diffusion rate, intracellular volume fraction, extracellular diffusion rate and extracellular diffusion tensor.

[0015] In an implementation of the first aspect, using a multi-layer model to fit the diffusion weighted amplitude image, obtaining the diffusion microstructure parameters includes the following steps:

[0016] Generate Simulation Label where f s represents the volume fraction of the stick interlayer, D s represents the stick interlayer diffusion rate, and represent the parallel and vertical diffusion rates of the airship, respectively, and f ext represents the extracellular volume fraction, D ext represents the extracellular diffusion rate, p l is the l-th order rotation invariant;

[0017] generating a simulation signal based on the simulation tag;

[0018] Using a polynomial regression model to fit the mapping space between the simulation label and the simulation signal;

[0019] The measured diffusion weighted amplitude data is input into the mapping space to obtain the estimated value of the corresponding diffusion microstructure parameter.

[0020] In an implementation of the first aspect, generating the low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameter includes:

[0021] according to Generate low-frequency conductivity tensor image σ L , where σH represents the high-frequency conductivity image, f ext represents the extracellular volume fraction, D int represents the intracellular diffusion rate, D ext represents the extracellular diffusion rate; β represents the ratio of ion concentration inside and outside the cell.

[0022] In an implementation of the first aspect, generating a digital skull model based on the magnetization prepared rapid gradient echo sequence of the skull comprises the following steps:

[0023] Acquiring a magnetic resonance image of the brain based on a magnetization prepared rapid gradient echo sequence;

[0024] A digital skull model is generated based on the magnetic resonance image.

[0025] In an implementation of the first aspect, generating a cranial low-frequency conductivity tensor model based on the low-frequency conductivity tensor image and the digital skull model comprises the following steps:

[0026] Segmenting the digital skull model into intracranial and extracranial parts;

[0027] Acquiring an intra-skull conductivity tensor and an extra-skull conductivity tensor based on the low-frequency conductivity tensor image;

[0028] A low-frequency conductivity tensor model of the brain is generated based on the conductivity tensor inside the skull and the conductivity tensor outside the skull.

[0029] In a second aspect, the present invention provides a cranial low-frequency conductivity tensor modeling system, the system comprising an acquisition module, a first acquisition module, a second acquisition module, a third acquisition module, a first generation module, a second generation module and a third generation module;

[0030] The acquisition module is used to obtain a phase image of a fast spin echo sequence of the brain;

[0031] The first acquisition module is used to acquire a high-frequency conductivity image of the brain based on the phase image;

[0032] The second acquisition module is used to acquire the diffusion-weighted amplitude image of the brain based on a multi-excitation diffusion-weighted sequence;

[0033] The third acquisition module is used to acquire the diffusion microstructure parameters of the brain by using a multi-layer model based on the diffusion weighted amplitude image;

[0034] The first generating module is used to generate a low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameter;

[0035] The second generating module is used to generate a digital skull model based on the magnetization prepared rapid gradient echo sequence of the skull;

[0036] The third generating module is used to generate a cranial low-frequency conductivity tensor model based on the low-frequency conductivity tensor image and the digital skull model.

[0037] In a third aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for modeling the low-frequency conductivity tensor of the cranial brain.

[0038] In a fourth aspect, the present invention provides an electronic device, comprising: a processor and a memory;

[0039] The memory is used to store computer programs;

[0040] The processor is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned cranial low-frequency conductivity tensor modeling method.

[0041] As described above, the method, system, medium, and electronic device for modeling low-frequency conductivity tensor of the cranial brain of the present invention have the following beneficial effects:

[0042] (1) It can accurately construct a personalized low-frequency conductivity tensor model of the brain, thereby providing more accurate target planning for tTIS;

[0043] (2) Accurately estimate the extracellular volume fraction based on a data-driven polynomial regression model, thereby obtaining a more accurate low-frequency conductivity tensor value;

[0044] (3) The problem of low modeling accuracy of the traditional CTI method was solved. By constructing a low-frequency conductivity tensor model of the brain, it facilitated the subsequent neural regulation modeling and target planning of tTIS. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Shown is a flow chart of a method for modeling low-frequency conductivity tensor of the brain according to an embodiment of the present invention;

[0046] Figure 2 It is a schematic diagram showing the structure of a method for modeling a cranial low-frequency conductivity tensor in one embodiment of the present invention;

[0047] Figure 3 It is a schematic diagram showing a comparison between the prior art and the cranial brain low-frequency conductivity tensor modeling method of the present invention in one embodiment;

[0048] Figure 4 Shown is a schematic structural diagram of a cranial low-frequency conductivity tensor modeling system of the present invention in one embodiment;

[0049] Figure 5 It is a schematic structural diagram of an electronic device of the present invention in one embodiment. DETAILED DESCRIPTION

[0050] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0051] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0052] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0053] like Figure 1 and Figure 2 As shown, in one embodiment, the method for modeling the low-frequency conductivity tensor of the brain of the present invention includes steps S1 to S7.

[0054] Step S1, obtaining a phase image of a fast spin echo sequence of the brain.

[0055] Specifically, a fast spin echo sequence is a pulse sequence that starts with a 90° excitation pulse, then applies multiple 180° pulses and generates multiple echo signals, and is placed in the same k-space to generate images. For images with the same spatial resolution, its imaging speed is significantly faster than that of a spin echo, and the image weight is determined by the echo time (TE) value of the central area of ​​the k-space. In the present invention, a phase image of a fast spin echo sequence of the brain is collected.

[0056] Step S2: acquiring a high-frequency conductivity image of the brain based on the phase image.

[0057] Specifically, according to Maxwell's equations, we can get:

[0058]

[0059] Where E(r) is the electric field, J(r) is the current density, H(r) is the magnetic field intensity, i is the imaginary unit, μ0 is the magnetic permeability of free space, κ is the node characteristic, ω is the magnetic resonance angular frequency, is the Hamiltonian operator. The current density J = κE = (σ + iω∈)E is given by the conduction current J c =σE (generated by the movement of mobile carriers) and displacement current J d :=iω∈E (produced by the polarization of immobile charges). Taking the curl operator we get:

[0060]

[0061] because We can get:

[0062]

[0063] According to the magnetic induction intensity B = μH, we can get the equation expressed by B:

[0064]

[0065] Since the k-space data measured by magnetic resonance is not B=(B x , B y , B z ), but B1 field, so we need to convert B into B1 field:

[0066]

[0067] Without complicated derivation, we can use the formula The phase-based convection-diffusion MREPT equation is obtained:

[0068]

[0069] Where ρ = 1 / σ H is the resistivity (the inverse of conductivity), is the phase of transmission and reception, that is, the phase of the magnetic resonance image. With amplitude and Phase plural With amplitude and Phase in Is the transmit and receive phase. The coefficients of the convection-diffusion equation are given by the phase Find the equation The solution is the high-frequency conductivity σ H The reciprocal of . is the Hamiltonian operator, which represents the gradient of space; ω0 represents the precession angular frequency of magnetic resonance; μ0 represents the magnetic permeability constant in vacuum. The equation in the figure contains many zero elements in the diagonal position, which causes the solution to oscillate. Therefore, diffusion terms are usually added to form the phase convection-diffusion reaction equation

[0070] Solving the phase convection-diffusion reaction equation, the solution obtained is the high-frequency conductivity σ H The reciprocal of . That is, the high-frequency conductivity image Where c represents the regularization parameter.

[0071] According to the divergence theorem equation Can be converted to The finite difference equation for this equation is as follows:

[0072]

[0073] Finally, it can be organized into the form of Aρ=b, where A is a sparse matrix of N*M*L×N*M*L, and b is a vector of 2ω0μ0.

[0074] Step S3: acquiring a diffusion-weighted amplitude image of the brain based on a multi-excitation diffusion-weighted sequence.

[0075] Specifically, the Readout Segmentation of Long Variable Echo-trains (RESOLVE) is a multi-excitation diffusion-weighted sequence that can obtain the corresponding diffusion-weighted amplitude image by diffusion-weighting the water molecules inside the brain. Among them, b is called the diffusion weighting factor, which represents the physical quantity of the degree of diffusion weighting. The larger the b value, the greater the diffusion weight of the water molecules and the smaller the acquisition signal.

[0076] Step S4: Based on the diffusion weighted amplitude image, a multi-layer model is used to obtain the diffusion microstructure parameters of the brain.

[0077] Specifically, the multi-layer model is a set of mathematical formulas, which are given according to the characteristics of diffusion. Among them, the diffusion can adopt a stick model, an airship model and a sphere model. The stick model indicates that water molecules can only diffuse along the direction of the stick, showing high anisotropy. The airship model indicates that they can diffuse in all directions, showing low anisotropy. The sphere model indicates that they can diffuse in all directions, showing isotropy. The volume fractions of the stick model, the airship model and the sphere model are weighted to form the kernel function of the multi-layer model. When the multi-layer model is used for fitting, this set of mathematical formulas is used to infinitely approximate the data. When the minimum error is reached, the parameters of the mathematical formula, that is, the volume fraction and the diffusion rate, can be obtained.

[0078] In the present invention, a multi-layer model is used to fit the diffusion weighted amplitude image to obtain the diffusion microstructure parameters. The diffusion microstructure parameters include the intracellular diffusion rate D int , extracellular volume fraction f ext , extracellular diffusion rate D ext and the extracellular diffusion tensor D ext .

[0079] The nerve fiber bundle is equivalent to the kernel function, and then the diffusion signal of the voxel is equal to the convolution of the kernel function and the fiber direction distribution function (fODF). Specifically, in the field of diffusion magnetic resonance, when the diffusion time is long enough, the diffusion signal can be written as the convolution of the kernel function and the fiber direction distribution function:

[0080]

[0081] in, is the fiber direction distribution function, is the kernel function, is the diffusion weighted amplitude signal, S0 is the amplitude signal without diffusion weighting, is the direction of the fiber, is the direction of the magnetic resonance pulse diffusion gradient, represents a two-dimensional sphere, and b is the diffusion weighting factor. The kernel function formula is:

[0082]

[0083] in, yes exist The projection of the direction, f s is the volume fraction of the stick interlayer, D s is the stick interlayer diffusion rate, and are the airship parallel and vertical diffusivities, f ext is the extracellular volume fraction, D ext is the extracellular diffusion rate.

[0084] The direction of the fibers is determined by The fiber direction distribution function is described in the form of decomposition on the spherical harmonic (SH) basis:

[0085]

[0086] Among them, L max is the order of spherical harmonic decomposition, when l max = 2, its unknown parameters are the same as the 3×3 symmetric tensor. m represents the degree of spherical harmonics, p lm is the spherical harmonic coefficient, Y lm is the spherical harmonic basis function.

[0087] Spherical harmonic basis is the expression of Fourier basis in spherical coordinates. The convolution in the spatial domain can be written in the form of product under the spherical harmonic basis:

[0088]

[0089] Among them, S lm 、p lm and are the coefficients of the signal, fiber direction distribution function and kernel function under the spherical harmonic basis. In order to reduce the fitting parameters, the rotation invariant is introduced:

[0090]

[0091] equation can be converted to: l=0, 2, ...

[0092] In one embodiment, the multi-layer model is used to fit the diffusion weighted amplitude image, and obtaining the diffusion microstructure parameters includes the following steps:

[0093] (a) Generate simulation labels where f s represents the volume fraction of the stick interlayer, D s represents the stick interlayer diffusion rate, and represent the parallel and vertical diffusion rates of the airship, respectively, and f ext represents the extracellular volume fraction, D ext represents the extracellular diffusion rate, p l is a rotational invariant of order l.

[0094] In which, the scalar parameter is first estimated and the rotation-independent basis p l The present invention uses a data-driven machine learning method to estimate the scalar parameter x and the rotation-independent basis p respectively. l .

[0095] (b) generating a simulated signal based on the simulated tag.

[0096] The simulation label is introduced into the convolution formula of the fiber direction distribution function and the kernel function to generate a simulation signal, thereby obtaining a data set of the simulation label and the simulation signal.

[0097] (c) Using a polynomial regression model to fit the mapping space between the simulated label and the simulated signal.

[0098] (d) Inputting the measured diffusion weighted amplitude data into the mapping space, and obtaining an estimated value of the corresponding diffusion microstructure parameter as the diffusion microstructure parameter.

[0099] The measured diffusion weighted amplitude data is input into the mapping space to obtain the estimated value of the diffusion microstructure parameter corresponding to the measured diffusion weighted amplitude data.

[0100] In one embodiment, the formula of the polynomial regression model is:

[0101]

[0102] in, is the estimated diffusion microstructure parameter, where is the noisy diffusion-weighted amplitude data, W is the order of the polynomial, are the polynomial coefficients for training. The training labels are generated by simulation, and their range is f S =[0.02,0.98],D s =[1,3]μm 2 / ms, f ext =[0,1],D ext =[0.4,3]μm 2 / ms, p2 = [0, 0.99], p4 = [0, 0.99], the total number of samples is 1e5, and the generated data label x is substituted into l = 0, 2, ..., we can obtain the simulation signal set {S l (b)}, there is a mapping space between the simulation label and the simulation signal, and then the polynomial regression model is used to fit the mapping space to obtain the polynomial regression coefficient representing the mapping space For diffusion magnetic resonance parameter estimation, a third-order polynomial regression is sufficient, as higher-order polynomials will result in overfitting. and the polynomial regression model coefficients By multiplying, we can estimate the diffusion microstructure parameters This includes the accurate extracellular volume fraction f ext and the extracellular diffusion rate D ext .

[0103] like Figure 3 As shown, compared with the linear square method in the prior art to obtain diffusion microstructure parameters, the core of this method is to ensure that the extracellular diffusion rate D of the spherical layer must be calculated during the training phase. ext Add to the simulation tag, without adding D ext The value is fixed at 3 μm 2 / ms. It should be noted that in the prior art, the stick compartment is the space inside the neurite, the airship compartment and the ball compartment are the space outside the neurite, and the diffusion rate of the ball compartment is fixed to free diffusion, so only the information of cerebrospinal fluid can be extracted. The present invention creatively proposes to add the diffusion rate of the ball compartment to the simulation label, and the value range is between non-diffusion and free diffusion, so that the information of isotropic diffusion rate in any space can be adaptively extracted, and the corresponding volume fraction can also be extracted, and the volume fraction of the ball compartment is closer to the real extracellular volume fraction. Therefore, it is necessary to redivide the compartments, and the stick compartment and the airship compartment are divided into the intracellular space, and the ball compartment is the extracellular space.

[0104] Step S5: generating a low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameters.

[0105] Specifically, according to Generate low-frequency conductivity tensor image σ L , where σ H represents the high-frequency conductivity image, f ext represents the extracellular volume fraction, D int represents the intracellular diffusion rate, D ext represents the extracellular diffusion rate; β represents the ratio of ion concentration inside and outside the cell. For the human brain, the empirical value of 0.41 is used.

[0106] The low-frequency conductivity tensor decomposition formula is as follows:

[0107]

[0108] in, is the average extracellular diffusion rate, D ext is the extracellular diffusion tensor; is the apparent extracellular ion concentration; is the low-frequency conductivity tensor within the skull.

[0109] The water diffusion tensor can be written as the following positive definite matrix:

[0110]

[0111] Among them, S D is the eigenvalue decomposition matrix, is the transpose of the eigenvalue decomposition matrix. The signal intensity of diffusion magnetic resonance can be written as By fitting the diffusion tensor DTI, the water diffusion tensor D can be obtained. Where b is the diffusion weighting factor, is the direction of the magnetic resonance pulse diffusion gradient, ρ0 is the signal amplitude without diffusion weighting, ρ Dis the diffusion-weighted signal amplitude. Assuming that the extracellular diffusion tensor and the water diffusion tensor have the same eigenvalue, then D = (1-f w )D int +f w D ext Assuming that the microstructure inside and outside the cell is the same, the extracellular diffusion tensor is in, for Normalized tensors with the same orientation.

[0112] Step S6: Generate a digital skull model based on the magnetization prepared rapid gradient echo sequence of the brain.

[0113] Specifically, firstly, the magnetic resonance image of the brain is acquired based on the magnetization prepared rapid gradient echo sequence; and then a digital skull model is generated based on the magnetic resonance image. When generating the digital skull model, it is usually necessary to segment the acquired magnetic resonance image of the brain. There are many algorithms for segmentation, such as the unified segmentation algorithm used in the software SPM12, which is segmented into multiple tissues, and then a surface is generated based on the segmented image, and finally a digital skull model is generated based on the surface.

[0114] Step S7: generating a cranial low-frequency conductivity tensor model based on the low-frequency conductivity tensor image and the digital skull model.

[0115] Specifically, the digital skull model is segmented into the skull and the skull. The conductivity tensor of the skull has been solved above. and the extracranial conductivity tensor Based on the conductivity tensor in the skull and the extracranial electrical conductivity tensor Finally, the brain conductivity tensor C including the inside and outside of the brain can be obtained.

[0116] In addition, when two or more pairs of electrodes are placed on the scalp surface and the tTIS device is used to inject current into the subject, according to the electrostatic theory, it can be obtained:

[0117]

[0118] Among them, Ω represents the entire cranial brain area, represents the boundary of the brain, ε represents the set of all injected electrodes, g(r) is the injected current, and u(r) is the electric potential.

[0119] It should be noted that currently SANDI can also estimate the accurate extracellular volume fraction, but the method has high requirements for magnetic resonance equipment and the imaging time is long. To use the SANDI model for imaging, b = 0 / 500 / 1000 / 2000 / 3000 / 4000 / 6000 is required, and the total number of directions is about 200. The method proposed in the present invention only requires b = 0 / 1000 / 2000, and the total number of directions is 34, which is nearly 6 times less data, so the imaging time can be reduced by 6 times. At present, magnetic resonance electrical impedance imaging can also reconstruct low-frequency conductivity tensors, but this method requires multiple additional external devices, including magnetic resonance-compatible transcranial electrical stimulation devices, and it is also necessary to develop a special sequence to synchronize magnetic resonance and electrical stimulation devices, which greatly increases the cost and operation time of use, and the imaging signal-to-noise ratio is low, and image reconstruction is an ill-conditioned inverse problem, so its clinical practicality is very limited.

[0120] The protection scope of the method for modeling the low-frequency conductivity tensor of the brain described in the embodiment of the present invention is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present invention are included in the protection scope of the present invention.

[0121] An embodiment of the present invention also provides a cranial and cerebral low-frequency conductivity tensor modeling system, which can implement the cranial and cerebral low-frequency conductivity tensor modeling method described in the present invention. However, the implementation device of the cranial and cerebral low-frequency conductivity tensor modeling system described in the present invention includes but is not limited to the structure of the cranial and cerebral low-frequency conductivity tensor modeling system listed in this embodiment. All structural deformations and replacements of the prior art made according to the principles of the present invention are included in the protection scope of the present invention.

[0122] like Figure 4 As shown, in one embodiment, the brain low-frequency conductivity tensor modeling system provided by the present invention includes an acquisition module 41, a first acquisition module 42, a second acquisition module 43, a third acquisition module 44, a first generation module 45, a second generation module 46 and a third generation module 47.

[0123] The acquisition module 41 is used to acquire the phase image of the fast spin echo sequence of the brain.

[0124] The first acquisition module 42 is connected to the acquisition module 41 and is used to acquire the high-frequency conductivity image of the brain based on the phase image.

[0125] The second acquisition module 43 is connected to the acquisition module 41 and is used to acquire the diffusion weighted amplitude image of the brain based on a multi-excitation diffusion weighted sequence.

[0126] The third acquisition module 44 is connected to the second acquisition module 43, and is used to acquire the diffusion microstructure parameters of the brain based on the diffusion weighted amplitude image by using a multi-layer model.

[0127] The first generating module 45 is connected to the first acquiring module 42 and the third acquiring module 44, and is used to generate a low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameters.

[0128] The second generating module 46 is used to generate a digital skull model based on the magnetization prepared rapid gradient echo sequence of the skull.

[0129] The third generating module 47 is connected to the first generating module 45 and the second generating module 46, and is used to generate a cranial low-frequency conductivity tensor model based on the low-frequency conductivity tensor image and the digital skull model.

[0130] Among them, the structures and principles of the acquisition module 41, the first acquisition module 42, the second acquisition module 43, the third acquisition module 44, the first generation module 45, the second generation module 46 and the third generation module 47 correspond one by one to the above-mentioned brain low-frequency conductivity tensor modeling method, so they will not be repeated here.

[0131] In the several embodiments provided by the present invention, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0132] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0133] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0134] The embodiment of the present invention also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for modeling the cranial low-frequency conductivity tensor of the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0135] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory.

[0136] The memory is used to store computer programs.

[0137] The memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[0138] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned cranial low-frequency conductivity tensor modeling method.

[0139] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0140] like Figure 5 As shown, the electronic device of the present invention is in the form of a general computing device. The components of the electronic device may include but are not limited to: one or more processors or processing units 51, a memory 52, and a bus 53 connecting different system components (including the memory 52 and the processing unit 51).

[0141] Bus 53 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0142] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0143] The memory 52 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 521 and / or cache memory 522. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 523 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called a "hard drive"). Although Figure 5Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 53 via one or more data medium interfaces. The memory 52 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0144] A program / utility 524 having a set (at least one) of program modules 5241 may be stored, for example, in the memory 52, such program modules 5241 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 5241 generally perform the functions and / or methods of the embodiments described herein.

[0145] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 54. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 55. Figure 5 As shown, the network adapter 55 communicates with other modules of the electronic device via the bus 53. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0146] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for modeling low-frequency conductivity tensor of cranial brain, characterized in that: The method comprises the following steps: Acquire phase images of the brain using a fast spin echo sequence; Acquire a high-frequency conductivity image of the brain based on the phase image; Acquiring a diffusion-weighted amplitude image of the brain based on a multi-excitation diffusion-weighted sequence; Based on the diffusion weighted amplitude image, a multi-layer model is used to obtain diffusion microstructure parameters of the brain; generating a low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameter; generating a digital skull model based on a magnetization-prepared rapid gradient echo sequence of the skull; A cranial low-frequency conductivity tensor model is generated based on the low-frequency conductivity tensor image and the digital skull model.

2. The method for modeling low-frequency conductivity tensor of cranial brain according to claim 1, characterized in that: Acquiring the high-frequency conductivity image of the brain based on the phase image includes: Based on the phase image, the phase convection-diffusion reaction equation is used. Acquire the high frequency conductivity image Where ρ represents the resistivity, Indicates the transmit and receive phase, represents the Hamiltonian operator, ω0 represents the precession angular frequency of the magnetic resonance; μ0 represents the magnetic permeability constant in vacuum; c represents the regularization parameter.

3. The method for modeling low-frequency conductivity tensor of cranial brain according to claim 1, characterized in that: Based on the diffusion weighted amplitude image, obtaining the diffusion microstructure parameters of the brain using a multi-layer model comprises the following steps: The diffusion weighted amplitude image is fitted by using a multi-compartment model to obtain the diffusion microstructure parameters, which include intracellular diffusion rate, intracellular volume fraction, extracellular diffusion rate and extracellular diffusion tensor.

4. The method for modeling low-frequency conductivity tensor of cranial brain according to claim 3, characterized in that: Using a multi-layer model to fit the diffusion weighted amplitude image and obtain the diffusion microstructure parameters includes the following steps: Generate Simulation Label where f s represents the volume fraction of the stick interlayer, D s represents the stick interlayer diffusion rate, and represent the parallel and vertical diffusion rates of the airship, respectively, and f ext represents the extracellular volume fraction, D ext represents the extracellular diffusion rate, p l is the l-th order rotation invariant; generating a simulation signal based on the simulation tag; Using a polynomial regression model to fit the mapping space between the simulation label and the simulation signal; The measured diffusion weighted amplitude data is input into the mapping space to obtain the estimated value of the corresponding diffusion microstructure parameter.

5. The method for modeling low-frequency conductivity tensor of cranial brain according to claim 1, characterized in that: Generating the low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameter comprises: according to Generate low-frequency conductivity tensor image σ L , where σ H represents the high-frequency conductivity image, f ext represents the extracellular volume fraction, D int represents the intracellular diffusion rate, D ext represents the extracellular diffusion rate; β represents the ratio of ion concentration inside and outside the cell.

6. The method for modeling low-frequency conductivity tensor of cranial brain according to claim 1, characterized in that: Generating a digital skull model based on the magnetization-prepared rapid gradient echo sequence of the brain comprises the following steps: Acquiring a magnetic resonance image of the brain based on a magnetization prepared rapid gradient echo sequence; A digital skull model is generated based on the magnetic resonance image.

7. The method for modeling cranial low-frequency conductivity tensor according to claim 1, characterized in that: Generating a cranial low-frequency conductivity tensor model based on the low-frequency conductivity tensor image and the digital skull model comprises the following steps: Segmenting the digital skull model into intracranial and extracranial parts; Acquiring an intra-skull conductivity tensor and an extra-skull conductivity tensor based on the low-frequency conductivity tensor image; A low-frequency conductivity tensor model of the brain is generated based on the conductivity tensor inside the skull and the conductivity tensor outside the skull.

8. A cranial and cerebral low-frequency conductivity tensor modeling system, characterized by: The system comprises a collection module, a first acquisition module, a second acquisition module, a third acquisition module, a first generation module, a second generation module and a third generation module; The acquisition module is used to obtain a phase image of a fast spin echo sequence of the brain; The first acquisition module is used to acquire a high-frequency conductivity image of the brain based on the phase image; The second acquisition module is used to acquire the diffusion-weighted amplitude image of the brain based on a multi-excitation diffusion-weighted sequence; The third acquisition module is used to acquire the diffusion microstructure parameters of the brain by using a multi-layer model based on the diffusion weighted amplitude image; The first generating module is used to generate a low-frequency conductivity tensor image of the brain based on the high-frequency conductivity image and the diffusion microstructure parameter; The second generating module is used to generate a digital skull model based on the magnetization prepared rapid gradient echo sequence of the skull; The third generating module is used to generate a cranial low-frequency conductivity tensor model based on the low-frequency conductivity tensor image and the digital skull model.

9. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for modeling the low-frequency conductivity tensor of the cranial brain described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory, so that the electronic device executes the method for modeling the cranial low-frequency conductivity tensor according to any one of claims 1 to 7.

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