Skull-brain multi-frequency conductivity tensor modeling method, system, medium and electronic device

Abstract

The application provides a kind of brain multi-frequency conductivity tensor modeling method, system, medium, electronic equipment, the method comprises the following steps: based on the high-frequency conductivity of brain of brain's ultra-short echo sequence generation;Based on the diffusion microstructure parameter of brain of multi-shot diffusion weighted sequence acquisition;Based on the low-frequency conductivity tensor of the high-frequency conductivity and the diffusion microstructure parameter generation brain;Based on the high-frequency conductivity and the low-frequency conductivity tensor generation multi-frequency conductivity tensor;Based on the digital head model of magnetization preparation fast gradient echo sequence generation head;Based on the multi-frequency conductivity tensor and the digital head model generation brain frequency conductivity tensor model.The brain multi-frequency conductivity tensor modeling method, system, medium, electronic equipment of the application can accurately construct individualized brain multi-frequency conductivity tensor model, to provide support for tumor treatment field target planning and dose evaluation.

Description

Technical Field

[0001] This invention relates to the technical field of biomedical engineering, and in particular to a method, system, medium, and electronic device for modeling multi-frequency conductivity tensors of the brain. Background Technology

[0002] According to the theory of bioelectricity, when the frequency is direct current, the cell membrane is completely insulated, and the current can only flow outside the cell, such as... Figure 1 As shown, when the frequency gradually increases from DC to the range of several hundred MHz, the cell membrane first undergoes polarization (around several hundred Hz). At this point, although the current flows only in the extracellular fluid, the conductivity increases slightly. As the frequency gradually increases (reaching tens of kHz), the most crucial change occurs in the cell membrane. The cell membrane is not a conductor; it acts more like a capacitor, with two conductors (intracellular fluid and extracellular fluid) separated by an insulator (lipid bilayer). The conductivity of alternating current by capacitance is called capacitance, which is proportional to frequency. As the frequency increases, capacitance increases, and the conductivity improves. Increased capacitance means that current can more easily "bypass" the insulating conductivity of the cell membrane and "penetrate" the cell membrane through its capacitive properties. Specifically, the alternating electric field causes the cell membrane to continuously charge and discharge, allowing ions to move back and forth in the direction of the applied electric field without actually passing through the insulating cell membrane. This is equivalent to opening a new, more unobstructed path for the current. When the frequency reaches tens of MHz, the insulating properties of the cell membrane disappear, and the current can flow freely throughout the biological tissue.

[0003] bioconductivity Frequency conductivity is an important biophysical parameter of biological tissues, and it is a frequency-dependent quantity. High-frequency conductivity plays an important role in estimating the specific absorption ratio (SAR), while low-frequency conductivity plays a very important role in neural modulation. Tumor-Treating Fields (TTFields) are excited at a frequency of 200 kHz, which falls within the range of mid-frequency conductivity.

[0004] Tumor therapeutic fields have been established as an effective adjuvant therapy for newly diagnosed glioblastoma patients. In glioblastoma treatment, this method injects alternating current (typically 200 kHz) into the brain of a shaved-head patient via two pairs of electrode arrays distributed anteriorly, posteriorly, and laterally. The placement of the tumor therapeutic field electrodes depends on the patient's individual 200 kHz intracranial conductivity, and the optimization of treatment efficacy and dosage assessment require the participation of the patient's individualized conductivity throughout the process. After surgical removal of the glioblastoma tumor, the brain does not return to "normal" but is a dynamic and fragile environment, often accompanied by cerebral edema, surgical cavities, necrotic tissue, hemorrhage, and hematoma. The 200 kHz conductivity in these areas varies considerably in the literature, making it difficult to assign empirical values. Therefore, developing an individualized multi-frequency conductivity tensor modeling method for tumor therapeutic electric fields has significant clinical and social value.

[0005] Traditional methods for individualized conductivity modeling of tumor treatment fields typically employ direct mapping and water mapping methods. The direct mapping method assumes a linear relationship between diffusion and conductivity tensor eigenvalues. ,in It is the first voxel Each conductivity, It is the voxel number The modeling method directly links water diffusivity to conductivity, neglecting complex postoperative ion concentration changes and failing to consider conductivity dispersion, resulting in significant modeling errors. The water mapping method, however, uses two Magnetic Resonance Imaging (MRI) structural images with different repetition times to map brain tissue water content using a formula, then calculates conductivity at 128 MHz (the precession frequency of 3T MRI is 128 MHz) using an empirical formula. While this method can obtain individualized conductivity, it has a large error for the following reasons: conductivity is determined by ion concentration and mobility, while the mapped water content is only proportional to mobility. This is because tissues with high water content, such as cerebrospinal fluid, have high diffusivity, and mobility is proportional to diffusivity, but water content cannot reflect the differences in ion concentration among different tissues.

[0006] Conductivity tensor imaging (CTI) is a method for reconstructing the anisotropic low-frequency conductivity tensor of biological tissues. Its basic principle is as follows: High-frequency conductivity at the Larmor frequency (128 MHz) is reconstructed using magnetic resonance imaging. Then, using the bioelectrical properties theory mentioned above, the low-frequency conductivity is decomposed based on the intracellular and extracellular volume fractions and diffusion rate. However, current CTI can only obtain conductivity at two frequencies (Lammor frequency and DC frequency), and cannot calculate mid-frequency conductivity images around 200 kHz. Summary of the Invention

[0007] In view of the above-mentioned problems, the purpose of this invention is to provide a method, system, medium, and electronic device for modeling multi-frequency conductivity tensors of the brain, which can accurately construct individualized multi-frequency conductivity tensor models of the brain, thereby providing support for target planning and dose assessment in tumor treatment fields.

[0008] In a first aspect, the present invention provides a method for modeling the multi-frequency conductivity tensor of the cranium, the method comprising the following steps: generating high-frequency conductivity of the cranium based on an ultrashort echo sequence of the cranium; obtaining diffusion microstructure parameters of the cranium based on a multi-excitation diffusion weighted sequence; generating a low-frequency conductivity tensor of the cranium based on the high-frequency conductivity and the diffusion microstructure parameters; generating a multi-frequency conductivity tensor based on the high-frequency conductivity and the low-frequency conductivity tensor; generating a digital head model based on a fast gradient echo sequence of magnetization preparation of the cranium; and generating a mid-frequency conductivity tensor model of the cranium based on the multi-frequency conductivity tensor and the digital head model.

[0009] In one implementation of the first aspect, generating the high-frequency conductivity of the cranium based on an ultrashort echo sequence of the cranium includes the following steps:

[0010] Phases of the brain were acquired based on ultrashort echo sequences;

[0011] The high-frequency conductivity of the brain is obtained based on the phase; wherein a phase-based convection-diffusion reaction equation is used. Obtain the high-frequency conductivity ,in Represents resistivity. Indicates the transmit and receive phase. Represents the Hamiltonian operator. Indicates the precession angular frequency of magnetic resonance; Represents the magnetic permeability constant in vacuum; This represents the regularization parameter.

[0012] In one implementation of the first aspect, obtaining diffusion microstructure parameters of the brain based on multi-excitation diffusion-weighted sequences includes the following steps:

[0013] The diffusion-weighted amplitude of the brain is acquired based on a multi-excitation diffusion-weighted sequence.

[0014] A multi-layer model was used to fit the diffusion-weighted amplitude to obtain the diffusion microstructure parameters of the brain; the diffusion microstructure parameters include intracellular diffusion rate, extracellular volume fraction, extracellular diffusion rate, and extracellular diffusion tensor.

[0015] In one implementation of the first aspect, generating the low-frequency conductivity tensor of the brain based on the high-frequency conductivity and the diffused microstructure parameters includes:

[0016] according to Generating low-frequency conductivity tensors ,in, Indicates high-frequency conductivity. Indicates the extracellular volume fraction. Indicates intracellular diffusion rate. Indicates extracellular diffusion rate, Represents the extracellular diffusion tensor; This indicates the ratio of ion concentrations inside and outside the cell.

[0017] In one implementation of the first aspect, generating a multi-frequency conductivity tensor based on a low-frequency conductivity tensor includes the following steps:

[0018] Construct the corrected conductivity Among them, the correction factor , , Indicates DC conductivity. Indicates high-frequency conductivity. , Represents the distribution constant. Indicates the relaxation time. Represents angular frequency. Represents frequency Time function The value;

[0019] The multi-frequency conductivity tensor is generated based on the corrected conductivity and the low-frequency conductivity tensor. ,in Indicates angular frequency as Tensor correction factor at time, Indicates the tensor decomposition factor. Indicates the extracellular volume fraction. Indicates intracellular diffusion rate. Indicates extracellular diffusion rate. Represents the extracellular diffusion tensor; This indicates the ratio of ion concentrations inside and outside the cell.

[0020] In one implementation of the first aspect, generating a digital head model based on a fast gradient echo sequence prepared by magnetization of the cranium includes the following steps:

[0021] The brain was acquired using a fast gradient echo sequence based on magnetization preparation;

[0022] A digital head model is generated based on the magnetic resonance image.

[0023] In one implementation of the first aspect, generating a brain mid-frequency conductivity tensor model from the multi-frequency conductivity tensor and the digital skull model includes the following steps:

[0024] The intermediate frequency conductivity tensor is obtained based on the multi-frequency conductivity tensor;

[0025] Generate the cranial text corresponding to the finite element mesh coordinates and the mid-frequency conductivity tensor;

[0026] A brain mid-frequency conductivity tensor model is generated based on the brain text and the digital head model.

[0027] Secondly, the present invention provides a brain multi-frequency conductivity tensor modeling system, the system comprising a first generation module, an acquisition module, a second generation module, a third generation module, a fourth generation module, and a fifth generation module;

[0028] The first generation module is used to generate the high-frequency conductivity of the cranium based on the ultrashort echo sequence of the cranium;

[0029] The acquisition module is used to acquire diffusion microstructure parameters of the brain based on multi-excitation diffusion-weighted sequences.

[0030] The second generation module is used to generate a low-frequency conductivity tensor of the brain based on the high-frequency conductivity and the diffusion microstructure parameters;

[0031] The third generation module is used to generate a multi-frequency conductivity tensor based on the low-frequency conductivity tensor.

[0032] The fourth generation module is used to generate a digital head model based on the magnetization preparation of the cranium using a fast gradient echo sequence.

[0033] The fifth generation module is used to generate a brain mid-frequency conductivity tensor model based on the multi-frequency conductivity tensor and the digital skull model.

[0034] Thirdly, the present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for modeling the multi-frequency conductivity tensor of the brain.

[0035] Fourthly, the present invention provides an electronic device, comprising: a processor and a memory;

[0036] The memory is used to store computer programs;

[0037] The processor is used to execute the computer program stored in the memory, so that the electronic device performs the above-described method for modeling the multi-frequency conductivity tensor of the brain.

[0038] As described above, the brain multi-frequency conductivity tensor modeling method, system, medium, and electronic device of the present invention have the following beneficial effects:

[0039] (1) Combining the Cole-Cole model of complex conductivity and magnetic resonance conductivity tensor imaging to realize multi-frequency conductivity tensor imaging of the brain, and obtaining the brain conductivity tensor model in the DC to high frequency range, thereby providing TTFields with an individualized mid-frequency brain conductivity tensor model;

[0040] (2) It can assist in the placement of TTFields electrodes and the calculation of treatment dose, truly allowing the conductivity of TTFields in the treatment phase to transition from an assumption to a measurement phase;

[0041] (3) Existing commercial sequences can collect all the information required for imaging without the need for separate sequence design, which effectively reduces modeling complexity and cost. Attached Figure Description

[0042] Figure 1 The diagram shows the flow of low-frequency and high-frequency currents in a living organism in one embodiment.

[0043] Figure 2 The flowchart shown is an embodiment of the brain multi-frequency conductivity tensor modeling method of the present invention;

[0044] Figure 3 The diagram shows an architecture schematic of the brain multi-frequency conductivity tensor modeling method of the present invention in one embodiment;

[0045] Figure 4 The diagram shown is a structural schematic of the brain multi-frequency conductivity tensor modeling system of the present invention in one embodiment.

[0046] Figure 5 The diagram shown is a structural schematic of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0047] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0048] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0049] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0050] like Figure 2 and Figure 3 As shown, in one embodiment, the brain multi-frequency conductivity tensor modeling method of the present invention includes steps S1-S6.

[0051] Step S1: Generate the high-frequency conductivity of the brain based on the ultrashort echo sequence of the brain.

[0052] Specifically, generating the high-frequency conductivity of the brain based on ultrashort echo sequences of the brain includes the following steps:

[0053] 11) The phase of the brain is acquired based on an ultrashort echo sequence.

[0054] Ultra-short echo time (UTE) sequences are a type of magnetic resonance imaging (MRI) sequence characterized by extremely short echo times (TE), primarily used to visualize short T2* tissues that are difficult to image with conventional sequences. In this invention, the phase of the brain is acquired based on the ultra-short echo sequence to obtain the corresponding phase image.

[0055] 12) Obtain the high-frequency conductivity of the brain based on the phase; wherein a phase-based convection-diffusion reaction equation is used. Obtain the high-frequency conductivity ,in Represents resistivity. Indicates the transmit and receive phase. Represents the Hamiltonian operator. Indicates the precession angular frequency of magnetic resonance; Represents the magnetic permeability constant in vacuum; This represents the regularization parameter.

[0056] According to Maxwell's equations, we can obtain:

[0057]

[0058] Where E(r) is the electric field, J(r) is the current density, H(r) is the magnetic field strength, and i is the imaginary unit. Let be the permeability in free space, and κ be the nodal property. ω is the magnetic resonance angular frequency, and ∽ is the Hamiltonian operator. Current density By conduction current (Generated by the movement of moving charge carriers) and displacement current (Formed by the polarization of non-moving charges). For Taking the curl operator, we get:

[0059]

[0060] because We can obtain:

[0061]

[0062] According to magnetic induction intensity You can get it Equation:

[0063]

[0064] Because the k-space data measured by magnetic resonance is not It is not the B1 court, so it needs to be... Convert to B1 field:

[0065]

[0066] Skipping the tedious derivation, we can use the formula... The phase-based convection-diffusion MREPT equations are obtained:

[0067]

[0068] in, It is resistivity (the reciprocal of conductivity). It refers to the transmit and receive phase, which is the phase of the magnetic resonance image. (Complex number) Amplitude and phase ,plural Amplitude and phase ,in It refers to the transmit / receive phase. Transmit / receive phase The coefficients of the convection-diffusion equation are derived from the phase. The equation is obtained. The solution is the high-frequency conductivity. The reciprocal of. Among them Here, is the Hamiltonian operator, representing the gradient of the space; Indicates the precession angular frequency of magnetic resonance; This represents the magnetic permeability constant in vacuum. (Due to the formula...) The equations in the equations contain many zero elements at the diagonal positions, causing oscillations in the solutions. Therefore, diffusion terms are usually added to form phase convection-diffusion reaction equations. Solving the phase convection-diffusion reaction equation yields the high-frequency conductivity. The reciprocal of . That is, the high-frequency conductivity. ,in This represents the regularization parameter.

[0069] According to the divergence theorem , can be converted The finite difference equation for this equation is as follows:

[0070] Finally, it can be organized into In the form of. Among them, It is an N*M*L×N*M*L sparse matrix. yes The vector.

[0071] Step S2: Obtain diffusion microstructure parameters of the brain based on multi-excitation diffusion weighted sequences.

[0072] Specifically, obtaining diffusion microstructure parameters of the brain based on multi-excitation diffusion-weighted sequences includes the following steps:

[0073] 21) The diffusion-weighted amplitude of the brain is acquired based on a multi-excitation diffusion-weighted sequence.

[0074] Among them, the Readout Segmentation of Long Variable Echo-trains (RESOLVE) is a multi-excitation diffusion-weighted sequence that obtains the corresponding diffusion-weighted amplitude by diffusion-weighting water molecules inside the brain.

[0075] 22) The diffusion weighted amplitude is fitted using a multi-layer model to obtain the diffusion microstructure parameters of the brain; the diffusion microstructure parameters include intracellular diffusion rate, extracellular volume fraction, extracellular diffusion rate and extracellular diffusion tensor.

[0076] The multi-layer model is a set of mathematical formulas based on the characteristics of diffusion. Diffusion can be represented by stick, airship, and sphere models. The stick model indicates that water molecules can only diffuse along the direction of the stick, exhibiting high anisotropy. The airship model indicates that diffusion can occur in all directions, exhibiting low anisotropy. The sphere model indicates that diffusion can occur in all directions, exhibiting isotropy. The volume fraction weights of the stick, airship, and sphere models form the kernel function of the multi-layer model. When fitting data using the multi-layer model, this set of mathematical formulas is used to approximate the data infinitely. When the minimum error is reached, the parameters of the mathematical formulas, namely the volume fraction and diffusivity, can be obtained.

[0077] In this invention, a multi-layer model is used to fit the diffusion-weighted amplitude to obtain the diffusion microstructure parameters. These diffusion microstructure parameters include intracellular diffusion rate. extracellular volume fraction extracellular diffusion rate and extracellular diffusion tensor .

[0078] The nerve fiber bundle is equivalent to a kernel function, and the diffuse signal of a voxel is equal to the convolution of the kernel function and the fiber orientation distribution function (fODF). Specifically, in the field of diffusion magnetic resonance imaging, when the diffusion time is long enough, the diffuse signal can be written as the convolution of the kernel function and the fiber orientation distribution function:

[0079]

[0080] in, The fiber orientation distribution function, It's a kernel function. It is a diffusion-weighted amplitude signal. It is an amplitude signal without diffusion weighting. The direction of the fiber, The direction of the magnetic resonance pulse diffusion gradient. Represents a two-dimensional sphere. This is the diffusion weighting factor. The kernel function formula is:

[0081]

[0082] in, yes exist Projection of direction It is the extracellular volume fraction. It is the extracellular diffusion coefficient. and The two components are the parallel and vertical diffusion coefficients of the airship model. It is the volume fraction of the stick model. It is the diffusion coefficient of the stick model.

[0083] Spherical harmonics are the representation of Fourier bases in spherical coordinates. Convolution in the spatial domain under spherical harmonics can be written as a product:

[0084]

[0085] in, , and These are the coefficients of the signal, fiber orientation distribution function, and kernel function, respectively, under spherical harmonics. To reduce the number of fitting parameters, rotational invariants are introduced:

[0086]

[0087] equation It can be converted to: .

[0088] First, estimate the scalar parameters. and rotational independent basis .

[0089] Estimating scalar parameters using data-driven machine learning methods. and rotational independent basis Multinomial regression is used, and its formula is: .in, These are the estimated microstructure parameters, here they are... , The data is noisy. It is the order of the polynomial. These are the polynomial coefficients used in training. The training dataset is generated using simulation, and its range is... =[0.02, 0.98], =[0.2,2.5] , =[0.05, 1.3]× , =[0.05, 1.2]× , =[0, 1], =[1.1, 3] , =[0, 0.99], =[0, 0.99]. Where, for Weighting function The larger the value, The smaller the value, the total number of samples is 2e5. Using the method above, the outer cell volume fraction in space can be adaptively estimated. and outer cell diffusion coefficient .

[0090] The water diffusion tensor can be written as a positive definite matrix as follows:

[0091]

[0092] in, For the eigenvalue decomposition matrix, This is the transpose of the eigenvalue decomposition matrix. The signal intensity of diffuse magnetic resonance can be written as... The water diffusion tensor can be obtained by fitting the diffusion tensor DTI. .in, For diffusion weighting factor, The direction of the magnetic resonance pulse diffusion gradient. The amplitude of the signal without diffusion weighting. This represents the diffusion-weighted signal amplitude. Assuming the outer cell diffusion tensor and the water diffusion tensor have the same eigenvalues, then...

[0093]

[0094] in, , .

[0095] Step S3: Generate the low-frequency conductivity tensor of the brain based on the high-frequency conductivity and the diffusion microstructure parameters.

[0096] Specifically, low-frequency conductivity scalar ,in, As a decomposition factor, For high-frequency conductivity, It is the extracellular volume fraction. Intracellular diffusion rate, Extracellular diffusion rate, This is the ratio of intracellular to extracellular ion concentrations. For the human brain, an empirical value of 0.41 is used.

[0097] Based on the low-frequency conductivity scalar, the low-frequency conductivity tensor can then be reconstructed. Generating low-frequency conductivity tensors Tensor decomposition factor , Indicates high-frequency conductivity. Indicates the extracellular volume fraction. Indicates intracellular diffusion rate. Indicates extracellular diffusion rate. Represents the extracellular diffusion tensor; This indicates the ratio of ion concentrations inside and outside the cell.

[0098] Step S4: Generate a multi-frequency conductivity tensor based on the low-frequency conductivity tensor.

[0099] Specifically, generating a multi-frequency conductivity tensor based on a low-frequency conductivity tensor includes the following steps:

[0100] 41) Construct the corrected conductivity Among them, the correction factor , , This represents the DC conductivity, the value of which is obtained from the decomposition formula. , Indicates the scalar decomposition factor. Indicates high-frequency conductivity. , Represents the distribution constant. Indicates the relaxation time. Represents angular frequency. Representation function The value of .

[0101] The Cole-Cole formula for complex conductivity is expressed as follows: ,in, It is an angular frequency of Complex conductivity at time , It is frequency, measured in Hz. It is DC conductivity. It is the high-frequency limiting conductivity. It is the relaxation time. It is the distribution constant.

[0102] make and ,use to replace First, according to Euler's formula Therefore, we can conclude that:

[0103]

[0104] Elevate the above formula to The power of 1 yields:

[0105]

[0106] According to Euler's formula We can obtain:

[0107]

[0108] make ,but .in, .

[0109] According to the formula By rationalizing the denominator (multiplying both the numerator and denominator by their conjugate complex number), we get:

[0110]

[0111] in .

[0112] Expand the denominator of the above formula and then use... We can obtain:

[0113]

[0114] Therefore, it can be obtained .

[0115] Therefore, conductivity is the real part of complex conductivity. .

[0116] because ,and Therefore .Will Transform it into a hyperbolic function. Observe the denominator. .make ,but The denominator becomes .

[0117] Substitute into the formula We can obtain:

[0118]

[0119] make ,when hour, ,when . formula The requirement is that the frequency tends to The conductivity at certain frequencies is measured, but currently, magnetic resonance imaging (MRI) can only measure conductivity at Larmor frequencies. For example, a 3T MRI corresponds to a frequency of approximately 128 MHz. Approximate substitution This will cause discontinuities around 128 MHz. To ensure the consistency of the formula itself, this invention introduces a correction factor. Its function is to interpolate the conductivity at the Larmor frequency to the conductivity at infinity. Therefore, we can conclude that:

[0120]

[0121] Substitute the correction factor into the formula , can be obtained .

[0122] 42) Generate the multi-frequency conductivity tensor based on the corrected conductivity and the low-frequency conductivity tensor. ,in Indicates angular frequency as Tensor correction factor at time, Indicates the tensor decomposition factor. Indicates the extracellular volume fraction. Indicates intracellular diffusion rate. Indicates extracellular diffusion rate. Represents the extracellular diffusion tensor; This indicates the ratio of ion concentrations inside and outside the cell.

[0123] Wherein, the corrected conductivity Substitute the low-frequency conductivity scalar The following are the multi-frequency conductivity scalar values:

[0124]

[0125] in, As a decomposition factor, The frequency relaxation curve is shown. , , For frequency Correction factor at that time.

[0126] Similarly, the corrected conductivity... Substitute the low-frequency conductivity tensor The multi-frequency conductivity tensor is obtained as follows:

[0127]

[0128] in, For frequency Tensor correction factor at time.

[0129] Step S5: Generate a digital head model based on the rapid gradient echo sequence prepared by the magnetization of the cranium.

[0130] Specifically, firstly, magnetic resonance images of the brain are acquired based on the magnetization preparation fast gradient echo sequence; then, a three-dimensional brain entity is generated based on the magnetic resonance images; finally, a digital head model with finite element mesh is generated based on the three-dimensional brain entity.

[0131] Step S6: The multi-frequency conductivity tensor and the digital skull model generate a brain mid-frequency conductivity tensor model.

[0132] Specifically, firstly, a mid-frequency conductivity tensor is obtained based on the multi-frequency conductivity tensor; then, the cranial text corresponding to the finite element mesh coordinates and the mid-frequency conductivity tensor is generated; finally, a cranial mid-frequency conductivity tensor model is generated based on the cranial text and the digital skull model. Additionally, the cranial electric field distribution is calculated based on the cranial mid-frequency conductivity tensor model and electrode locations. The partial differential equation used to calculate the cranial electric field is:

[0133]

[0134] in, It is electric potential. It is the intermediate frequency conductivity tensor. The normal vector representing the boundary. Represents the entire cranial region. Represents the boundaries of the skull, This represents the set of all injected electrodes. It is the injected current.

[0135] The scope of protection of the brain multi-frequency conductivity tensor modeling method described in this embodiment is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principle of this invention is included within the scope of protection of this invention.

[0136] This invention also provides a cranial multi-frequency conductivity tensor modeling system. The cranial multi-frequency conductivity tensor modeling system can implement the cranial multi-frequency conductivity tensor modeling method described in this invention. However, the implementation device of the cranial multi-frequency conductivity tensor modeling system described in this invention includes, but is not limited to, the structure of the cranial multi-frequency conductivity tensor modeling system listed in this embodiment. All structural modifications and substitutions of the prior art made according to the principles of this invention are included within the protection scope of this invention.

[0137] like Figure 4 As shown, in one embodiment, the brain multi-frequency conductivity tensor modeling system of the present invention includes a first generation module 41, an acquisition module 42, a second generation module 43, a third generation module 44, a fourth generation module 45, and a fifth generation module 46.

[0138] The first generation module 41 is used to generate the high-frequency conductivity of the cranium based on the ultrashort echo sequence of the cranium.

[0139] The acquisition module 42 is used to acquire the diffusion microstructure parameters of the brain based on the multi-excitation diffusion weighted sequence.

[0140] The second generation module 43 is connected to the first generation module 41 and the acquisition module 42, and is used to generate the low-frequency conductivity tensor of the brain based on the high-frequency conductivity and the diffusion microstructure parameters.

[0141] The third generation module 44 is connected to the second generation module 43 and is used to generate a multi-frequency conductivity tensor based on the low-frequency conductivity tensor.

[0142] The fourth generation module 45 is used to generate a digital head model based on the rapid gradient echo sequence prepared by the magnetization of the cranium.

[0143] The fifth generation module 46 is connected to the third generation module 44 and the fourth generation module 45, and is used to generate a brain mid-frequency conductivity tensor model based on the multi-frequency conductivity tensor and the digital skull model.

[0144] The structure and principle of the first generation module 41, the acquisition module 42, the second generation module 43, the third generation module 44, the fourth generation module 45 and the fifth generation module 46 correspond one-to-one with the above-mentioned brain multi-frequency conductivity tensor modeling method, so they will not be described in detail here.

[0145] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0146] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of the present invention, depending on actual needs. 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.

[0147] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0148] This invention also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the above-described method for modeling the multi-frequency conductivity tensor of the brain can be executed by a program instructing a processor. This program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0149] This invention also provides an electronic device. The electronic device includes a processor and a memory.

[0150] The memory is used to store computer programs.

[0151] The memory includes various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0152] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device performs the above-described method for modeling the multi-frequency conductivity tensor of the brain.

[0153] 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 gate or transistor logic devices, or discrete hardware components.

[0154] like Figure 5 As shown, the electronic device of the present invention is embodied in the form of a general-purpose 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).

[0155] Bus 53 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0156] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0157] 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, storage system 523 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5As not 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, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 53 via one or more data media interfaces. Memory 52 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0158] A program / utility 524 having a set (at least one) of program modules 5241 may be stored, for example, in memory 52. ​​Such program modules 5241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 5241 typically perform the functions and / or methods described in the embodiments of the present invention.

[0159] The electronic device can also communicate with one or more external devices (e.g., keyboard, pointing device, display, 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 interface card, modem, etc.). This communication can be performed through input / output (I / O) interface 54. Furthermore, the electronic device can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 55. Figure 5 As shown, network adapter 55 communicates with other modules of the electronic device via 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.

[0160] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can 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 those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for modeling multi-frequency conductivity tensors of the brain, characterized in that: The method includes the following steps: High-frequency conductivity of the brain is generated based on ultrashort echo sequences of the brain. Diffusion microstructure parameters of the brain were obtained based on multi-excitation diffusion-weighted sequences; The low-frequency conductivity tensor of the brain is generated based on the high-frequency conductivity and the diffusion microstructure parameters. A multi-frequency conductivity tensor is generated based on the high-frequency conductivity and the low-frequency conductivity tensor. Digital head model generated by fast gradient echo sequence based on cranial magnetization preparation; A mid-frequency conductivity tensor model of the brain is generated based on the multi-frequency conductivity tensor and the digital skull model. Generating the low-frequency conductivity tensor of the brain based on the high-frequency conductivity and the diffused microstructure parameters includes: according to Generating low-frequency conductivity tensors ,in, Indicates high-frequency conductivity. Indicates the extracellular volume fraction. Indicates intracellular diffusion rate. Indicates extracellular diffusion rate. Represents the extracellular diffusion tensor; Indicates the ratio of intracellular to extracellular ion concentrations; Generating a multi-frequency conductivity tensor based on a low-frequency conductivity tensor includes the following steps: Construct the corrected conductivity Among them, the correction factor , , Indicates DC conductivity. Indicates high-frequency conductivity. , Represents the distribution constant. Indicates the relaxation time. Represents angular frequency. Represents frequency Time function The value; The multi-frequency conductivity tensor is generated based on the corrected conductivity and the low-frequency conductivity tensor. ,in Indicates angular frequency as Tensor correction factor at time, Indicates the tensor decomposition factor. Indicates the extracellular volume fraction. Indicates intracellular diffusion rate. Indicates extracellular diffusion rate. Represents the extracellular diffusion tensor; This indicates the ratio of ion concentrations inside and outside the cell.

2. The method for modeling the multi-frequency conductivity tensor of the brain according to claim 1, characterized in that: Generating high-frequency electrical conductivity of the brain based on ultrashort echo sequences involves the following steps: Phases of the brain were acquired based on ultrashort echo sequences; The high-frequency conductivity of the brain is obtained based on the phase; wherein a phase-based convection-diffusion reaction equation is used. Obtain the high-frequency conductivity ,in Represents resistivity. Indicates the transmit and receive phase. Represents the Hamiltonian operator. Indicates the precession angular frequency of magnetic resonance; Represents the magnetic permeability constant in vacuum; This represents the regularization parameter.

3. The method for modeling the multi-frequency conductivity tensor of the brain according to claim 1, characterized in that: Obtaining diffusion microstructure parameters of the brain based on multi-excitation diffusion-weighted sequences includes the following steps: The diffusion-weighted amplitude of the brain was acquired based on a multi-excitation diffusion-weighted sequence; A multi-layer model was used to fit the diffusion-weighted amplitude to obtain the diffusion microstructure parameters of the brain; the diffusion microstructure parameters include intracellular diffusion rate, extracellular volume fraction, extracellular diffusion rate, and extracellular diffusion tensor.

4. The method for modeling the multi-frequency conductivity tensor of the brain according to claim 1, characterized in that: The process of generating a digital head model based on the magnetization preparation of the cranium using a fast gradient echo sequence includes the following steps: The brain was acquired using a fast gradient echo sequence based on magnetization preparation; A three-dimensional brain entity is generated based on the magnetic resonance images; A digital skull model based on the finite element mesh generated from the three-dimensional skull entity.

5. The method for modeling the multi-frequency conductivity tensor of the brain according to claim 4, characterized in that: The generation of the intracranial mid-frequency conductivity tensor model using the multi-frequency conductivity tensor and the digital skull model includes the following steps: The intermediate frequency conductivity tensor is obtained based on the multi-frequency conductivity tensor; Generate the cranial text corresponding to the finite element mesh coordinates and the mid-frequency conductivity tensor; A brain mid-frequency conductivity tensor model is generated based on the brain text and the digital head model.

6. A system for modeling multi-frequency electrical conductivity tensors of the brain, characterized in that: The system includes a first generation module, an acquisition module, a second generation module, a third generation module, a fourth generation module, and a fifth generation module; The first generation module is used to generate the high-frequency conductivity of the cranium based on the ultrashort echo sequence of the cranium; The acquisition module is used to acquire diffusion microstructure parameters of the brain based on multi-excitation diffusion weighted sequences; The second generation module is used to generate a low-frequency conductivity tensor of the brain based on the high-frequency conductivity and the diffusion microstructure parameters; The third generation module is used to generate a multi-frequency conductivity tensor based on the low-frequency conductivity tensor. The fourth generation module is used to generate a digital head model based on the magnetization preparation of the cranium using a fast gradient echo sequence. The fifth generation module is used to generate a brain mid-frequency conductivity tensor model based on the multi-frequency conductivity tensor and the digital skull model; Generating the low-frequency conductivity tensor of the brain based on the high-frequency conductivity and the diffused microstructure parameters includes: according to Generating low-frequency conductivity tensors ,in, Indicates high-frequency conductivity. Indicates the extracellular volume fraction. Indicates intracellular diffusion rate. Indicates extracellular diffusion rate. Represents the extracellular diffusion tensor; Indicates the ratio of intracellular to extracellular ion concentrations; Generating a multi-frequency conductivity tensor based on a low-frequency conductivity tensor includes the following steps: Construct the corrected conductivity Among them, the correction factor , , Indicates DC conductivity. Indicates high-frequency conductivity. , Represents the distribution constant. Indicates the relaxation time. Represents angular frequency. Represents frequency Time function The value; The multi-frequency conductivity tensor is generated based on the corrected conductivity and the low-frequency conductivity tensor. ,in Indicates angular frequency as Tensor correction factor at time, Indicates the tensor decomposition factor. Indicates the extracellular volume fraction. Indicates intracellular diffusion rate. Indicates extracellular diffusion rate, Represents the extracellular diffusion tensor; This indicates the ratio of intracellular to extracellular ion concentrations.

7. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the brain multi-frequency conductivity tensor modeling method as described in any one of claims 1 to 5.

8. 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 to cause the electronic device to perform the brain multi-frequency conductivity tensor modeling method according to any one of claims 1 to 5.

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