Individual head tissue conductivity model construction method and system

Abstract

The invention discloses an individual head tissue conductivity model construction method and system. According to the method, a non-uniform intracranial tissue conductivity value is obtained by adopting a conductivity model construction method based on the water content, and meanwhile, the conductivity of the scalp and the skull is measured through an improved electrical impedance imaging technology, so that the defects of the conductivity model construction method based on the water content during measurement of the conductivity of the scalp and the skull are overcome. According to the method, the difference of individual tissue conductivity is fully considered, the conductivity distribution of the head tissue of each patient can be measured independently, and a more accurate simulation modeling and optimization scheme can be provided in the fields of electrical stimulation of nerve regulation, tumor electric field treatment and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical engineering, and in particular relates to a method and system for constructing an individual head tissue conductivity model. Background Art

[0002] The measurement of dielectric properties of head tissue is of great significance in the field of disease diagnosis and physical therapy. Dielectric properties mainly include conductivity and dielectric constant, which are important physical properties exhibited under electromagnetic excitation and have very important clinical application value. For example, in the field of tumor treatment, on the one hand, the conductivity of tumor tissue is different from that of other normal tissues, so the measurement of tissue electrical properties is of great help in the diagnosis of tumors; on the other hand, tumor electric field therapy (TTFields) treats tumors by applying medium-frequency electric fields to inhibit the mitosis of tumor cells. It is necessary to determine the treatment plan and ensure the treatment effect by observing the distribution of electric fields in tissues, but it is very difficult to measure the distribution of electric fields in tissues. Therefore, modeling and simulation methods are usually used for observation. The existing TTFields modeling method usually assigns values ​​to different tissues according to the anatomical structure of the model, but uses a unified electrical property value reported in the literature for all patients, and the electrical property values ​​of different regions of the same tissue are also equal. The electrical property parameters of brain tissue reported by different research institutes are affected by the measurement method, and the results are also different, which will reduce the accuracy of the analysis results and may also lead to differences and adaptability of treatment plans. In other electrical stimulation fields, such as transcranial electrical stimulation (tES) and transcranial magnetic stimulation (TMS), the prediction of the distribution of therapeutic fields in the brain mostly needs to be completed through simulation modeling. Therefore, the measurement of dielectric properties of biological tissues is of great significance in the fields of physical therapy and disease diagnosis.

[0003] At present, common methods for measuring tissue electrical properties include direct current measurement, electrical impedance tomography (EIT), and magnetic resonance electrical impedance tomography (MREIT). Direct current measurement uses the potential difference generated by the applied current to calculate the electrical properties of tissues by implanting invasive electrodes or directly applying current to the excised tissue sample. However, this method has a large application range and is difficult to be widely used in clinical practice. The MREIT method uses magnetic resonance to measure the change in magnetic flux generated by the human body after the current is applied, and inverts the conductivity distribution map of the tissue based on the change in magnetic flux. The uncertainty of its solution limits the promotion of the technology, and it is difficult to measure the electrical properties of tissues at specific frequencies. In addition, there are magnetic resonance sequences that reconstruct the dielectric properties of brain tissue based on changes in water content, but they are affected by imaging uniform fields and are difficult to use to measure the electrical properties of scalp and skull tissues. Past studies have shown that the electrical property distribution of the scalp and skull has the greatest impact on the electric field distribution of tumor electric field therapy or transcranial electromagnetic stimulation in the head. EIT technology is highly feasible in measuring the electrical properties of tissues and is easy to promote and use. However, traditional EIT measurement methods often require multiple measurement channels for relative measurement of whole-brain electrical impedance, which is relatively cumbersome to operate in practical applications. Summary of the invention

[0004] The purpose of the present invention is to solve the above problems in the prior art and provide a method and system for constructing an individual head tissue conductivity model. The present invention aims to design a targeted conductivity measurement method according to the characteristics of different head tissues, realize the whole brain head tissue conductivity detection, and make the simulation modeling and parameter optimization work in the field of electromagnetic physical therapy more accurate and effective.

[0005] The specific technical solutions adopted by the present invention are as follows:

[0006] In a first aspect, the present invention provides a method for constructing an individual head tissue conductivity model, comprising:

[0007] S1. extracting the longitudinal relaxation time signal intensity of each voxel from the head magnetic resonance scan image of the target patient, and calculating the head tissue water content distribution according to a first relationship model between the longitudinal relaxation time signal intensity and the tissue water content;

[0008] S2. Based on the water content distribution of head tissues and combined with the spatial mask of intracranial soft tissues, the tissue conductivity distribution of intracranial soft tissues under the magnetic resonance excitation frequency is calculated by using the second relationship model between tissue water content and tissue conductivity; according to the standard curve of tissue conductivity and excitation frequency, the tissue conductivity distribution of intracranial soft tissues under the magnetic resonance excitation frequency is converted into the tissue conductivity distribution of intracranial soft tissues under the target excitation frequency through mapping transformation;

[0009] S3, after the electrode matrix to be attached to the head of the target patient is adjusted to a first arrangement position in which the sensitive field distribution is concentrated in the scalp area, the conductivity distribution of the scalp area is measured by bioelectrical impedance tomography at a target excitation frequency; after the electrode matrix is ​​adjusted to a second arrangement position in which the sensitive field distribution is concentrated in the skull area, the conductivity distribution of the skull area is measured by bioelectrical impedance tomography at a target excitation frequency;

[0010] S4. Integrate the tissue conductivity distribution of the intracranial soft tissue, the conductivity distribution of the scalp region, and the conductivity distribution of the skull region into the individual head model of the target patient to obtain an individual head tissue conductivity model.

[0011] As a preferred embodiment of the first aspect, the longitudinal relaxation time signal intensity is extracted based on a head magnetic resonance scan image obtained by scanning twice at different repetition times.

[0012] As a preferred embodiment of the first aspect, the first relationship model is obtained by performing magnetic resonance scanning on a series of tissue samples with known water content and extracting the respective longitudinal relaxation time signal intensities as sample data fitting.

[0013] As a preferred embodiment of the first aspect, the second relationship model is obtained by fitting a series of tissue samples with known water contents and performing conductivity measurements as sample data.

[0014] As a preferred embodiment of the first aspect, the specific method of the mapping transformation is: searching and determining the first standard conductivity corresponding to the magnetic resonance excitation frequency and the second standard conductivity corresponding to the target excitation frequency on the standard curve of the tissue conductivity and the excitation frequency, and then for each voxel in the intracranial soft tissue, calculating the relative change amplitude between the tissue conductivity of the voxel at the magnetic resonance excitation frequency and the first standard conductivity, and then performing the same relative change amplitude on the second standard conductivity to obtain the tissue conductivity of the voxel at the target excitation frequency.

[0015] As a preferred embodiment of the first aspect above, the first arrangement position and the second arrangement position of the electrode matrix are predetermined by simulation, and the simulation method is: using the head magnetic resonance scanning image of the target patient to construct an individual head model, arranging the electrode matrix on the individual head model, and determining the sensitive field distribution of the scalp area and the skull area corresponding to the electrode matrix at different arrangement positions through simulation, and the electrode matrix arrangement position where the sensitive field distribution is concentrated in the scalp area is taken as the first arrangement position, and the electrode matrix arrangement position where the sensitive field distribution is concentrated in the skull area is taken as the second arrangement position.

[0016] As a preferred embodiment of the above-mentioned first aspect, the individual head model of the target patient is constructed using the head magnetic resonance scanning image of the target patient, and masks of the intracranial soft tissue, scalp area and skull area are obtained through tissue segmentation. Then, the tissue conductivity distribution of the intracranial soft tissue, the conductivity distribution of the scalp area and the conductivity distribution of the skull area are respectively aligned to their respective masks, and conductivity is assigned to all voxels in the head area to complete the construction of the individual head tissue conductivity model.

[0017] In a second aspect, the present invention provides a system for constructing an individual head tissue conductivity model, comprising:

[0018] A head tissue water content distribution acquisition module is used to extract the longitudinal relaxation time signal intensity of each voxel from the head magnetic resonance scan image of the target patient, and calculate the head tissue water content distribution according to a first relationship model between the longitudinal relaxation time signal intensity and the tissue water content;

[0019] An intracranial soft tissue conductivity acquisition module is used to calculate the tissue conductivity distribution of the intracranial soft tissue under the magnetic resonance excitation frequency based on the head tissue water content distribution, combined with the intracranial soft tissue spatial mask, and using the second relationship model between tissue water content and tissue conductivity; according to the standard curve of tissue conductivity and excitation frequency, the tissue conductivity distribution of the intracranial soft tissue under the magnetic resonance excitation frequency is converted into the tissue conductivity distribution of the intracranial soft tissue under the target excitation frequency through mapping transformation;

[0020] The extracranial tissue conductivity acquisition module is used to measure the conductivity distribution of the scalp area by bioelectrical impedance tomography at a target excitation frequency after the electrode matrix to be attached to the head of the target patient is adjusted to a first arrangement position that makes the sensitive field distribution concentrate on the scalp area, and to measure the conductivity distribution of the skull area by bioelectrical impedance tomography at a target excitation frequency after the electrode matrix is ​​adjusted to a second arrangement position that makes the sensitive field distribution concentrate on the skull area;

[0021] The conductivity model building module is used to integrate the tissue conductivity distribution of intracranial soft tissue, the conductivity distribution of the scalp area and the conductivity distribution of the skull area into the individual head model of the target patient to obtain an individual head tissue conductivity model.

[0022] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for constructing an individual head tissue conductivity model as described in any one of the schemes of the first aspect is implemented.

[0023] In a fourth aspect, the present invention provides a computer electronic device comprising a memory and a processor;

[0024] The memory is used to store computer programs;

[0025] The processor is used to implement the individual head tissue conductivity model construction method as described in any one of the solutions of the first aspect when executing the computer program.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] (1) The actual conductivity value distribution of head tissue is macroscopically non-uniform. Compared with the traditional method of defining the same tissue as a uniform conductivity value, the present invention adopts a conductivity model construction method based on water content to obtain non-uniform tissue conductivity values. At the same time, the present invention measures the conductivity of the scalp and skull through the improved EIT technology, which solves the defects of the conductivity model construction method based on water content in measuring the conductivity of the scalp and skull.

[0028] (2) The method for constructing an individual head tissue conductivity model proposed in the present invention takes into account the differences in individual tissue conductivity and measures the tissue conductivity of each patient individually, which can provide more accurate simulation modeling and optimization solutions in the fields of neural regulation electrical stimulation and tumor electric field therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Schematic diagram of the steps in the method for building an individual head tissue conductivity model;

[0030] Figure 2 Schematic diagram of the electrode matrix and excitation system used in measuring conductivity distribution using EIT technology;

[0031] Figure 3 It is a schematic diagram of the change of sensitive field distribution during the adjustment of the excitation electrode distance;

[0032] Figure 4 To obtain process images of tissue conductivity distribution;

[0033] Figure 5 is a standard curve diagram of tissue conductivity-excitation frequency of white matter provided in an embodiment of the present invention;

[0034] Figure 6 is a schematic diagram of the results of the individualized conductivity model established in an embodiment of the present invention;

[0035] Figure 7 Schematic diagram of the modular composition of the system for building an individual head tissue conductivity model;

[0036] Figure 8 It is a schematic diagram of the structure of computer electronic equipment. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0038] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for the purpose of distinction and description, and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0039] like Figure 1 As shown, in a preferred embodiment of the present invention, a method for constructing an individual head tissue conductivity model is provided, which includes:

[0040] S1. Extracting the longitudinal relaxation time signal intensity of each voxel from the head magnetic resonance scan image of the target patient, and calculating the head tissue water content distribution according to a first relationship model between the longitudinal relaxation time signal intensity and the tissue water content.

[0041] It should be noted that the target patient's head magnetic resonance scan image is a signal image obtained by scanning the target patient's head through a magnetic resonance imaging device. In actual implementation, a specific scanning sequence, scanning parameters and radio frequency coil can be selected to perform a magnetic resonance scan on the patient's head and extract scanning imaging data. The corresponding scanning sequence, scanning parameters and radio frequency coil values ​​of the head tissue belong to the prior art and can be searched and obtained by those skilled in the art, and there is no restriction on this.

[0042] In addition, the longitudinal relaxation time signal intensity, i.e., T1 signal intensity, of the present invention can be extracted based on the head magnetic resonance scan image obtained by scanning at two different repetition times (Repetition Time, TR). This method belongs to the prior art. For ease of understanding, the principle is briefly introduced below. Assuming that the repetition times of the two scans are respectively recorded as RT1 and RT2, the magnetic resonance imaging signal equations of the two scans are:

[0043]

[0044]

[0045] where ρ is the proton density, κ is the scaling factor, θ is the flip angle, T1 and T2 are the longitudinal and transverse relaxation times, respectively, TE is the echo time, and S(RT1) and S(RT2) are the MRI signal intensity values ​​under the two scans corresponding to RT1 and RT2, respectively.

[0046] The magnetic resonance imaging signal intensity values ​​obtained by two magnetic resonance scans are compared with the above two equations, and the value of the longitudinal relaxation time T1 can be obtained after obtaining the ratio:

[0047]

[0048] The signal strength values ​​S(RT1) and S(RT2) of the two scans, the repetition times RT1 and RT2, and the flip angle θ are all known parameters.

[0049] It should be noted that the first relationship model reflects the relationship between T1 signal intensity and tissue water content. The first relationship model can be obtained by performing magnetic resonance scanning on a series of tissue samples with known water content and extracting their respective T1 signal intensities as sample data fitting. Specifically, by scanning a large number of tissue samples with known water content (biomimetic tissues such as hydrogels can be used) and measuring their T1 signal intensity values, a series of [ Based on these sample data, the T1 signal intensity and tissue water content W can be fitted. c The first relationship model between them, the specific first relationship model is expressed by the formula:

[0050]

[0051] Among them, u and v represent the coefficients obtained by fitting, and their values ​​are related to the intensity of the main magnetic field of the magnetic resonance.

[0052] Since the magnetic resonance imaging signal of the scanned tissue contains the longitudinal relaxation time (T1) information of the tissue (in nuclear magnetic resonance, relaxation refers to the phenomenon that when the atomic nucleus is in a high-energy state and resonates, it quickly recovers to the original low-energy state after the radio frequency pulse stops. The recovery process is called the relaxation process, and the longitudinal relaxation time is the longitudinal recovery time), the first relationship model indicates that in magnetic resonance imaging, the inverse of the longitudinal relaxation time 1 / T1 is related to the tissue water content (W c ) is linearly related to the inverse of .

[0053] After the first relationship model is fitted, since the T1 signal intensity of each voxel has been extracted from the head magnetic resonance scan image of the target patient, the T1 signal intensity of each voxel can be converted to the tissue water content W corresponding to the voxel through the first relationship model. c , the tissue water content W of all voxels c This constitutes the water content distribution of the head tissue.

[0054] S2. Based on the water content distribution of head tissue and combined with the spatial mask of intracranial soft tissue, the second relationship model between tissue water content and tissue conductivity is used to calculate the tissue conductivity distribution of intracranial soft tissue under the magnetic resonance excitation frequency; according to the standard curve of tissue conductivity and excitation frequency, the tissue conductivity distribution of intracranial soft tissue under the magnetic resonance excitation frequency is converted into the tissue conductivity distribution of intracranial soft tissue under the target excitation frequency through mapping transformation.

[0055] It should be noted that the intracranial soft tissue includes cerebrospinal fluid, gray matter and white matter, tumors and other soft tissues surrounded by the skull, while the skull and scalp can be regarded as extracranial tissue. The conductivity characteristics of intracranial soft tissue and extracranial tissue are different, and the present invention needs to be measured separately. The head tissue water content distribution obtained in S1 includes intracranial soft tissue and extracranial tissue, so it is necessary to identify the intracranial soft tissue area through the intracranial soft tissue spatial mask, and the S2 step only needs to obtain the tissue conductivity distribution of the intracranial soft tissue. The intracranial soft tissue spatial mask can be obtained by performing tissue segmentation on the individual head model of the target patient.

[0056] Similarly, the second relationship model between tissue water content and tissue conductivity can also be obtained by measuring the conductivity of a series of tissue samples with known water content and fitting them as sample data. Of course, in addition to obtaining data through experimental measurement, the present invention can also construct sample data by collecting prior knowledge data of different tissue water contents and corresponding conductivity reported in existing literature. The sample data is analyzed using statistical methods to determine the relationship between water content and conductivity, and an appropriate mathematical function is selected based on the analysis results to map the second relationship model between water content and conductivity, and finally the fitting parameter value in the second relationship model is determined using a data fitting method.

[0057] In an embodiment of the present invention, the specific functional form of the second relationship model between water content and conductivity can be set to:

[0058]

[0059] in, , , is the fitting parameter.

[0060] Therefore, based on the water content distribution of head tissue and combined with the spatial mask of intracranial soft tissue, the water content distribution of the intracranial soft tissue area is extracted, and then the tissue conductivity of each voxel in the intracranial soft tissue area is calculated using the fitted second relationship model. The tissue conductivity of all voxels in the intracranial soft tissue area can constitute the tissue conductivity distribution of the intracranial soft tissue.

[0061] It should be noted that the mapping relationship between the conductivity parameters of biological tissues and the water content of tissues is related to the excitation frequency, and the above-mentioned magnetic resonance imaging is obtained at a specific magnetic resonance excitation frequency. Therefore, the tissue conductivity distribution of intracranial soft tissues corresponds to the magnetic resonance excitation frequency. However, the application scenarios of individual head tissue conductivity models are often used for simulation modeling in fields such as electrical stimulation and tumor electric field therapy. The requirements for excitation frequency in these scenarios often deviate greatly from the magnetic resonance excitation frequency. Therefore, it is necessary to convert the tissue conductivity distribution of intracranial soft tissues at the magnetic resonance excitation frequency to the excitation frequency required for practical applications.

[0062] The target excitation frequency that actually needs to be converted in the present invention is determined according to the application scenario of the individual head tissue conductivity model. For example, in one embodiment, if the individual head tissue conductivity model is applied to the TTFields field, since the frequency of the electric field applied by TTFields is in the medium frequency range (100 kHz ~ 500 kHz), the target excitation frequency needs to be set to the medium frequency range (100 kHz ~ 500 kHz); in another embodiment, if the individual head tissue conductivity model is applied to the tES field, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), the frequency of the electric field applied by tES is usually in the low frequency range of DC ~ 200 Hz, so the target excitation frequency needs to be set to the DC ~ 200 Hz range. The target excitation frequency in the present invention can be a frequency value or a range consisting of a series of frequency values. The present invention does not limit the specific target excitation frequency value.

[0063] In the present invention, the tissue conductivity distribution of the intracranial soft tissue under the magnetic resonance excitation frequency is mapped and transformed, and converted to the target excitation frequency, which needs to rely on the standard curve of tissue conductivity and excitation frequency. There is a certain fixed relationship between tissue conductivity and excitation frequency, and this relationship can be displayed by a fitting curve. In the present invention, the conductivity-frequency fitting curve is called a standard curve. The standard curve of tissue conductivity and excitation frequency can be determined based on experiments, or it can be determined by prior knowledge such as literature reports. There are a large number of such studies in the prior art. In an embodiment of the present invention, a standard curve of conductivity changing with frequency can be established by summarizing and analyzing the existing literature data on the measurement of each tissue conductivity value at different frequencies.

[0064] In the present invention, the specific method of mapping and transforming the tissue conductivity distribution under different excitation frequencies using the standard curve is:

[0065] Assume that the magnetic resonance excitation frequency is , the target excitation frequency is , find and determine the magnetic resonance excitation frequency on the standard curve of tissue conductivity and excitation frequency Corresponding first standard conductivity and target excitation frequency The corresponding second standard conductivity Then, for each voxel v in the intracranial soft tissue, the voxel v is excited at the magnetic resonance excitation frequency Tissue conductivity under Calculate the relative change with the first standard conductivity , then the second standard conductivity Perform the same relative change , and obtain the tissue conductivity of the voxel at the target excitation frequency Therefore, the tissue conductivity of all voxels in the intracranial soft tissue is The tissue conductivity distribution of the intracranial soft tissue under the target excitation frequency can be constituted.

[0066] S3. After the electrode matrix to be pasted on the target patient's head is adjusted to a first arrangement position in which the sensitive field distribution is concentrated in the scalp area, the conductivity distribution of the scalp area is measured by bioelectrical impedance tomography (EIT) technology at a target excitation frequency. After the electrode matrix is ​​adjusted to a second arrangement position in which the sensitive field distribution is concentrated in the skull area, the conductivity distribution of the skull area is measured by bioelectrical impedance tomography technology at the target excitation frequency.

[0067] It should be noted that the electrode matrix attached to the head of the target patient in the present invention refers to the electrode matrix used in bioelectrical impedance tomography technology, which is composed of at least two pairs of excitation electrodes and measurement electrodes. EIT technology belongs to the prior art, so the specific method of measuring the conductivity distribution using EIT technology can also be achieved by referring to the methods in the prior art. The excitation electrodes and measurement electrodes used in EIT can be ceramic electrodes, and conductive paste is coated between the electrode sheet and the scalp surface.

[0068] In the embodiment of the present invention, the electrode matrix used in the EIT technology to measure the conductivity distribution has a total of 4 electrodes, including two sets of paired excitation electrodes and measurement electrodes, and its structure is as follows: Figure 2 shown.

[0069] However, the particularity of measuring the conductivity distribution using the EIT technology in the present invention is that it is necessary to distinguish the scalp area and the skull area, and measure the conductivity distribution of each of the two areas respectively. The present invention achieves this measurement requirement through a two-step method, that is, first adjusting the position of the electrode matrix to the first arrangement position, so that the sensitive field distribution (i.e., the spatial distribution of the voltage generated by the excitation current) is concentrated in the scalp area, thereby measuring the conductivity distribution of the scalp area by the EIT technology at the target excitation frequency; after measuring the conductivity distribution of the scalp area, the position of the electrode matrix is ​​adjusted to the second arrangement position, so that the sensitive field distribution is concentrated as much as possible in the skull area. At this time, since the electrodes are directly attached to the scalp, there will still be some sensitive field distribution in the scalp area, but since the conductivity distribution of the scalp area has been measured in advance, the inversion of the conductivity distribution of the skull area can still be achieved by the EIT technology at the target excitation frequency.

[0070] It can be seen that the first arrangement position and the second arrangement position of the electrode matrix are the key to realize the two-step measurement. In an embodiment of the present invention, the first arrangement position and the second arrangement position of the electrode matrix can be predetermined by simulation, and the simulation method is:

[0071] An individual head model is constructed using the head magnetic resonance scanning image of the target patient, and a simulation model of the above-mentioned electrode matrix is ​​arranged on the individual head model. All possible arrangement positions of the electrode matrix are simulated by finite element simulation to determine the sensitive field distribution of the scalp area and the skull area corresponding to the electrode matrix at different arrangement positions, and then the electrode matrix arrangement position where the sensitive field distribution is concentrated in the scalp area is taken as the first arrangement position, and the electrode matrix arrangement position where the sensitive field distribution is concentrated in the skull area is taken as the second arrangement position.

[0072] The key difference between the first arrangement position and the second arrangement position is that Figure 2The distance between the four electrodes in the middle. First, use EIT to measure the scalp conductivity. At this time, it is necessary to minimize the influence of the skull on the scalp conductivity. Figure 3 As shown in the left figure, the excitation electrodes are first set to be close to each other. The sensitive field distribution is obtained for the EIT measurement setting with a close excitation electrode distance, so that the excitation current mostly passes through the scalp and the sensitivity of the target scalp area is improved as much as possible. Then, a suitable excitation electrode spacing is selected to make the sensitive field distribution concentrated in the scalp area and the skull area as small as possible. This is used as the first arrangement position for subsequent scalp conductivity measurement. Then gradually increase the distance between the excitation electrodes, such as Figure 3 As shown in the right figure in the figure, the sensitive field distribution under different excitation electrode spacing is further obtained to increase the sensitivity of the skull area as much as possible. The appropriate excitation electrode spacing is selected as the second layout position, and combined with the scalp conductivity determined in the first step Skull conductivity Thus, by adjusting the excitation electrodes from far to near according to the first arrangement position and the second arrangement position, the distribution ratio of the excitation current in the scalp and skull can be adjusted to accurately obtain the individual local scalp and skull conductivity.

[0073] It should be noted that the criterion for judging whether the sensitive field distribution is concentrated can be determined based on the sensitivity coefficient, and the calculation formula is as follows:

[0074]

[0075] Where: is the sensitivity coefficient of the i-th pair of electrodes for excitation and the j-th pair of electrodes for measurement, , is the i-th pair of electrodes (excitation current is ) and the jth pair of electrodes (excitation current is ) Field potential distribution during excitation.

[0076] The positions of the two pairs of electrodes were adjusted continuously in the finite element simulation software to find the corresponding scalp area. The electrode placement positions that exceed the threshold (which can be set to 0.75) are used as the first placement positions to find the corresponding skull area. The electrode arrangement position exceeding the threshold (which can be set to 0.75) is used as the second arrangement position. When actually measuring the conductivity, the electrode matrix can be arranged according to the arrangement position determined by the simulation, the excitation current can be applied to the excitation electrode in a swept frequency manner, and the voltage distribution data can be collected by measuring the electrode, and then the conductivity distribution of the individual's local scalp and skull area can be obtained according to the EIT principle. In an embodiment of the present invention, a reverse solution model can be established through the distribution of sensitive fields in the scalp area and the skull area to perform iterative optimization until convergence, thereby obtaining the conductivity spatial distribution of the scalp and skull. Specifically, the iterative optimization goal of solving the conductivity spatial distribution of the two regions is to minimize the error between the measured voltage and the model predicted voltage. Based on this goal, the conductivity distribution is continuously adjusted until the error converges, and the final conductivity distribution is the conductivity spatial distribution in the region.

[0077] S4. Integrate the tissue conductivity distribution of the intracranial soft tissue, the conductivity distribution of the scalp region, and the conductivity distribution of the skull region into the individual head model of the target patient to obtain an individual head tissue conductivity model.

[0078] In an embodiment of the present invention, an individual head model of a target patient is constructed using a magnetic resonance imaging scan image of the target patient's head, and masks of the intracranial soft tissue, scalp region, and skull region are obtained through tissue segmentation. Then, the tissue conductivity distribution of the intracranial soft tissue, the conductivity distribution of the scalp region, and the conductivity distribution of the skull region are respectively registered to their respective masks, thereby assigning conductivity to all voxels in the head region, and completing the construction of an individual head tissue conductivity model.

[0079] In the present invention, the head tissue segmentation is performed on the scanned patient magnetic resonance structural image MRI, which can be achieved through segmentation models such as U-Net. Masks of tissues such as scalp, skull, cerebrospinal fluid, gray matter and white matter, tumor, etc. can be obtained through segmentation. The conductivity value of each tissue at each spatial position is corresponded to the position of each voxel of the segmented tissue mask, and an individual head tissue conductivity model can be obtained. Figure 4 The figure shows the various intermediate images and tissue conductivity distribution map in the process of obtaining tissue conductivity distribution. Figure 5 The standard curve between white matter (WM) tissue conductivity and excitation frequency used in the calculation process is shown as an example. Figure 6 The result schematic diagram of the individualized conductivity model finally established is further exemplified.

[0080] The individual head tissue conductivity model finally constructed is subsequently operated according to the needs of downstream applications. In an embodiment of the present invention, if the individual head tissue conductivity model is used for transcranial electrical stimulation electric field modeling and simulation, the subsequent processing steps are to place electrodes on the scalp surface, gridding, and finally perform finite element calculation and electric field simulation. The method for constructing the individual head tissue conductivity model fully considers the differences in individual tissue conductivity, and can construct tissue conductivity that conforms to the actual situation of each patient individually, thereby realizing the construction of a high-precision individual head tissue electric field simulation model.

[0081] It should also be noted that the individual head tissue conductivity model construction method described in S1 to S4 of the above embodiments can essentially be executed by a computer program or module.

[0082] Therefore, based on the same inventive concept, Figure 7 As shown, another preferred embodiment of the present invention also provides a system for constructing an individual head tissue conductivity model, which includes:

[0083] A head tissue water content distribution acquisition module is used to extract the longitudinal relaxation time signal intensity of each voxel from the head magnetic resonance scan image of the target patient, and calculate the head tissue water content distribution according to a first relationship model between the longitudinal relaxation time signal intensity and the tissue water content;

[0084] An intracranial soft tissue conductivity acquisition module is used to calculate the tissue conductivity distribution of the intracranial soft tissue under the magnetic resonance excitation frequency based on the head tissue water content distribution, combined with the intracranial soft tissue spatial mask, and using the second relationship model between tissue water content and tissue conductivity; according to the standard curve of tissue conductivity and excitation frequency, the tissue conductivity distribution of the intracranial soft tissue under the magnetic resonance excitation frequency is converted into the tissue conductivity distribution of the intracranial soft tissue under the target excitation frequency through mapping transformation;

[0085] The extracranial tissue conductivity acquisition module is used to measure the conductivity distribution of the scalp area by bioelectrical impedance tomography at a target excitation frequency after the electrode matrix to be attached to the head of the target patient is adjusted to a first arrangement position that makes the sensitive field distribution concentrate on the scalp area, and to measure the conductivity distribution of the skull area by bioelectrical impedance tomography at a target excitation frequency after the electrode matrix is ​​adjusted to a second arrangement position that makes the sensitive field distribution concentrate on the skull area;

[0086] The conductivity model building module is used to integrate the tissue conductivity distribution of intracranial soft tissue, the conductivity distribution of the scalp area and the conductivity distribution of the skull area into the individual head model of the target patient to obtain an individual head tissue conductivity model.

[0087] Similarly, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer program product corresponding to the individual head tissue conductivity model construction method provided in the above embodiment, including a computer program / instructions. When the computer program / instructions are executed by a processor, the individual head tissue conductivity model construction method in the above embodiment can be implemented.

[0088] Similarly, based on the same inventive concept, Figure 8 As shown, another preferred embodiment of the present invention also provides a computer electronic device corresponding to the method for constructing an individual head tissue conductivity model provided in the above embodiment, which includes a memory and a processor;

[0089] The memory is used to store computer programs;

[0090] The processor is used to implement the individual head tissue conductivity model construction method in the above embodiment when executing the computer program.

[0091] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0092] Therefore, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer-readable storage medium corresponding to the method for constructing an individual head tissue conductivity model provided in the above embodiment, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the method for constructing an individual head tissue conductivity model in the above embodiment can be implemented.

[0093] It is understandable that the above storage medium may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. The storage medium may also be a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc., which can store program codes.

[0094] It is understandable that the above-mentioned 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, discrete hardware components.

[0095] It should also be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the various embodiments provided in this application, the division of steps or modules in the system and method is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or steps can be combined or integrated together, and a module or step can also be split.

[0096] The above-described embodiments are only some preferred implementations of the present invention, but are not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for constructing an individual head tissue conductivity model, characterized in that: include: S1. extracting the longitudinal relaxation time signal intensity of each voxel from the head magnetic resonance scan image of the target patient, and calculating the head tissue water content distribution according to a first relationship model between the longitudinal relaxation time signal intensity and the tissue water content; S2. Based on the water content distribution of head tissues and combined with the spatial mask of intracranial soft tissues, the tissue conductivity distribution of intracranial soft tissues under the magnetic resonance excitation frequency is calculated by using the second relationship model between tissue water content and tissue conductivity; according to the standard curve of tissue conductivity and excitation frequency, the tissue conductivity distribution of intracranial soft tissues under the magnetic resonance excitation frequency is converted into the tissue conductivity distribution of intracranial soft tissues under the target excitation frequency through mapping transformation; S3, after the electrode matrix to be attached to the head of the target patient is adjusted to a first arrangement position in which the sensitive field distribution is concentrated in the scalp area, the conductivity distribution of the scalp area is measured by bioelectrical impedance tomography at a target excitation frequency; after the electrode matrix is ​​adjusted to a second arrangement position in which the sensitive field distribution is concentrated in the skull area, the conductivity distribution of the skull area is measured by bioelectrical impedance tomography at a target excitation frequency; S4. Integrate the tissue conductivity distribution of the intracranial soft tissue, the conductivity distribution of the scalp region, and the conductivity distribution of the skull region into the individual head model of the target patient to obtain an individual head tissue conductivity model.

2. The method for constructing an individual head tissue conductivity model according to claim 1, characterized in that: The longitudinal relaxation time signal intensity is extracted based on the head magnetic resonance scan images obtained by scanning twice at different repetition times.

3. The method for constructing an individual head tissue conductivity model according to claim 1, characterized in that: The first relationship model is obtained by performing magnetic resonance scanning on a series of tissue samples with known water content and extracting the longitudinal relaxation time signal intensity of each sample as sample data fitting.

4. The method for constructing an individual head tissue conductivity model according to claim 1, characterized in that: The second relationship model is obtained by fitting a series of tissue samples with known water contents and measuring their electrical conductivity as sample data.

5. The method for constructing an individual head tissue conductivity model according to claim 1, characterized in that: The specific method of the mapping transformation is: searching and determining the first standard conductivity corresponding to the magnetic resonance excitation frequency and the second standard conductivity corresponding to the target excitation frequency on the standard curve of the tissue conductivity and the excitation frequency, and then for each voxel in the intracranial soft tissue, calculating the relative change amplitude between the tissue conductivity of the voxel at the magnetic resonance excitation frequency and the first standard conductivity, and then performing the same relative change amplitude on the second standard conductivity to obtain the tissue conductivity of the voxel at the target excitation frequency.

6. The method for constructing an individual head tissue conductivity model according to claim 1, characterized in that: The first arrangement position and the second arrangement position of the electrode matrix are predetermined by simulation, and the simulation method is: using the head magnetic resonance scanning image of the target patient to construct an individual head model, arranging the electrode matrix on the individual head model, determining the sensitive field distribution of the scalp area and the skull area corresponding to the electrode matrix at different arrangement positions by simulation, and taking the arrangement position of the electrode matrix where the sensitive field distribution is concentrated in the scalp area as the first arrangement position, and taking the arrangement position of the electrode matrix where the sensitive field distribution is concentrated in the skull area as the second arrangement position.

7. The method for constructing an individual head tissue conductivity model according to claim 1, characterized in that: The individual head model of the target patient is constructed using the head magnetic resonance scan image of the target patient, and masks of the intracranial soft tissue, scalp area and skull area are obtained through tissue segmentation. Then, the tissue conductivity distribution of the intracranial soft tissue, the conductivity distribution of the scalp area and the conductivity distribution of the skull area are respectively aligned to their respective masks, and conductivity is assigned to all voxels in the head area to complete the construction of the individual head tissue conductivity model.

8. A system for constructing an individual head tissue conductivity model, characterized in that: include: A head tissue water content distribution acquisition module is used to extract the longitudinal relaxation time signal intensity of each voxel from the head magnetic resonance scan image of the target patient, and calculate the head tissue water content distribution according to a first relationship model between the longitudinal relaxation time signal intensity and the tissue water content; An intracranial soft tissue conductivity acquisition module is used to calculate the tissue conductivity distribution of the intracranial soft tissue under the magnetic resonance excitation frequency based on the head tissue water content distribution, combined with the intracranial soft tissue spatial mask, and using the second relationship model between tissue water content and tissue conductivity; according to the standard curve of tissue conductivity and excitation frequency, the tissue conductivity distribution of the intracranial soft tissue under the magnetic resonance excitation frequency is converted into the tissue conductivity distribution of the intracranial soft tissue under the target excitation frequency through mapping transformation; The extracranial tissue conductivity acquisition module is used to measure the conductivity distribution of the scalp area by bioelectrical impedance tomography at a target excitation frequency after the electrode matrix to be attached to the head of the target patient is adjusted to a first arrangement position that makes the sensitive field distribution concentrate on the scalp area, and to measure the conductivity distribution of the skull area by bioelectrical impedance tomography at a target excitation frequency after the electrode matrix is ​​adjusted to a second arrangement position that makes the sensitive field distribution concentrate on the skull area; The conductivity model building module is used to integrate the tissue conductivity distribution of intracranial soft tissue, the conductivity distribution of the scalp area and the conductivity distribution of the skull area into the individual head model of the target patient to obtain an individual head tissue conductivity model.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for constructing an individual head tissue conductivity model as described in any one of claims 1 to 7 is implemented.

10. A computer electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the individual head tissue conductivity model construction method as described in any one of claims 1 to 7 when executing the computer program.

Images