Method and System for Constructing Electrical Conductivity Model of Individual Head Tissue

Abstract

The present invention discloses a method and system for constructing an individual head tissue conductivity model. The present invention uses a conductivity model construction method based on water content to obtain non-uniform intracranial tissue conductivity values, and at the same time measures the conductivity of the scalp and skull through an improved electrical impedance tomography technology, solving the defect of the conductivity model construction method based on water content in measuring the conductivity of the scalp and skull. The present invention fully considers the differences in individual tissue conductivity, can separately measure the head tissue conductivity distribution of each patient, and can provide more accurate simulation modeling and optimization solutions in the fields of electrical stimulation for neuromodulation and tumor electric field therapy, etc.

Description

Technical Field

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

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

[0003] Currently, common measurement methods for tissue electrical characteristic parameters include direct current measurement, Electrical Impedance Tomography (EIT), and Magnetic Resonance Electrical Impedance Tomography (MREIT). Direct current measurement applies current by implanting invasive electrodes or directly on excised tissue samples, and calculates tissue electrical characteristics using the potential difference generated by the applied current. However, the application scope of this method is highly restricted and it is difficult to be widely used clinically. The MREIT method measures the change in magnetic flux generated by the human body after applying current through magnetic resonance, and inversely calculates the conductivity distribution map of tissues based on the change value of magnetic flux. The uncertainty in its solution restricts the popularization of the technology, and it is difficult to measure the electrical characteristics of tissues at specific frequencies. Additionally, there are also magnetic resonance sequences that reconstruct the dielectric properties of brain tissues based on changes in water content. However, affected by imaging field uniformity, etc., it is difficult to be used to measure the electrical characteristic values of scalp and skull tissues. Past research has shown that the electrical characteristic distribution of the scalp and skull has the greatest impact on the electric field distribution in the head during tumor electric field therapy or transcranial electromagnetic stimulation. The EIT technology is highly feasible in measuring tissue electrical properties and is easy to promote and use. However, traditional EIT measurement methods often require multiple measurement channels for relative measurement of the whole-brain electrical impedance, which is rather cumbersome 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, achieve the detection of the conductivity of head tissues in the whole brain, 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 the first aspect, the present invention provides a method for constructing an individual head tissue conductivity model, which includes:

[0007] S1. 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 the first relationship model between the longitudinal relaxation time signal intensity and the tissue water content;

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

[0009] S3. After the electrode matrix to be pasted on the head of the target patient is adjusted to the first arrangement position where the sensitive field distribution is concentrated in the scalp area, measure the scalp area conductivity distribution by electrical impedance tomography at the target excitation frequency. After the electrode matrix is adjusted to the second arrangement position where the sensitive field distribution is concentrated in the skull area, measure the skull area conductivity distribution by electrical impedance tomography at the target excitation frequency;

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

[0011] As a preference of the first aspect above, the longitudinal relaxation time signal intensity is extracted based on the head magnetic resonance scan images obtained by scanning at two different repetition times.

[0012] As a preference of the first aspect above, the first relationship model is obtained by fitting the sample data after performing magnetic resonance scans on a series of tissue samples with known water contents and extracting their respective longitudinal relaxation time signal intensities.

[0013] As a preference of the first aspect above, the second relationship model is obtained by fitting the sample data after measuring the conductivities of a series of tissue samples with known water contents.

[0014] As a preference of the first aspect above, the specific method of the mapping transformation is: find and determine 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. Then, for each voxel in the intracranial soft tissue, calculate the relative change amplitude between the tissue conductivity of this voxel at the magnetic resonance excitation frequency and the first standard conductivity, and then perform the same relative change amplitude on the second standard conductivity to obtain the tissue conductivity of this voxel at the target excitation frequency.

[0015] Preferably, as for the first aspect above, the first arrangement position and the second arrangement position of the electrode matrix are pre-determined by simulation. The simulation method is as follows: an individual head model is constructed using the head magnetic resonance scan image of the target patient, the electrode matrix is arranged on the individual head model, and the sensitive field distributions of the corresponding scalp region and skull region under different arrangement positions of the electrode matrix are determined by simulation. The arrangement position of the electrode matrix where the sensitive field distribution is concentrated in the scalp region is used as the first arrangement position, and the arrangement position of the electrode matrix where the sensitive field distribution is concentrated in the skull region is used as the second arrangement position.

[0016] Preferably, as for the first aspect above, the individual head model of the target patient is constructed using the head magnetic resonance scan image of the target patient. The masks of the intracranial soft tissue, scalp region and skull region are obtained through tissue segmentation, and then the tissue conductivity distributions of the intracranial soft tissue, scalp region conductivity distribution and skull region conductivity distribution are respectively registered onto their respective masks, and the conductivity is assigned to all voxels in the head region 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, which includes:

[0018] A head tissue water content distribution acquisition module, configured 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 the first relationship model between the longitudinal relaxation time signal intensity and the tissue water content;

[0019] An intracranial soft tissue conductivity acquisition module, configured to calculate the tissue conductivity distribution of the intracranial soft tissue at the magnetic resonance excitation frequency based on the head tissue water content distribution and in combination with the intracranial soft tissue space mask using the second relationship model between the tissue water content and the tissue conductivity; and convert the tissue conductivity distribution of the intracranial soft tissue at the magnetic resonance excitation frequency into the tissue conductivity distribution of the intracranial soft tissue at the target excitation frequency through mapping transformation according to the standard curve of the tissue conductivity and the excitation frequency;

[0020] An extracranial tissue conductivity acquisition module, configured to measure the scalp region conductivity distribution by electrical impedance tomography at the target excitation frequency after adjusting the electrode matrix to be pasted on the head of the target patient to the first arrangement position where the sensitive field distribution is concentrated in the scalp region, and measure the skull region conductivity distribution by electrical impedance tomography at the target excitation frequency after adjusting the electrode matrix to the second arrangement position where the sensitive field distribution is concentrated in the skull region;

[0021] A conductivity model construction module, configured to integrate the tissue conductivity distribution of intracranial soft tissues, the conductivity distribution of the scalp region, and the conductivity distribution of the skull region into an individual head model of a target patient to obtain an individual head tissue conductivity model.

[0022] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for constructing an individual head tissue conductivity model according to any one of the above first aspect solutions is implemented.

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

[0024] The memory is used to store a computer program;

[0025] The processor is configured to implement the method for constructing an individual head tissue conductivity model according to any one of the above first aspect solutions when executing the computer program.

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

[0027] (1) The actual conductivity numerical distribution of head tissues shows macroscopic inhomogeneity. Compared with the traditional method of defining the same tissue as a unified conductivity value, the conductivity model construction method based on water content adopted by the present invention can obtain non-uniform tissue conductivity values. At the same time, the present invention measures the conductivity of the scalp and skull through an improved EIT technology, solving the defect of the conductivity model construction method based on water content when measuring the conductivity of the scalp and skull.

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

[0029] Figure 1 It is a schematic diagram of the steps of the method for constructing an individual head tissue conductivity model;

[0030] Figure 2 It is a schematic diagram of the electrode matrix and excitation system used when the EIT technology measures the conductivity distribution;

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

[0032] Figure 4 It is a process image for obtaining the tissue conductivity distribution;

[0033] Figure 5 is the standard curve graph of tissue conductivity - excitation frequency of white matter provided in the embodiments of the present invention;

[0034] Figure 6 is the schematic diagram of the result of the individualized conductivity model established in the embodiments of the present invention;

[0035] Figure 7 is the schematic diagram of the module composition of the individual head tissue conductivity model construction system;

[0036] Figure 8 is the schematic diagram of the structure of the computer electronic device. Detailed implementation manners

[0037] To make the above - mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the accompanying drawings. Many specific details are set forth in the following description to fully understand 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 departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined correspondingly without conflict.

[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 distinguishing descriptions, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of these features.

[0039] As Figure 1 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. 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 the first relationship model between the longitudinal relaxation time signal intensity and the tissue water content.

[0041] It should be noted that the head magnetic resonance scan image of the target patient is a signal image obtained by scanning the head of the target patient with a magnetic resonance imaging device. In actual implementation, a specific scan sequence, scan parameters, and radio frequency coil can be selected to perform magnetic resonance scanning on the patient's head and extract the scan imaging data. The corresponding scan sequence, scan 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 are not limited herein.

[0042] In addition, the above-mentioned longitudinal relaxation time signal intensity, i.e., the T1 signal intensity, of the present invention can be extracted based on head magnetic resonance scan images 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. Assume that the repetition times of the two scans are denoted as RT1 and RT2 respectively, then the magnetic resonance imaging signal equations for 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 magnetic resonance imaging signal intensity values corresponding to the two scans at RT1 and RT2 respectively.

[0046] By the magnetic resonance imaging signal intensity values obtained from the two magnetic resonance scans, the above two equations are compared. After obtaining the ratio, the value of the longitudinal relaxation time T1 can be calculated:

[0047]

[0048] where the signal intensity 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 above first relationship model reflects the relationship between the T1 signal intensity and the tissue water content This first relationship model can be obtained by performing magnetic resonance scans on a series of tissue samples with known water contents and extracting their respective T1 signal intensities as sample data for fitting. Specifically, by scanning a large number of tissue samples with known water contents (biomimetic tissues such as hydrogels can be used) and measuring their T1 signal intensity values, a series of - form sample data can be constructed. Based on these sample data, the first relationship model between the T1 signal intensity and the tissue water content W c can be fitted. The specific first relationship model is expressed by the formula:

[0050]

[0051] where 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 information of the longitudinal relaxation time (T1) of the tissue (in nuclear magnetic resonance, relaxation refers to the phenomenon that when the atomic nucleus is in a high-energy state and resonance occurs, it quickly returns to the original low-energy state after the radio frequency pulse stops, and the recovery process is called the relaxation process, and the longitudinal relaxation time is the time of longitudinal recovery), the above first relationship model represents that in magnetic resonance imaging, the reciprocal 1 / T1 of the longitudinal relaxation time is linearly related to the reciprocal of the tissue water content (W c ).

[0053] After the first relationship model is obtained by fitting, 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 , and the tissue water content W of all voxels c can form the head tissue water content distribution.

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

[0055] It should be noted that the intracranial soft tissue includes soft tissues such as cerebrospinal fluid, gray matter, white matter, and tumors surrounded by the skull, and the skull and scalp can be regarded as extracranial tissues. The conductivity characteristics of intracranial soft tissues and extracranial tissues are different, and they need to be measured separately in the present invention. The head tissue water content distribution obtained in S1 includes intracranial soft tissues and extracranial tissues. Therefore, an intracranial soft tissue spatial mask is needed to identify the intracranial soft tissue region, and only the tissue conductivity distribution of the intracranial soft tissue needs to be obtained in step S2. The intracranial soft tissue spatial mask can be obtained by tissue segmentation of the individual head model of the target patient.

[0056] Similarly, the above second relationship model between the tissue water content and the tissue conductivity can also be obtained by fitting as sample data after measuring the conductivity of a series of tissue samples with known water contents. Of course, in addition to obtaining data through experimental measurement, the present invention can also collect prior knowledge data of different tissue water contents and corresponding conductivities reported in existing literature to construct sample data. Use statistical methods to analyze the sample data to determine the relationship form between the water content and the conductivity, and select an appropriate mathematical function according to the analysis results to map the second relationship model between the water content and the conductivity, and finally use the data fitting method to determine the fitting parameter values in the second relationship model.

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

[0058]

[0059] Wherein, , , are fitting parameters.

[0060] Thus, based on the water content distribution of the head tissue, combined with the intracranial soft tissue space mask, the water content distribution of the intracranial soft tissue region is extracted. Then, using the second relationship model obtained by fitting, the tissue conductivity of each voxel in the intracranial soft tissue region is calculated, and the tissue conductivity distribution of the intracranial soft tissue can be formed by the tissue conductivities of all voxels in the intracranial soft tissue region.

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

[0062] In the present invention, the target excitation frequency that actually needs to be converted 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 electric field frequency applied by TTFields is in the intermediate frequency range (100 kHz ~ 500 kHz), the target excitation frequency needs to be set in the intermediate 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. Therefore, the target excitation frequency needs to be set in the range of DC ~ 200 Hz. The target excitation frequency in the present invention can be a frequency value or a range composed of a series of frequency values. The present invention does not limit the specific value of the target excitation frequency.

[0063] In the present invention, mapping transformation of the tissue conductivity distribution of intracranial soft tissues at the magnetic resonance excitation frequency to that at the target excitation frequency depends 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 shown by a fitting curve. In the present invention, the conductivity-frequency fitting curve is called the standard curve. The standard curve of tissue conductivity and excitation frequency can be determined by experiments or by prior knowledge such as literature reports. There have been a large number of such studies in the prior art. In the embodiments of the present invention, a standard curve showing the change of conductivity with frequency can be established by summarizing and analyzing the literature data of the conductivity values of various tissues measured at different frequencies.

[0064] In the present invention, the specific method for performing mapping transformation on the tissue conductivity distribution at different excitation frequencies by using the standard curve is as follows:

[0065] Assume that the magnetic resonance excitation frequency is and the target excitation frequency is . Locate and determine 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 tissue conductivity and excitation frequency. Then, for each voxel v in the intracranial soft tissues, calculate the relative change amplitude between the tissue conductivity of this voxel v at the magnetic resonance excitation frequency and the first standard conductivity. Then, perform the same relative change amplitude on the second standard conductivity to obtain the tissue conductivity of this voxel at the target excitation frequency. Thus, the tissue conductivities of all voxels in the intracranial soft tissues can constitute the tissue conductivity distribution of the intracranial soft tissues at the target excitation frequency.

[0066] S3. After the electrode matrix to be pasted on the head of the target patient is adjusted to the first arrangement position where the sensitive field distribution is concentrated in the scalp area, measure the conductivity distribution in the scalp area at the target excitation frequency by using electrical impedance tomography (EIT) technology. After the electrode matrix is adjusted to the second arrangement position where the sensitive field distribution is concentrated in the skull area, measure the conductivity distribution in the skull area at the target excitation frequency by using electrical impedance tomography technology.

[0067] It should be noted that the electrode matrix pasted on the head of the target patient in the present invention refers to the electrode matrix used in electrical impedance tomography technology, which consists of at least two pairs of excitation electrodes and measurement electrodes. The EIT technology belongs to the prior art, so the specific method of measuring the conductivity distribution using the EIT technology can also be implemented by referring to the methods in the prior art. The excitation electrodes and measurement electrodes used during EIT can be ceramic electrodes, and a conductive paste is applied between the electrode patches and the scalp surface.

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

[0069] However, the particularity of measuring the conductivity distribution using the EIT technology in the present invention lies in the need to distinguish between the scalp area and the skull area, and measure the conductivity distribution of each of the two areas separately. The present invention realizes this measurement requirement through a two-step method, that is, first adjust the position of the electrode matrix to the first arrangement position, so that the sensitive field distribution (that is, the spatial distribution of the voltage generated by the excitation current) is concentrated in the scalp area, and thus the conductivity distribution of the scalp area can be measured through the EIT technology at the target excitation frequency; when the conductivity distribution of the scalp area is measured, then adjust the position of the electrode matrix to the second arrangement position, so that the sensitive field distribution is as concentrated as possible in the skull area. At this time, since the electrodes are directly pasted on the scalp, there will still be a partial 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 through 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 above electrode matrix are the keys to realizing the above two-step measurement. In the embodiment of the present invention, the first arrangement position and the second arrangement position of the above electrode matrix can be determined in advance through simulation, and the simulation method is:

[0071] Use the head magnetic resonance scan image of the target patient to construct an individual head model, arrange the simulation model of the above electrode matrix on the individual head model, and through the finite element simulation method, simulate all possible arrangement positions of the electrode matrix, determine the sensitive field distributions of the scalp area and the skull area corresponding to the electrode matrix at different arrangement positions, and then take the arrangement position of the electrode matrix with the sensitive field distribution concentrated in the scalp area as the first arrangement position, and take the arrangement position of the electrode matrix with the sensitive field distribution concentrated in the skull area as the second arrangement position.

[0072] The key difference between the above first arrangement position and the second arrangement position lies in Figure 2The spacing between the four electrodes. 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. As shown in the left figure of Figure 3 , first set the excitation electrode distance to be relatively close. Obtain the sensitivity field distribution for the EIT measurement with a relatively close excitation electrode distance, so that most of the excitation current passes through the scalp, and the sensitivity of the target scalp area is increased as much as possible. Then select an appropriate excitation electrode spacing so that the sensitivity field distribution is concentrated in the scalp area and the skull area is as small as possible. Use this as the first arrangement position to carry out subsequent scalp conductivity measurement. Then gradually increase the distance between the excitation electrodes. As shown in the right figure of Figure 3 , further obtain the sensitivity field distribution under different excitation electrode spacings, so that the sensitivity of the skull area is increased as much as possible. Select an appropriate excitation electrode spacing as the second arrangement position, and combine the scalp conductivity determined by the first-step measurement to measure the 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 the skull can be adjusted, and the scalp and skull conductivities of the individual's local area can be accurately obtained.

[0073] It should be noted that the judgment criterion for whether the sensitivity field distribution is concentrated can be determined based on the sensitivity coefficient. The calculation formula is as follows:

[0074]

[0075] In the formula: is the sensitivity coefficient for the i-th pair of electrode excitation and the j-th pair of electrode measurement. , are the field potential distributions when the i-th pair of electrodes (excitation current is ) and the j-th pair of electrodes (excitation current is ) are excited.

[0076] Continuously adjust the positions of the two pairs of electrodes in the finite element simulation software, and find the electrode arrangement position corresponding to the exceeding the threshold (which can be set to 0.75) in the scalp area as the first arrangement position, and find the corresponding The electrode arrangement positions exceeding the threshold (which can be set to 0.75) are used as the second arrangement positions. When actually measuring the conductivity, the electrode matrix can be arranged according to the arrangement positions determined by the simulation. An excitation current is applied to the excitation electrodes in a swept-frequency manner, and the voltage distribution data is collected through the measurement electrodes. Then, according to the EIT principle, the conductivity distribution of the scalp and skull regions of the individual local part is obtained. In the embodiments of the present invention, a reverse solution model can be established through the sensitivity field distributions of the scalp region and the skull region respectively, and iteratively optimized until convergence, so as to obtain the conductivity spatial distributions of the scalp and the skull. Specifically, the iterative optimization objective for solving the conductivity spatial distributions of the two regions is to minimize the error between the measured voltage and the model-predicted voltage. Based on this objective, the conductivity distribution is continuously adjusted until the error converges, and the final conductivity distribution is the conductivity spatial distribution within 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 the embodiments of the present invention, the individual head model of the target patient is constructed using the head magnetic resonance scan image of the target patient. The masks of the intracranial soft tissue, the scalp region, and the 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 onto their respective masks, so as to assign conductivity to all voxels in the head region, and complete the construction of the individual head tissue conductivity model.

[0079] In the present invention, for the head tissue segmentation of the scanned patient magnetic resonance structural image MRI, it can be achieved through segmentation models such as U-Net. Through segmentation, the masks of tissues such as the scalp, skull, cerebrospinal fluid, gray matter, white matter, and tumors can be obtained. By corresponding the conductivity values of each tissue at each spatial position to the voxel positions of the segmented tissue masks, the individual head tissue conductivity model can be obtained. Figure 4 Exemplarily shows each intermediate image and the tissue conductivity distribution diagram during the process of obtaining the tissue conductivity distribution, while Figure 5 Exemplarily shows the standard curve between the conductivity of the white matter (WM) tissue and the excitation frequency used in this calculation process. Figure 6 Further exemplarily shows the schematic diagram of the result of the finally established individualized conductivity model.

[0080] The finally constructed individual head tissue conductivity model is subject to subsequent operations according to the requirements 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, mesh, 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, can separately construct the tissue conductivity that conforms to the actual situation of each patient, and realizes the construction of a high-precision individual head tissue electric field simulation model.

[0081] In addition, it should be noted that the method for constructing the individual head tissue conductivity model described in S1 to S4 in the above embodiment can essentially be executed by a computer program or module.

[0082] Thus, based on the same inventive concept, as Figure 7 shown, in another preferred embodiment of the present invention, there is also provided an individual head tissue conductivity model construction system, which includes:

[0083] A head tissue water content distribution acquisition module, configured 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 the first relationship model between the longitudinal relaxation time signal intensity and the tissue water content;

[0084] An intracranial soft tissue conductivity acquisition module, configured to, based on the head tissue water content distribution, in combination with the intracranial soft tissue space mask, use the second relationship model between the tissue water content and the tissue conductivity to calculate the tissue conductivity distribution of the intracranial soft tissue at the magnetic resonance excitation frequency; according to the standard curve of the tissue conductivity and the excitation frequency, convert the tissue conductivity distribution of the intracranial soft tissue at the magnetic resonance excitation frequency into the tissue conductivity distribution of the intracranial soft tissue at the target excitation frequency through mapping transformation;

[0085] An extracranial tissue conductivity acquisition module, configured to, after adjusting the electrode matrix to be pasted on the head of the target patient to the first arrangement position where the sensitive field distribution is concentrated in the scalp area, measure the scalp area conductivity distribution by bioelectrical impedance tomography at the target excitation frequency, and after adjusting the electrode matrix to the second arrangement position where the sensitive field distribution is concentrated in the skull area, measure the skull area conductivity distribution by bioelectrical impedance tomography at the target excitation frequency;

[0086] A conductivity model construction module, configured to integrate the tissue conductivity distribution of the intracranial soft tissue, the scalp area conductivity distribution, and the skull area conductivity distribution 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, in another preferred embodiment of the present invention, there is also provided a computer program product corresponding to the individual head tissue conductivity model construction method provided in the above embodiment, including computer programs / instructions, which can implement the individual head tissue conductivity model construction method in the above embodiment when executed by a processor.

[0088] Similarly, based on the same inventive concept, as Figure 8 shown, in another preferred embodiment of the present invention, there is also provided a computer electronic device corresponding to the individual head tissue conductivity model construction method 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, when the logical instructions in the above memory are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.

[0092] Therefore, based on the same inventive concept, in another preferred embodiment of the present invention, there is also provided a computer-readable storage medium corresponding to the individual head tissue conductivity model construction method provided in the above embodiment. The storage medium stores a computer program, which can implement the individual head tissue conductivity model construction method in the above embodiment when executed by a processor.

[0093] It can be understood that the above storage medium may include a random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory. At the same time, the storage medium can also be various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc.

[0094] It can be understood 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] In addition, it should 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 above-described system can refer to the corresponding process in the foregoing method embodiments, and will not be elaborated herein. In each of the embodiments provided in the present 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. For example, 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 implementation solutions of the present invention, but are not intended to limit the present invention. Those of ordinary skill in the relevant art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all technical solutions obtained by means of equivalent replacement or equivalent transformation fall within the protection scope of the present invention.

Claims

1. A method for constructing an electrical conductivity model of an individual's head tissue, characterized in that, Including: S1. 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 the first relationship model between the longitudinal relaxation time signal intensity and the tissue water content; S2. Based on the head tissue water content distribution, combined with the intracranial soft tissue space mask, use the second relationship model between the tissue water content and the tissue conductivity to calculate the tissue conductivity distribution of the intracranial soft tissue at the magnetic resonance excitation frequency; according to the standard curve of the tissue conductivity and the excitation frequency, convert the tissue conductivity distribution of the intracranial soft tissue at the magnetic resonance excitation frequency into the tissue conductivity distribution of the intracranial soft tissue at the target excitation frequency through mapping transformation; S3. After the electrode matrix to be pasted on the head of the target patient is adjusted to the first arrangement position where the sensitive field distribution is concentrated in the scalp area, measure the scalp area conductivity distribution by electrical impedance tomography at the target excitation frequency. After the electrode matrix is adjusted to the second arrangement position where the sensitive field distribution is concentrated in the skull area, measure the skull area conductivity distribution by electrical impedance tomography at the target excitation frequency; S4. Integrate the tissue conductivity distribution of the intracranial soft tissue, the scalp area conductivity distribution and the skull area conductivity distribution 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, wherein The longitudinal relaxation time signal intensity is extracted based on the head magnetic resonance scan images obtained by scanning at two different repetition times.

3. The method for constructing an individual head tissue conductivity model according to claim 1, wherein The first relationship model is obtained by fitting the sample data after performing magnetic resonance scans on a series of tissue samples with known water contents and extracting their respective longitudinal relaxation time signal intensities.

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 the sample data after measuring the conductivities of a series of tissue samples with known water contents.

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: find and determine 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. Then, for each voxel in the intracranial soft tissue, calculate the relative change amplitude between the tissue conductivity of this voxel at the magnetic resonance excitation frequency and the first standard conductivity, and then perform the same relative change amplitude on the second standard conductivity to obtain the tissue conductivity of this voxel at the target excitation frequency.

6. The method for constructing an individual head tissue conductivity model according to claim 1, wherein The first arrangement position and the second arrangement position of the electrode matrix are pre-determined by simulation. The simulation method is: construct an individual head model using the head magnetic resonance scan image of the target patient, arrange the electrode matrix on the individual head model, determine the sensitive field distributions of the scalp area and the skull area corresponding to different arrangement positions of the electrode matrix through simulation, and take the electrode matrix arrangement position where the sensitive field distribution is concentrated in the scalp area as the first arrangement position, and take the electrode matrix arrangement position 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 images of the target patient. Masks for intracranial soft tissues, scalp regions, and skull regions are obtained through tissue segmentation. Then, the tissue conductivity distributions of the intracranial soft tissues, scalp regions, and skull regions are respectively registered onto their respective masks, and conductivities are assigned to all voxels in the head region to complete the construction of the individual head tissue conductivity model.

8. A system for constructing an electrical conductivity model of an individual's head tissue, characterized in that, Including: A head tissue water content distribution acquisition module, configured to extract the longitudinal relaxation time signal intensity of each voxel from the head magnetic resonance scan images of the target patient, and calculate the head tissue water content distribution according to the first relationship model between the longitudinal relaxation time signal intensity and the tissue water content; An intracranial soft tissue conductivity acquisition module, configured to, based on the head tissue water content distribution, in combination with the intracranial soft tissue spatial mask, use the second relationship model between the tissue water content and the tissue conductivity to calculate the tissue conductivity distribution of the intracranial soft tissues at the magnetic resonance excitation frequency; according to the standard curve of the tissue conductivity and the excitation frequency, convert the tissue conductivity distribution of the intracranial soft tissues at the magnetic resonance excitation frequency into the tissue conductivity distribution of the intracranial soft tissues at the target excitation frequency through mapping transformation; An extracranial tissue conductivity acquisition module, configured to, after adjusting the electrode matrix to be pasted on the head of the target patient to the first arrangement position where the sensitive field distribution is concentrated in the scalp region, measure the scalp region conductivity distribution by electrical impedance tomography at the target excitation frequency, and after adjusting the electrode matrix to the second arrangement position where the sensitive field distribution is concentrated in the skull region, measure the skull region conductivity distribution by electrical impedance tomography at the target excitation frequency; A conductivity model construction module, configured to integrate the tissue conductivity distributions of the intracranial soft tissues, scalp regions, and skull regions into the individual head model of the target patient to obtain the individual head tissue conductivity model.

9. A computer-readable storage medium, characterized in that, The computer program is stored on the storage medium. When the computer program is executed by the processor, the method for constructing the individual head tissue conductivity model according to any one of claims 1 to 7 is implemented.

10. A computer electronic device, characterized in that, Including a memory and a processor; The memory is used for storing the computer program; The processor is configured to, when executing the computer program, implement the method for constructing the individual head tissue conductivity model according to any one of claims 1 to 7.

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