A three-dimensional EIT imaging method, imaging system, and storage medium

Abstract

This invention discloses a three-dimensional EIT imaging method, imaging system, and storage medium, belonging to the field of medical testing. Its key technical points are: first, acquiring a general set of bleeding points; then, processing each point in the set individually to obtain an image of the organ containing each point; wherein for any bleeding point P... i The processing method is as follows: SE01, first, determine P i The organ in question and the depth region described; SE02, based on the results of SE01, calls the corresponding dedicated sensitivity matrix and the corresponding boundary voltage difference matrix to generate P. i The relative conductivity distribution matrix corresponding to the single organ model and the field model representing the overall environment of the area to be detected is normalized to generate a reconstructed three-dimensional image. This invention aims to provide a three-dimensional EIT imaging method, imaging system, and storage medium to achieve imaging effects in multiple organs, such as the abdominal environment.

Description

Technical Field

[0001] The present invention belongs to the field of medical detection, and particularly relates to a three-dimensional EIT imaging method, an imaging system, and a storage medium. Background Art

[0002] <Technical Problem>

[0003] Abdominal trauma, especially blunt abdominal trauma (BAT), often leads to damage to internal organs and internal bleeding, especially damage to solid organs such as the liver, spleen, and kidneys. In emergency departments, such traumas have a very high mortality rate, so rapid assessment and emergency treatment are required to prevent hemorrhagic shock or death. However, internal bleeding is mostly occult bleeding, and before it develops into obvious signs such as shock, it often shows hidden symptoms. Therefore, in order to identify intra-abdominal bleeding as early as possible, it is particularly important to develop a fast, comprehensive, convenient, and accurate detection method.

[0004] <Existing Imaging Methods>

[0005] First, MSCT (multi-slice computed tomography) is based on X-rays and can generate high-resolution images of the internal structures of the body. It is the gold standard for the diagnosis of abdominal trauma and has high sensitivity and specificity. However, its detection equipment is large and immovable, and it exposes patients to the risks of ionizing radiation and nephrotoxic intravenous contrast agents. Therefore, it is not suitable for continuous monitoring of intra-abdominal bleeding in a simple environment.

[0006] Second, the imaging process of MRT (magnetic resonance imaging) is a time-consuming process and is rarely used in acute abdominal trauma.

[0007] Third, FAST (abdominal ultrasound) is a technique for rapidly assessing internal bleeding or fluid accumulation through ultrasound, but it is usually difficult to distinguish the specific types of free fluids (such as urine, bile, blood, or ascites). In addition, in the case of an organ hematoma without intra-abdominal free fluid, ultrasound is difficult to accurately detect.

[0008] Fourth, EIT (electrical impedance tomography) is widely used in various medical detections and there are also related studies on abdominal bleeding. For example, in Reference 1: "Li Zheng, Dong Xiuzhen, You Fusheng, et al. Experimental study on electrical impedance imaging for monitoring intra-abdominal bleeding in rabbits [J]. Biomedical Engineering and Clinical Medicine, 2006, 10(1):4", a bleeding monitoring system based on EIT was established in the abdominal cavity of rabbits.

[0009] <Technical Dilemma of Applying EIT to the Abdomen>

[0010] Existing abdominal bleeding mainly focuses on the two-dimensional EIT field. There has been no relevant research on using 3D-EIT for real-time abdominal bleeding detection. The main reasons are as follows:

[0011] 3D-EIT is a relatively mature study in tissues such as the chest. For example, Reference 2: Chen Xiaoyan, Chu Mengli, Chang Xiaomin, et al. Construction and image reconstruction of three-dimensional EIT model of lung [J]. Chinese Journal of Biomedical Engineering, 2017, 36(5):5; however, there are few studies on the abdomen.

[0012] Following the research and development approach of 3D-EIT in chest tissue, the basic technical approach for applying 3D-EIT to the abdominal plate is as follows:

[0013] (1) Hardware preparation: A three-dimensional impedance sensor similar to CN220193010U. For example... Figure 1-3 As shown, the abdominal 3D-EIT imaging system 1 includes: a node controller 2, a coaxial cable 3 with snap-fit ​​connectors, a 32-channel robust multiplexer 4, a wearable EIT sensor 5 (with 32 built-in electrodes 501-532, evenly arranged in a ring), and a computer 6. The four ports (Lc, Lp, Hp, and Hc) of the node controller 2 are expanded to 32 ports via the 32-channel robust multiplexer 4. The 32 channels are connected to the 32 electrodes 501-532 of the wearable EIT sensor 5 using the coaxial cable 3 with snap-fit ​​connectors; that is, the node controller 2 is connected to the 32-channel robust multiplexer 4 via the coaxial cable 3 with snap-fit ​​connectors. Data transmission between the node controller 2 and the computer 6 is wireless / wired.

[0014] The wearable EIT sensor 5 (with an electrode diameter of 6mm and a layer spacing of 60mm between the upper and lower layers) can be integrated with the node controller 2, the coaxial cable 3 with a snap-fit ​​connector, and the 32-channel robust multiplexer 4 to ensure stable contact between the 32 electrodes 501~532 and the human body and to prevent the movement of the subject from hindering the operation of the entire system during the test.

[0015] (2) Method for obtaining electrical impedance distribution: Multiple sets of cyclic measurements were performed on the subjects over a period of time (e.g., measurement in the mode of adjacent excitation-adjacent measurement) to obtain the boundary voltage data matrix of the three-dimensional electric field area Ω of the whole abdomen at t0, t1, ... tm.

[0016] (3) Calculate the relative conductivity distribution matrix σ, and then perform imaging:

[0017] σ=(J T J+k t I+k n W) -1 J T △V.

[0018] Where J represents the sensitivity matrix, J T It is the transpose of J;

[0019] Where, k t k n All are regularization parameters;

[0020] Wherein, W represents a diagonal matrix of the same order and diagonal elements as J;

[0021] Where I represents the identity matrix with the same number of columns as J;

[0022] Here, △V represents the boundary voltage difference matrix, which can be obtained from the boundary voltage data matrix.

[0023] For J, the three-dimensional sensitivity matrix is ​​obtained by solving the COMSOL software.

[0024] As the analysis above shows, the sensitivity matrix J is a key parameter affecting the relative conductivity distribution matrix result. For the sensitivity matrix, the process is as follows: first, construct a geometric model of the tissue, then import it into the finite element simulation software COMSOL. Electrodes are then simulated and set at the contour boundary of the tissue model to obtain the sensitivity matrix J (generally using an adjacent excitation-adjacent measurement method).

[0025] However, the above method did not produce good imaging results when applied to the human abdomen. The key reason is that the chest only contains the lungs, while the abdomen contains multiple organs such as the liver, kidneys, and spleen. Therefore, directly using the sensitivity matrix J obtained from the entire abdomen for imaging is not ideal.

[0026] However, the conventional approach to EIT calculation is to obtain the sensitivity matrix J and the boundary voltage difference matrix, then calculate the relative conductivity distribution matrix, and finally perform imaging.

[0027] To address the problem of poor imaging of multiple organs, one approach is to improve the accuracy of the geometric model and continuously increase the accuracy of the sensitivity matrix J, thereby improving the sensitivity of the imaging. However, this approach is not ideal in practice.

[0028] This has limited the application of 3D-EIT in abdominal examinations. Summary of the Invention

[0029] The purpose of this invention is to solve the problems existing in the prior art and to provide a three-dimensional EIT imaging method, imaging system, and storage medium.

[0030] The present invention is achieved through the following technical solution:

[0031] A three-dimensional EIT imaging method includes the following steps:

[0032] Step A: Obtain the overall sensitivity matrix J of the area to be detected, the grid point position matrix L, and the specific sensitivity matrices of a single organ and the external environment representing the overall environment of the area to be detected at different depths.

[0033] Step B: Obtain the boundary voltage difference matrix ΔV1, ΔV2, ΔV3 of the area to be detected under the double-layer electrode according to the methods of adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement.

[0034] Step C: Obtain the relative conductivity distribution matrix based on ΔV1 and J, normalize it, and then perform imaging.

[0035] Step D: Obtain the bleeding point set based on the results of step C;

[0036] Step E involves processing each point in the hemorrhage point set individually to obtain an image of the organ containing each point; where, for any hemorrhage point P... i The processing method is as follows:

[0037] SE01, First, determine P i The organ in which it is located and the depth region described;

[0038] SE02, based on the results of SE01, calls the corresponding dedicated sensitivity matrix and the corresponding boundary voltage difference matrix to generate P. i The relative conductivity distribution matrix corresponding to the single organ model and the field model representing the overall environment of the area to be detected is used to generate a reconstructed three-dimensional image.

[0039] SE03, Obtain the precise coordinates of the bleed point: The grid point location information corresponding to the elements in the normalized relative conductivity distribution matrix that are greater than the threshold ε is the precise coordinate of the bleed point.

[0040] Furthermore, the S-shaped excitation and S-shaped measurement refer to the following: both the upper and lower layers have T electrodes, with the upper layer electrodes numbered 1 to T and the lower layer electrodes numbered T+1 to 2T. First, the excitation current signal is applied: an AC sinusoidal current signal is injected from signal electrode 1 and then flows out from electrode T+1, forming a current loop. Then, the voltage signal is measured: the amplitude of 2(T-2)+1 boundary voltage signals between electrodes T+2 and 2, 2 and 3, 3 and T+3, ..., T and 2T is measured sequentially. The excitation is then performed by switching to signal electrodes T+2 and 2, 2 and 3, 3 and T+3, ..., T and 2T, and the amplitude of 2(T-2)+1 boundary voltage signals is obtained by sequentially measuring the remaining electrodes in an S-shape.

[0041] Furthermore, step A includes the following sub-steps:

[0042] SA01, obtain the overall sensitivity matrix J of the overall three-dimensional simulation model M of the area to be detected under the adjacent excitation ~ adjacent measurement mode and the grid point position matrix L corresponding to M;

[0043] SA02, the area to be detected includes N organs. Extract the model of each individual organ and the field model that represents the overall environment of the area to be detected to construct a new three-dimensional simulation model M1~MN; N is a natural number greater than or equal to 2.

[0044] SA03, M is divided into three depth regions I to III from the outside to the inside;

[0045] SA04, calculate the corresponding sensitivity matrices for M1~MN in depth regions I~III:

[0046] For any model Mi, forward problem simulations are performed in depth regions I, II, and III using adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement methods, respectively, to obtain the corresponding dedicated sensitivity matrix J. uai,1 J uai,2 J uai,3 .

[0047] Furthermore, step D includes: normalizing the relative conductivity distribution matrix σ uni The location information of all grid points corresponding to all elements greater than the threshold ε forms the bleeding point set location matrix.

[0048] Furthermore, step SE02 includes: P i In the j-th organ, at depth region k, the dedicated sensitivity matrix J is called. uaj ,k Boundary voltage difference calls ΔV k ; where j is any natural number from 1 to N, and k is 1, 2, or 3.

[0049] Furthermore, the area to be detected is the abdomen, and N equals 3.

[0050] A three-dimensional EIT imaging system, comprising:

[0051] a, Overall sensitivity matrix storage unit, which stores the overall sensitivity matrix J of the overall three-dimensional simulation model M of the area to be detected under adjacent excitation and adjacent measurement modes;

[0052] b, a grid point position matrix storage unit, which stores the grid point position matrix L of M;

[0053] c, Dedicated sensitivity matrix storage unit, which stores the dedicated sensitivity matrix J ua1,1 J ua1,2 J ua1,3 ...J uaN,1 JuaN,2 J uaN,3 ; among which, J uai,1 J uai,2 J uai,3 The meaning is as follows: The region to be detected has N organs. A three-dimensional simulation model is constructed from each individual organ model and an external field model representing the overall environment of the region to be detected, denoted as M1~MN. M is divided into three depth regions I~III from the outside in. Any model Mi is simulated in depth regions I, II, and III using methods of adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement, respectively, to obtain the corresponding dedicated sensitivity matrix: J. uai,1 J uai,2 J uai,3 ;

[0054] d, the first to third boundary voltage difference matrix storage unit, which is used to store the boundary voltage difference matrices △V1, △V2, and △V3 under the methods of adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement;

[0055] e, the overall relative conductivity distribution matrix solution unit (first solution unit), which calls ΔV1 and J to solve the overall field relative conductivity distribution matrix;

[0056] f, the approximate location of the bleeding point set is solved by the second solution unit, which retrieves the result of the overall relative conductivity distribution matrix solution unit to obtain the approximate location of the bleeding point;

[0057] g, the relative conductivity distribution matrix solution unit for bleeding points (the third solution unit), which retrieves the results of the solution unit for the approximate location of the bleeding point set to solve the relative conductivity distribution matrix corresponding to the single organ model where the bleeding point is located and the field model that represents the overall environment of the area to be detected.

[0058] h, the imaging unit, is capable of reading the imaging results of the overall relative conductivity distribution matrix solution unit or the bleeding point set location unit to draw a three-dimensional image.

[0059] Furthermore, it also includes: i, a bleeding point precise location solution unit (fourth solution unit), which retrieves the result of the bleeding point relative conductivity distribution matrix solution unit to obtain the precise location of the bleeding point.

[0060] Furthermore, it employs the aforementioned three-dimensional EIT imaging method.

[0061] A storage medium storing a program capable of executing the aforementioned method.

[0062] The advantages of the technical solution of this invention are mainly reflected in:

[0063] First, the basic idea of ​​this application is as follows: In a multi-organ environment (e.g., the abdominal environment), imaging is performed using a sensitivity matrix and boundary voltage difference obtained through adjacent excitation and adjacent measurement. The imaging sensitivity is very good for the lateral parts of the body surface, but relatively poor for the medial parts. S-shaped excitation and S-shaped measurement are sensitive to imaging deep within the body surface, but insensitive to imaging the body surface and deeper areas. Relative excitation and relative measurement are sensitive to imaging deeper within the body surface, but insensitive to imaging other areas. The method of this application can improve the accuracy of EIT imaging in tissues such as the brain, lungs, and abdomen.

[0064] Second, the three-dimensional EIT imaging method proposed in this application is not a disease diagnosis method. First, the location of the bleeding point is initially determined using existing technology (coarse estimation). Then, based on the depth region and organ where the bleeding point is located, a corresponding dedicated sensitivity matrix and a corresponding boundary voltage difference matrix are selected to realize a three-dimensional EIT schematic diagram of the organ with accurate reconstruction of the bleeding point, and the location of the bleeding point is accurately determined in the new image (precise determination). Attached Figure Description

[0065] The present invention will be further described in detail below with reference to the embodiments shown in the accompanying drawings, but this does not constitute any limitation on the present invention.

[0066] Figure 1 This is a hardware schematic diagram of an abdominal 3D-EIT imaging system according to this application.

[0067] Figure 2 This is a frontal view of the wearable EIT sensor of this application on the side away from the skin.

[0068] Figure 3 This is a back view of the wearable EIT sensor of this application, with the side in contact with the skin.

[0069] Figure 4 This is the EIT electrode layout diagram of this application.

[0070] Figure 5 This is a schematic diagram of the three different incentive methods proposed in this application.

[0071] Figure 6 This is a flowchart of the three-dimensional EIT imaging method of this application.

[0072] Figure 7 This is a comparison of the imaging effects of three methods for treating hemorrhages of different sizes in the liver.

[0073] 1: Abdominal 3D-EIT Imaging System;

[0074] 2: Node controller;

[0075] 3: Coaxial cable with snap-fit ​​connector;

[0076] 4: Thirty-two channel robust multiplexer;

[0077] 5: Wearable EIT sensor;

[0078] 501~532: Thirty-two electrodes;

[0079] 6: Computer. Detailed Implementation

[0080] The objectives, advantages, and features of this invention will be explained through the following non-limiting description of preferred embodiments. These embodiments are merely typical examples of applying the technical solutions of this invention, and all technical solutions formed by equivalent substitutions or equivalent transformations fall within the scope of protection claimed by this invention.

[0081] <Example 1: A Three-Dimensional EIT Imaging System and Imaging Method>

[0082] A three-dimensional EIT imaging method includes the following steps:

[0083] S100, obtain the overall sensitivity matrix J of the overall three-dimensional simulation model M of the area to be detected under the adjacent excitation ~ adjacent measurement mode and the grid point position matrix L corresponding to M (L and J can be obtained based on COMSOL software).

[0084] S200, the area to be detected includes N organs (when the area to be detected is the abdomen, N=3, which correspond to the liver, spleen and kidney respectively). Extract the model of each single organ and the field model that represents the overall environment of the area to be detected to construct a new three-dimensional simulation model M1~MN; N is a natural number greater than or equal to 2.

[0085] S300 divides M into three regions according to depth: Region I to III (the user can set the division and selection of depth regions I, II, and III according to actual needs. For example: Region III is the region contained within one-third of the distance from the center point to the body surface; Region III is the region formed at one-third of the distance from the center point to the body surface; Region II is the region formed by the outer boundary of Region III and two-thirds of the distance from the center point to the body surface).

[0086] Region I is the region remaining after removing regions III and II, that is, the outer boundary of region I is the outer boundary of M, and the inner boundary is the outer boundary of region II;

[0087] S400, for each single organ model and the field models M1~MN that represent the overall environment of the area to be detected in regions I~III, the corresponding sensitivity matrices are obtained.

[0088] When the area to be detected is the abdomen, the three-dimensional simulation model M1 is a field model that only includes the liver model and the external representation of the overall environment of the abdominal cavity; the three-dimensional simulation model M2 is a field model that only includes the spleen model and the external representation of the overall environment of the abdominal cavity; and the three-dimensional simulation model M3 is a field model that only includes the kidney model and the external representation of the overall environment of the abdominal cavity.

[0089] An arbitrary model Mi is simulated in the depth region I using an adjacent excitation and adjacent measurement method to perform a forward problem simulation, and the sensitivity matrix J is obtained. uai,1 ;

[0090] An arbitrary model Mi is simulated in depth region II using an S-shaped excitation and S-shaped measurement method to perform a forward problem simulation, resulting in the sensitivity matrix J. uai,2 ;

[0091] Arbitrary model Mi is simulated in depth region III using a relative excitation and relative measurement method to perform a forward problem simulation, and the sensitivity matrix J is obtained. uai,3 ;

[0092] That is,

[0093] For the depth region I of the mesh model V1 corresponding to M1, adjacent excitations and adjacent measurements are selected for forward problem simulation to obtain the dedicated sensitivity matrix J. ua1,1 .

[0094] For the depth region II of mesh model V1 corresponding to M1, an S-shaped excitation and S-shaped measurement are selected for forward problem simulation to obtain the dedicated sensitivity matrix J. ua1,2 .

[0095] For the depth region Ⅲ of the mesh model V1 corresponding to M1, a forward problem simulation is performed using relative excitation and relative measurement to obtain the dedicated sensitivity matrix J. ua1,3 .

[0096] For the depth region I of the mesh model V2 corresponding to M2, adjacent excitations and adjacent measurements are selected for forward problem simulation to obtain the dedicated sensitivity matrix J. ua2,1 .

[0097] For the depth region II of the mesh model V2 corresponding to M2, an S-shaped excitation and S-shaped measurement are selected for forward problem simulation to obtain the dedicated sensitivity matrix J. ua2,2 .

[0098] For the depth region Ⅲ of the mesh model V2 corresponding to M2, a forward problem simulation is performed using relative excitation and relative measurement to obtain the dedicated sensitivity matrix J. ua2,3 .

[0099] For the depth region I of the mesh model V3 corresponding to M3, adjacent excitations and adjacent measurements are selected for forward problem simulation to obtain the dedicated sensitivity matrix J. ua3,1 .

[0100] For the depth region II of mesh model V3 corresponding to M3, an S-shaped excitation and S-shaped measurement are selected for forward problem simulation to obtain the dedicated sensitivity matrix J. ua3,2 .

[0101] For the depth region Ⅲ of the mesh model V3 corresponding to M3, a forward problem simulation is performed using relative excitation and relative measurement to obtain the dedicated sensitivity matrix J. ua3,3 .

[0102] S500, obtain the boundary voltage difference matrix △V1, △V2, △V3 of the test subject under adjacent excitation adjacent measurement, S-shaped excitation S-shaped measurement, and relative excitation relative measurement methods;

[0103] Specifically, the following methods are used sequentially: adjacent current input and adjacent voltage measurement (i.e., adjacent excitation and adjacent measurement method), S-shaped current input and S-shaped voltage measurement (i.e., S-shaped excitation and S-shaped measurement method), and relative current input and relative voltage measurement (i.e., relative excitation and relative measurement method). A constant current (3mA) with a frequency of f=100kHz is applied and applied to the upper abdominal region of the wearable EIT sensor 5 through a 32-channel robust multiplexer 4 and a coaxial line 3 with a snap-fit ​​connector. The impedance data matrix V1 (obtained by adjacent excitation and adjacent measurement method), V2 (obtained by S-shaped excitation and S-shaped measurement method), and V3 (obtained by relative excitation and relative measurement method) at a frequency of 100kHz are obtained.

[0104] ΔV1, ΔV2, and ΔV3 can be obtained from V1, V2, and V3 (by subtracting the impedance data matrices obtained at different times).

[0105] S600, utilizing J The relative conductivity distribution matrix σ of the overall model is obtained by solving ΔV1 and normalizing it to obtain the normalized relative conductivity distribution matrix σ. uni Then, an image is formed;

[0106] S700, Preliminary determination of the location of the bleeding point set: Select matrix σ uni The location information of all grid points corresponding to all elements greater than the threshold ε (these grid points are the bleed point set) forms the bleed point set location matrix Q;

[0107] It should be noted that: σ uni The elements are distributed in one column and have J elements; the corresponding matrix L also has J rows and 3 columns, and the two are corresponding. The data in the first to third columns of any G-th row actually represent the x, y, and z coordinates of the G-th grid point; σ uni The element in the G-th row represents the normalized relative conductivity value of that grid point.

[0108] When σ uni If any element in row G of L is greater than the threshold ε, return the three data points in row G of L.

[0109] S800 processes each point in the hemorrhage point set individually to obtain an image of the organ containing each point; wherein, for any point P in the hemorrhage point set... i The method for obtaining an imaging image of the organ where the point is located includes steps S801 to S802;

[0110] S801, First, determine P i The organ in which it is located and the depth region described;

[0111] S802, based on the result of S801, calls the corresponding dedicated sensitivity matrix and the corresponding boundary voltage difference matrix to generate P. i The single organ model and the field model M representing the overall environment of the area to be detected. j The corresponding relative conductivity distribution matrix, after being normalized, is then filled into the corresponding finite element mesh model V. j In the next image, the image is re-imaged; the precise coordinates of the bleeding point are determined in the new image (this process is similar).

[0112] For example, if P1 is located in the kidney area – depth region I (close to the body surface), then call: J ua3,1 And △V1. Regenerate a field model containing only the kidney model and the external representation of the overall peritoneal environment, at which point the bleeding point P1 can be seen.

[0113] A three-dimensional EIT imaging system, comprising:

[0114] (1) The overall sensitivity matrix storage unit stores the overall three-dimensional simulation model M of the area to be detected in the adjacent excitation ~ adjacent measurement modes, and the overall sensitivity matrix of the area to be detected. J ;

[0115] (2) The grid point position matrix storage unit stores the grid point position matrix L of M;

[0116] Dedicated sensitivity matrix storage unit stores: J ua1,1 J ua1,2 J ua1,3 ...J uaN,1 J uaN,2 J uaN,3 ; among which, J uai,1 J uai,2 J uai,3The meaning is as follows: The region to be detected has N organs. A three-dimensional simulation model is constructed from each individual organ model and an external field model representing the overall environment of the region to be detected, denoted as M1~MN. M is divided into three depth regions I~III from the outside in. Any model Mi is simulated in depth regions I, II, and III using methods of adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement, respectively, to obtain the corresponding dedicated sensitivity matrix: J. uai,1 J uai,2 J uai ,3 ;

[0117] (3) The first to third boundary voltage difference matrix storage unit is used to store the boundary voltage difference matrices △V1, △V2, and △V3 under the methods of adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement.

[0118] (4) The overall relative conductivity distribution matrix solution unit, which retrieves ΔV1 and J Solve for the relative conductivity distribution matrix of the entire field;

[0119] (5) The unit for solving the approximate location of the bleeding point set retrieves the result of the unit for solving the overall relative conductivity distribution matrix to obtain the approximate location of the bleeding point;

[0120] (6) The relative conductivity distribution matrix solution unit for bleeding points retrieves the results of the solution unit for the approximate location of the bleeding point set to solve the relative conductivity distribution matrix corresponding to the single organ model where the bleeding point is located and the field model that represents the overall environment of the area to be detected.

[0121] (7) Imaging unit, which can read the imaging results of the overall relative conductivity distribution matrix solution unit or the bleeding point set location unit to draw a three-dimensional image.

[0122] <Results Comparison>

[0123] Figure 7 The simulation results for different sizes of liver bleeding (V1=12mL, V2=26mL, V3=50mL) using a sensor with a 6cm interlayer electrode distance are shown: (a) is the location and size of bleeding in the simulation setting, (b) is the 3D simulation image of the double electrode based on 3D EIT (corresponding to the result obtained in step S600 of this application); (3) the 3D simulation image of the double electrode of this application.

[0124] Observations show that two-dimensional imaging cannot effectively characterize the size of bleeding points when the depth of the bleeding point increases but the cross-sectional area does not change significantly. The imaging image of this application is superior to 3D EIT in presenting the location and size of bleeding points. The image correlation coefficient (ICC) and structural similarity (SSIM) of the reconstructed image quality evaluation indicators are improved, and the imaging position offset is reduced.

[0125] It should be noted that: obtaining the overall sensitivity matrix corresponding to the simplified three-dimensional simulation model M of the organization. J It includes the following sub-steps S101~S103:

[0126] S101, first construct a simplified three-dimensional simulation model M of the organization; (for example, the upper abdomen includes: three organ models, namely the liver, spleen and kidney, and a field model representing the overall environment of the abdominal cavity).

[0127] S102: Using multi-slice CT images of the upper abdomen, import them into Mimics software. By setting the CT value range, generate the initial mask for each organ. After manually filling in the missing areas, use dynamic region growth to generate the three-dimensional model M of each organ.

[0128] S103, use SolidWorks software to draw a three-dimensional field model X that represents the overall environment of the abdominal cavity.

[0129] S104 uses COMSOL Multiphysics with MATLAB software to perform forward problem simulations, controlled by a MATLAB program. The MATLAB program primarily imports the constructed 3D models M and X, determines their positions by setting (x, y, z) coordinates, forming the overall model Z; it then calls COMSOL to adaptively mesh model Z using tetrahedral meshing, extracts the point coordinates and triangular element indices from the mesh information, and saves them as a mesh point position matrix L. Subsequently, it assigns the conductivity and relative permittivity at 100kHz to the fields representing the overall abdominal cavity environment and the fields of the liver, spleen, and kidneys. Following this, using the MATLAB program, it switches the electrode surface channels in the simulation model Z to receive a 3mA current excitation, using adjacent excitation to adjacent measurement electrode excitation paths, and measures the voltage on other electrode surfaces. Based on these data, a sensitivity matrix J is constructed, where J is (928 * number of mesh nodes).

[0130] It should be noted that the method for solving the relative conductivity distribution matrix σ of the overall model using J and ΔV is as follows:

[0131] σ=(J T J+k t I+k n W) -1 JT △V;

[0132] Among them, J T It is the transpose of J; k t k n All are regularization parameters; W represents a diagonal matrix of the same order and diagonal elements as J; I represents an identity matrix with the same number of columns as J.

[0133] It should be noted that the method of adjacent excitation and adjacent measurement is as follows (reference: CN116269303B): There are E electrodes, numbered 1, 2, ..., E; First, the excitation current signal: the AC sinusoidal current signal set by the system is injected into the signal electrode 1 and then flows out from the signal electrode 2, forming a current in-out loop; then, the voltage signal is measured: the amplitude of the boundary voltage signal between the E-3 boundary electrodes between the 3rd and 4th electrodes, the 4th and 5th electrodes, ..., the E-1th and Eth electrodes are measured in sequence; the excitation is then performed by changing the signal electrodes to the 2nd to 3rd electrodes, the 3rd to 4th electrodes, ..., the ith to i+1th electrodes, ..., the E-1th to Eth electrodes, and the Eth to 1st electrodes, and the amplitude of the voltage signal between the adjacent electrodes is measured in sequence until all electrodes have been measured.

[0134] It should be noted that the method for S-shaped excitation and S-shaped measurement is explained below (applicable to the case of double-layer electrodes):

[0135] There are 32 electrodes, numbered as follows: Figure 3 As shown: 501, 502...532; First, the excitation current signal: the AC sinusoidal current signal set by the system is first injected into signal electrode 501, and then flows out from electrode 517, forming a current in-and-out loop; then, the voltage signal is measured: the amplitude of 29 boundary voltage signals between electrodes 518 and 502, 502 and 503, 503 and 519, ..., 532 and 516 is measured in an S-shape; the excitation is then performed by sequentially changing signal electrodes 517~518, 518~502...516~501, and the amplitude of the 29 boundary voltage signals is measured in an S-shape until all electrodes are measured.

[0136] It should be noted that the method of relative excitation and relative measurement is described below (reference: CN115700556A):

[0137] First, the excitation current signal: the AC sinusoidal current signal set by the system is injected into signal electrode 501 and then flows out from electrode 525, forming a current in-and-out loop; then, the voltage signal is measured: the amplitude of 15 boundary voltage signals between electrodes 502 and 526, 503 and 527, 504 and 528, ..., 509 and 517, 510 and 518, ..., 516 and 524 is measured in sequence; the excitation is then performed by changing the signal electrodes 502~526, 503~527...516~524, and the amplitude of the voltage signal between all remaining opposite electrodes is measured in sequence until all electrodes have been measured.

[0138] The above description is merely a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. Those skilled in the art should realize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A three-dimensional EIT imaging method, characterized in that, Includes the following steps: Step A: Obtain the overall sensitivity matrix J of the area to be detected, the grid point position matrix L, and the specific sensitivity matrices of a single organ and the external environment representing the overall environment of the area to be detected at different depths. Step B: Obtain the boundary voltage difference matrices ΔV1, ΔV2, and ΔV3 under the adjacent excitation adjacent measurement, S-shaped excitation S-shaped measurement, and relative excitation relative measurement methods; Step C: Obtain the relative conductivity distribution matrix based on ΔV1 and J, normalize it, and then perform imaging. Step D: Obtain the bleeding point set based on the results of step C; Step E: Process each point in the bleeding point set one by one to obtain the imaging map of the organ where each point is located. For any bleeding point P i The processing method is as follows: SE01, First, determine P i The organ in which it is located and the depth region described; SE02, based on the results of SE01, calls the corresponding dedicated sensitivity matrix and the corresponding boundary voltage difference matrix to generate P. i The relative conductivity distribution matrix corresponding to the single organ model and the field model representing the overall environment of the area to be detected is normalized to generate a reconstructed three-dimensional image. The S-shaped excitation and S-shaped measurement refer to the following: both the upper and lower layers have T electrodes, with the upper layer electrodes numbered 1 to T and the lower layer electrodes numbered T+1 to 2T. First, the excitation current signal is applied: an AC sinusoidal current signal is injected from signal electrode 1 and then flows out from electrode T+1, forming a current loop. Then, the voltage signal is measured: the amplitude of 2(T-2)+1 boundary voltage signals between electrodes T+2 and 2, 2 and 3, 3 and T+3, ..., T and 2T is measured sequentially. The excitation is then performed by switching to signal electrodes T+2 and 2, 2 and 3, 3 and T+3, ..., T and 2T, and the amplitude of 2(T-2)+1 boundary voltage signals is obtained by sequentially measuring the remaining electrodes in an S-shape.

2. The three-dimensional EIT imaging method as described in claim 1, characterized in that, Step A includes the following sub-steps: SA01, obtain the overall sensitivity matrix J of the overall three-dimensional simulation model M of the area to be detected under the adjacent excitation ~ adjacent measurement mode and the grid point position matrix L corresponding to M; SA02, the area to be detected includes N organs. Extract the model of each individual organ and the field model that represents the overall environment of the area to be detected to construct a new three-dimensional simulation model M1~MN; N is a natural number greater than or equal to 2. SA03, M is divided into three depth regions I to III from the outside to the inside; SA04, calculate the corresponding sensitivity matrices for M1~MN in depth regions I~III: For any model Mi, forward problem simulations are performed in depth regions I, II, and III using adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement methods, respectively, to obtain the corresponding dedicated sensitivity matrix J. uai,1 J uai,2 J uai,3 .

3. The three-dimensional EIT imaging method as described in claim 1, characterized in that, Step D includes: normalizing the relative conductivity distribution matrix σ uni The location information of all grid points corresponding to all elements greater than the threshold ε forms the bleeding point set location matrix.

4. The three-dimensional EIT imaging method as described in claim 2, characterized in that, Step SE02 is followed by: SE03, obtaining the precise coordinates of the bleed point: the grid point location information corresponding to the elements in the normalized relative conductivity distribution matrix that are greater than the threshold ε is the precise coordinate of the bleed point.

5. A three-dimensional EIT imaging method as described in claim 1, characterized in that, The area to be detected is the abdomen, and N equals 3.

6. A three-dimensional EIT imaging system, characterized in that, It includes: a, Overall sensitivity matrix storage unit, which stores the overall sensitivity matrix J of the overall three-dimensional simulation model M of the area to be detected under adjacent excitation and adjacent measurement modes; b, the grid point position matrix storage unit, which stores the grid point position matrix L of M; c, Dedicated sensitivity matrix storage unit, which stores the dedicated sensitivity matrix J ua1,1 J ua1,2 J ua1,3 ...J uaN,1 J uaN,2 J uaN,3 J uai,1 J uai,2 J uai,3 The meaning is as follows: The region to be detected has N organs. A three-dimensional simulation model is constructed from each individual organ model and the external field model representing the overall environment of the region to be detected, denoted as M1~MN. M is divided into three depth regions I~III from the outside in. Any model Mi is simulated in depth regions I, II, and III using methods of adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement, respectively, to obtain the corresponding dedicated sensitivity matrix: J. uai ,1 J uai,2 J uai,3 ; d, the first to third boundary voltage difference matrix storage unit, which is used to store the boundary voltage difference matrices △V1, △V2, and △V3 under the methods of adjacent excitation and adjacent measurement, S-shaped excitation and S-shaped measurement, and relative excitation and relative measurement; e, the first solution unit, which retrieves ΔV1 and J to solve the relative conductivity distribution matrix of the overall field; f, the second solver unit, retrieves the result of the first solver to obtain the approximate location of the bleeding point set; g, the third solving unit, which calls the results of the second solving unit to solve the relative conductivity distribution matrix corresponding to the single organ model where the bleeding point is located and the field model that represents the overall environment of the area to be detected. h, imaging unit, which is used to draw three-dimensional images; The S-shaped excitation and S-shaped measurement refer to the following: both the upper and lower layers have T electrodes, with the upper layer electrodes numbered 1 to T and the lower layer electrodes numbered T+1 to 2T. First, the excitation current signal is applied: an AC sinusoidal current signal is injected from signal electrode 1 and then flows out from electrode T+1, forming a current loop. Then, the voltage signal is measured: the amplitude of 2(T-2)+1 boundary voltage signals between electrodes T+2 and 2, 2 and 3, 3 and T+3, ..., T and 2T is measured sequentially. The excitation is then performed by switching to signal electrodes T+2 and 2, 2 and 3, 3 and T+3, ..., T and 2T, and the amplitude of 2(T-2)+1 boundary voltage signals is obtained by sequentially measuring the remaining electrodes in an S-shape.

7. The three-dimensional EIT imaging system as described in claim 6, characterized in that, Also includes: i, the fourth solver unit, retrieves the results from the third solver unit to obtain the precise location of the bleeding point.

8. A storage medium, characterized in that, It stores a program that can be executed by a processor to implement the method as described in any one of claims 1 to 5.

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