EIT excitation measurement construction method, equipment and program product

Abstract

The invention relates to the field of intelligent medical treatment, in particular to an EIT excitation measurement construction method and device and a program product. Comprising N EIT electrodes, and N is a natural number larger than 1. Constructing a column full-rank electrode matrix; generating an excitation measurement vector based on the column full-rank electrode matrix to obtain an excitation measurement vector matrix; calculating the excitation measurement matrix to obtain a maximum excitation measurement channel set; and obtaining a maximum excitation measurement mode of the EIT electrode based on the maximum excitation measurement channel set. According to the method, redundant excitation can be avoided during EIT excitation measurement, efficient screening of independent excitation measurement channels is achieved, and the method has good clinical value.

Description

Technical Field

[0001] This application relates to the field of intelligent healthcare, specifically to an EIT stimulus measurement construction method, device, program product, and computer-readable storage medium. Background Technology

[0002] Currently, the most widely used EIT imaging method is 2D imaging. However, due to the natural properties of current divergence and propagation, 2D EIT images are actually 2D mappings of 3D conductivity distribution. While these 2D mapping images can reveal physiological and pathological information on the electrode plane to some extent, they are difficult to express the physiological and pathological conditions of brain tissue far from the 2D electrode plane, and are even affected by regions outside the plane, making the images of that plane difficult to interpret. 3D EIT images the spatial conductivity distribution, and compared to 2D EIT, it has a significant advantage in the accuracy of detecting lesion location and volume. For example, 2D EIT in the brain is not as good as 3D EIT in detecting lesion location and volume. Therefore, 3D EIT imaging has always been a research hotspot.

[0003] Currently, most 3D EIT methods are derived from the migration and development of classic 2D EIT methods. However, the expansion of dimensions has led to some new challenges: In terms of excitation measurement modes, the expansion from 2D to 3D means that the imaging area of ​​the detection site becomes larger and the area away from the electrode increases, which in turn weakens the ability to detect changes in conductivity of the detection site—the measurement sensitivity of the measurement site becomes weaker; the increase in electrodes leads to an increase in redundant SMCs and an increase in data acquisition time. Summary of the Invention

[0004] To address the above problems, this invention provides a method for constructing EIT excitation measurements, specifically including:

[0005] There are N EIT electrodes, where N is a natural number greater than 1;

[0006] Construct a full-rank column electrode matrix;

[0007] The excitation measurement vector matrix is ​​obtained by generating the excitation measurement vector based on the full-rank column electrode matrix;

[0008] The maximum set of excitation measurement channels is obtained by calculating the excitation measurement matrix;

[0009] The maximum excitation measurement mode of the EIT electrode is obtained based on the maximum excitation measurement channel set.

[0010] The construction process of the full-rank electrode matrix is ​​as follows:

[0011] The first column of the full-rank matrix is ​​generated using a random method;

[0012] The first excitation measurement vector matrix is ​​obtained by exciting and measuring the EIT electrode based on the first column full-rank matrix and through the first excitation measurement mode;

[0013] Calculate the rank of the first excitation vector matrix. When the rank of the first excitation vector matrix is ​​equal to the maximum number of independent excitation measurements of the first excitation measurement, the current first excitation vector matrix is ​​a column-full-rank electrode matrix.

[0014] Optionally, the first excitation measurement mode includes any one of the following: adjacent excitation measurement, relative excitation measurement, and interphase excitation measurement.

[0015] The excitation measurement vector is generated by calculating the excitation vector and measurement vector using a column full-rank electrode matrix, and the excitation vector and measurement vector are multiplied together to obtain the excitation measurement vector.

[0016] Optionally, the calculation of the excitation vector is expressed as:

[0017]

[0018] Where s represents the excitation electrode vector. This represents the positive independent component of the excitation electrode. This indicates that the excitation electrode is a negative independent component;

[0019] Optionally, the calculation of the measurement vector is expressed as:

[0020]

[0021] Where m represents the measuring electrode vector. This represents the positive independent component of the measuring electrode. Indicates the negative independent component of the measuring electrode;

[0022] Optionally, an excitation measurement vector matrix is ​​obtained by generating excitation measurement vectors for N EIT electrodes based on the full-rank column electrode matrix;

[0023] Optionally, the method further includes calculating the number of independent excitation measurement channels by calculating the rank of the excitation measurement matrix;

[0024] Optionally, the maximum set of excitation measurement channels is represented as a maximal linearly independent set of the excitation measurement matrix.

[0025] The method also includes maximum sensitivity calculation, which maximizes the measurement sensitivity of the change in conductivity at the measurement site when performing excitation measurement on the maximum excitation measurement mode, and obtains the maximum sensitive excitation measurement mode;

[0026] Optionally, the maximum sensitive excitation measurement mode is obtained by calculating and sorting the sensitivity of the excitation measurement channels in the EIT electrode, and adding the excitation measurement channels with high sensitivity to the maximum excitation measurement mode.

[0027] Optionally, the calculation process for the maximum sensitive excitation measurement mode is as follows:

[0028] S1. Obtain all excitation measurement channels for N EIT electrodes;

[0029] S2. Calculate the sensitivity of each excitation measurement channel and sort the sensitivities.

[0030] S3. Based on sensitivity, starting from the excitation measurement channel corresponding to the first sensitivity matrix, the excitation measurement channels are added sequentially to the maximum excitation measurement mode to obtain the second excitation measurement mode.

[0031] S4. Calculate the excitation measurement vector matrix of the second excitation measurement mode;

[0032] S5. Determine the independence of the added excitation measurement channels based on the excitation measurement vector matrix. If the determination result is independent, retain the channel; otherwise, remove the added excitation measurement channel.

[0033] Repeat steps S3-S5 to obtain the measurement mode with the maximum sensitivity to excitation.

[0034] Optionally, the maximum sensitivity excitation measurement mode performs two-dimensional EIT measurement and / or three-dimensional EIT measurement;

[0035] Optionally, the maximum sensitive excitation measurement mode is used for three-dimensional EIT measurement.

[0036] The calculation of the measurement sensitivity to maximize the change in conductivity at the measurement site is obtained by calculating the change in measurement voltage of the excitation measurement channel, the change in conductivity at the measurement site, and the sensitivity of the excitation measurement channel.

[0037] Optionally, the calculation of maximizing the measurement sensitivity of the change in conductivity at the measurement site further includes correction. The measurement voltage change of the first excitation measurement mode is calculated to obtain the first measurement voltage change. The absolute values ​​of the corresponding indices of the measurement voltage change of any excitation measurement channel and the first measurement change are taken and added to obtain the corrected measurement voltage change. The measurement sensitivity of maximizing the change in conductivity at the measurement site is calculated based on the corrected measurement voltage change of the excitation measurement channel, the change in conductivity at the measurement site, and the sensitivity of the excitation measurement channel.

[0038] Optionally, the measurement site includes one or more of the following: chest, brain.

[0039] The addition of the excitation measurement channel is to obtain the second excitation measurement mode by adding one excitation measurement channel at a time;

[0040] Optionally, the excitation measurement channel is added by adding L excitation measurement channels at a time, where L is a natural number greater than 1, to obtain a third excitation measurement mode. The excitation measurement vector matrix of the third excitation measurement mode is calculated and the independence is judged to obtain the maximum sensitive excitation measurement mode.

[0041] The excitation measurement vector matrix of the third excitation measurement mode also includes independent excitation measurement channel screening. The selected independent excitation measurement channels are obtained by calculating the excitation measurement vector matrix, thus obtaining the maximum sensitive excitation measurement mode.

[0042] Optionally, the calculation involves eliminating variables from the excitation measurement vector matrix to obtain the selected independent excitation measurement channels; optionally, the elimination includes one or more of the following: Gaussian elimination, column pivoting elimination, and LU decomposition;

[0043] Optionally, the elimination is Gaussian elimination.

[0044] The purpose of this invention is to provide a computer program product that includes a computer program or instructions, which are executed by a processor to implement the above-described EIT excitation measurement construction method.

[0045] The purpose of this invention is to provide a computer device comprising a memory, a processor, and a computer program or instructions stored in the memory, wherein the computer program or instructions are executed by the processor to implement the above-described EIT excitation measurement construction method.

[0046] The purpose of this invention is to provide a computer-readable storage medium having a computer program or instructions stored thereon, which are executed by a processor to implement the above-described EIT excitation measurement construction method.

[0047] Advantages of this invention:

[0048] 1. The independent redundancy relationship of excitation measurement channels (SMCs) determined by the circuit superposition theorem and reciprocity theorem is abstracted into a linear correlation between matrices and vectors, realizing efficient screening of independent SMCs. Compared with existing excitation measurement modes such as adjacent excitation measurement or opposing excitation measurement, the maximum excitation measurement mode (Max-M SMP) of this invention selects the maximum SMC from all SMCs in N electrodes for excitation measurement, avoiding redundancy in excitation measurement and reducing the impact of excitation redundancy on the conductivity change detection capability of the measurement part.

[0049] 2. Subsequently, the largest independent SMC set with theoretically maximum measurement sensitivity, namely Dual-M SMP, is obtained from all possible SMCs. This improves the sensitivity of the maximum excitation measurement mode to conductivity changes, enabling better capture of conductivity variations at the measurement site. Furthermore, the proposed SM matrix and SMP are utilized... ads The computational cost of Dual-M SMP has been significantly optimized, and the calculation of a finite element model of a 32-electrode head can be completed in less than 30 seconds. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the EIT excitation measurement construction method provided in an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the EIT excitation measurement construction system provided in an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the EIT excitation measurement construction device provided in an embodiment of the present invention;

[0054] Figure 4 Algorithm flow of Dual-M SMP provided for embodiments of the present invention

[0055] Figure 5 The 16-9-7 three-ring head electrode model provided in this embodiment of the invention. Electrodes E1-E16 form a 16-electrode ring R1, electrodes E17-E25 form a 9-electrode ring R2, and electrodes E26-E32 form a 7-electrode ring R3. (a) and (b) are the left view and top view of the electrode model, respectively.

[0056] Figure 6 This is an excitation-measurement distribution diagram of a Dual-M SMP provided in an embodiment of the present invention. The thickness of the red or cyan lines indicates the number of times the excitation pair or measurement pair participates in the excitation or measurement, the size of the blue electrode points indicates the number of times the electrode participates in the excitation or measurement, and the cyan lines represent the excitation or measurement pairs of the top 4 SMCs with the highest measurement sensitivity. (a) and (b) are Dual-M SMPs with 16 and 32 electrodes in the numerical model, respectively; (c) and (d) are Dual-M SMPs with 16 and 32 electrodes in the physical model, respectively.

[0057] Figure 7The diagram shows the singular values ​​and tolerances of SM and J corresponding to the Dual-M SMP provided in the embodiments of the present invention. (a) 16-electrode Dual-M SMP (b) 32-electrode Dual-M SMP;

[0058] Figure 8 This is a measurement sensitivity distribution diagram of Dual-M SMP and Skip-n SMP provided in an embodiment of the present invention. Normalization is achieved using the SMC with the highest measurement sensitivity among all SMPs;

[0059] Figure 9 The singular values, condition number, and number of independent SMCs for Dual-M SMP and Skip-n SMP provided in this embodiment of the invention. (a) The sum of the singular values ​​of J corresponding to the SMP in descending order; (b) The condition number and number of independent SMCs corresponding to J in the SMP;

[0060] Figure 10 Comparison of uniformity and sensitivity distribution of Dual-MSMP and Skip-n SMP provided in this embodiment of the invention. (a) Coefficient of variation of sensitivity distribution in the plane containing the 16 electrode ring R1 of the SMP, with the numerical model set as a uniform conductivity distribution. (b) Excitation-measurement distribution chord plot and sensitivity distribution plot of the SMP. The vertex set of the chord plot represents the electrodes, and the edge set represents the excitation or measurement pairs in the SMP. The width of the vertices and edges is related to the number of corresponding electrodes and excitation-measurement pairs. The values ​​in the sensitivity distribution plot are the minimum sensitivity after normalization of the maximum sensitivity. From left to right and from top to bottom, they represent Dual-MSMP and Skip-0 to Skip-7 SMP, respectively.

[0061] Figure 11 The excitation-measurement distribution of the Dual-M SMP provided in the embodiments of the present invention. (a) The vertex set of the chord diagram is the electrode, and the edge set is the excitation or measurement pair in the SMP; (b) are the top 8 SMCs with the highest measurement sensitivity, and the excitation pairs and measurement pairs belonging to the same SMC have the same chord color. Detailed Implementation

[0062] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0063] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as S101, S102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0064] Figure 1 The schematic diagram of the EIT excitation measurement construction method provided in this embodiment of the invention specifically includes: N EIT electrodes, where N is a natural number greater than 1;

[0065] S1: Construct a full-rank column electrode matrix;

[0066] In one embodiment, the construction process of the full-rank electrode matrix is ​​as follows:

[0067] The first column of the full-rank matrix is ​​generated using a random method;

[0068] The first excitation measurement vector matrix is ​​obtained by exciting and measuring the EIT electrode based on the first column full-rank matrix and through the first excitation measurement mode;

[0069] Calculate the rank of the first excitation vector matrix. When the rank of the first excitation vector matrix is ​​equal to the maximum number of independent excitation measurements of the first excitation measurement, the current first excitation vector matrix is ​​a column full-rank electrode matrix.

[0070] In one embodiment, the first excitation measurement mode includes any one of the following: adjacent excitation measurement, relative excitation measurement, and interphase excitation measurement.

[0071] In one specific embodiment, EIT widely employs the four-electrode method to measure voltage. For an N-electrode EIT system, a single excitation measurement can be described as follows:

[0072]

[0073] in, These are the electrode numbers for positive and negative excitation and positive and negative measurement, respectively. I is the excitation current, and v is the measurement voltage. This refers to the potential of the measuring electrode; the excitation electrode is not used as the measuring electrode. A single excitation measurement represents a stimulation and measurement channel (SMC), which is accessed through... A stimulation and measurement pattern (SMP) is a set of SMCs that conform to a specific rule. Skip-n mode is a commonly used SMP, where excitation occurs at n-intervals, and measurements are taken at adjacent electrodes. Due to the superposition theorem and reciprocity theorem, Skip-n mode often contains a large number of redundant SMCs. Taking Skip-0 as an example, this mode has N excitations, each with N-3 measurements, resulting in N(N-3) SMCs. Due to the superposition theorem, one of the N excitations is redundant. Due to the reciprocity theorem, except for the SMCs corresponding to the first and second excitations which are all independent, each subsequent excitation generates one additional redundant SMC. Therefore, the number M of independent SMCs is...

[0074]

[0075] Furthermore, other SMCs can be generated through SMC superposition and reciprocity in Skip-0 mode. Therefore, for an N-electrode EIT system, the maximum number of independent SMCs is... If an SMP has one and only If there are 1 independent SMC, then the SMP is the Max-I SMP.

[0076] To obtain Max-I SMP, this invention proposes a method for screening independent SMCs. One SMC, It consists of four independent electrode components, and N electrodes correspond to N independent components, thus constructing an electrode matrix with full column rank. A vector representing the SMC is generated through E. ,

[0077]

[0078]

[0079]

[0080] in, This represents the independent component corresponding to electrode i. This represents the positional multiplication of vectors, also known as the Hadamard product. Equations (3) and (4) represent the excitation and measurement processes, satisfying the superposition theorem, and equation (5) represents the excitation and measurement processes, satisfying the reciprocity theorem. A suitable E ensures that sm maintains independence consistent with SMC, therefore the matrix... It can reflect the independent redundancy relationship of SMC in SMP. Among them, E is the key to ensuring that sm and SMC have consistent independence.

[0081] In one specific embodiment, the generation of a suitable E is as follows:

[0082] The Hadamard product typically does not introduce new linear correlations (LCs) statistically. However, in special cases such as when E has many zero elements or certain elements of E have fixed proportional relationships, new LCs may be introduced. This causes sm to have additional LCs besides those introduced by superposition and reciprocity theorems, thus compromising independence. To avoid introducing new LCs, E is generated as follows:

[0083] First, a full-rank matrix E is generated randomly. The randomness of E avoids the presence of fixed proportional relationships between zero elements or certain elements, thus largely ensuring that no new LCs are introduced. Furthermore, we utilize known maximum independent SMPs or matrices containing M... max The SMP of an independent SMC avoids these special cases. The method is as follows: using adjacent excitation and adjacent measurement modes, the corresponding SM is generated based on the randomly generated E and formulas (3)(4)(5). ad Matrix, when SM ad rank r ad Equal to M max When we determine that E is the appropriate E at this time, we can use E. a express.

[0084] via r ad Is it equal to M? max The judgment, E a This ensures that the Hadamard product does not introduce new LC, as proven below: For any Max-I SMP, denoted by SMP1, SMP1 has M max Each independent SMC, through the superposition theorem and reciprocity theorem, SMP1 can represent all SMCs of adjacent excitation and adjacent measurement modes. Correspondingly, SM1 and SM ad via E a With the generation of formulas (3), (4), and (5), the superposition theorem and the reciprocity theorem are automatically satisfied. Therefore, SM1 can also express SM. ad , Right now And because of SM ad rank r ad Equal to M max Therefore, r = M max That is, SM1 has no additional LC.

[0085] S2: Generate the excitation measurement vector matrix based on the full-rank column electrode matrix;

[0086] In one embodiment, the excitation measurement vector is generated by calculating the excitation vector and measurement vector using a full-rank column electrode matrix, and the excitation vector and measurement vector are multiplied together to obtain the excitation measurement vector.

[0087] In one embodiment, the calculation of the excitation vector is expressed as follows:

[0088]

[0089] Where s represents the excitation electrode vector. This represents the positive independent component of the excitation electrode. This indicates that the excitation electrode is a negative independent component.

[0090] In one embodiment, the calculation of the measurement vector is represented as follows:

[0091]

[0092] Where m represents the measuring electrode vector. This represents the positive independent component of the measuring electrode. This represents the negative independent component of the measuring electrode.

[0093] In one embodiment, an excitation measurement vector matrix is ​​obtained by generating excitation measurement vectors for N EIT electrodes based on the full-rank column electrode matrix.

[0094] In one specific embodiment,

[0095] S3: Calculate the maximum set of excitation measurement channels from the excitation measurement matrix;

[0096] In one embodiment, the calculation involves eliminating elements from the excitation measurement matrix to obtain the maximum excitation measurement channel geometry; optionally, the elimination includes one or more of the following: Gaussian elimination, column pivoting elimination, and LU decomposition.

[0097] Optionally, the elimination is Gaussian elimination.

[0098] In one embodiment, the method further includes calculating the number of independent excitation measurement channels by calculating the rank of the excitation measurement matrix.

[0099] In one embodiment, the maximum set of excitation measurement channels is represented as a maximal linearly independent set of the excitation measurement matrix.

[0100] In one specific embodiment, for any SMP, calculating the rank of the corresponding SM yields the number of independent SMCs in the SMP. Gaussian elimination of the SM yields the maximal set of independent SMCs, thus obtaining the Max-I SMP. It should be noted that the set of independent SMCs in the SMP corresponds to a maximal linearly independent set in the SM matrix; therefore, the maximal set of independent SMCs is not unique.

[0101] S4: The maximum excitation measurement mode of the EIT electrode is obtained based on the maximum excitation measurement channel set.

[0102] In one embodiment, the method further includes maximum sensitivity calculation, which maximizes the measurement sensitivity of the change in conductivity at the measurement site when performing excitation measurement on the maximum excitation measurement mode, thereby obtaining the maximum sensitive excitation measurement mode;

[0103] Optionally, the maximum sensitive excitation measurement mode is obtained by calculating and sorting the sensitivity of the excitation measurement channels in the EIT electrode, and adding the excitation measurement channels with high sensitivity to the maximum excitation measurement mode.

[0104] In one embodiment, the calculation process for the maximum sensitive excitation measurement mode is as follows:

[0105] S1. Obtain all excitation measurement channels for N EIT electrodes;

[0106] S2. Calculate the sensitivity of each excitation measurement channel and sort the sensitivities.

[0107] S3. Based on sensitivity, starting from the excitation measurement channel corresponding to the first sensitivity matrix, the excitation measurement channels are added sequentially to the maximum excitation measurement mode to obtain the second excitation measurement mode.

[0108] S4. Calculate the excitation measurement vector matrix of the second excitation measurement mode;

[0109] S5. Determine the independence of the added excitation measurement channels based on the excitation measurement vector matrix. If the determination result is independent, retain the channel; otherwise, remove the added excitation measurement channel.

[0110] Repeat steps S3-S5 to obtain the measurement mode with the maximum sensitivity excitation.

[0111] In one embodiment, the maximum sensitivity excitation measurement mode performs two-dimensional EIT measurement and / or three-dimensional EIT measurement;

[0112] Optionally, the maximum sensitive excitation measurement mode is used for three-dimensional EIT measurement.

[0113] In one embodiment, an electrode sheet is fixed to the part to be measured, and excitation and measurement are performed using the electrode sheet in the maximum sensitivity excitation measurement mode or the maximum excitation measurement mode to generate a two-dimensional EIT image or a three-dimensional EIT image.

[0114] In one embodiment, the calculation of the measurement sensitivity to maximize the change in conductivity at the measurement site is obtained by calculating the change in measurement voltage of the excitation measurement channel, the change in conductivity at the measurement site, and the sensitivity of the excitation measurement channel.

[0115] In one embodiment, the calculation of maximizing the measurement sensitivity of the change in conductivity at the measurement site further includes correction, calculating the measurement voltage change of the first excitation measurement mode to obtain the first measurement voltage change, taking the absolute value of the corresponding index of the measurement voltage change of any excitation measurement channel and the first measurement voltage change and adding them to obtain the corrected measurement voltage change, and calculating the measurement sensitivity of maximizing the change in conductivity at the measurement site based on the corrected measurement voltage change of the excitation measurement channel, the change in conductivity at the measurement site and the sensitivity of the excitation measurement channel.

[0116] In one embodiment, the measurement site includes one or more of the following: chest, brain.

[0117] In one embodiment, the addition of the excitation measurement channel is to obtain the second excitation measurement mode by adding one excitation measurement channel at a time.

[0118] In one embodiment, the corresponding index refers to the index of any excitation measurement channel in the matrix (e.g., the i-th row), and then the absolute values ​​of all the values ​​in the i-th row are taken and summed.

[0119] In one embodiment, the excitation measurement channel is added by adding L excitation measurement channels at a time, where L is a natural number greater than 1, to obtain a third excitation measurement mode. The excitation measurement vector matrix of the third excitation measurement mode is calculated and the independence is determined to obtain the maximum sensitive excitation measurement mode.

[0120] Calculate the excitation measurement vector matrix of the third excitation measurement mode and perform independence judgment. If the judgment result is independent, retain it and add L more excitation measurement channels for independence judgment. If the judgment result is not independent, remove any one of the added excitation measurement channels and perform independence judgment again. If independent, add more excitation measurement channels; otherwise, remove them. Repeat the above steps until the maximum sensitive excitation measurement mode is obtained. In the maximum sensitive excitation measurement mode, the excitation measurement channels are mutually independent.

[0121] In one embodiment, the excitation measurement vector matrix of the third excitation measurement mode further includes independent excitation measurement channel filtering. The filtered independent excitation measurement channels are obtained by calculating the excitation measurement vector matrix, thus obtaining the most sensitive excitation measurement mode.

[0122] Optionally, the calculation involves eliminating elements from the excitation measurement vector matrix to obtain the selected independent excitation measurement channels; optionally, the elimination is Gaussian elimination.

[0123] In one specific embodiment, the Max-I SMP, which exhibits the highest sensitivity for brain measurements, is:

[0124] EIT observes changes in boundary voltage. It is estimated to be due to changes in the internal conductivity distribution. , and These are the number of channels in the SMP model and the number of elements in the finite element model (FEM), respectively. The differential EIT forward model is...

[0125]

[0126] in, is the sensitivity matrix (Jacobi matrix), and n is the measurement noise.

[0127] Brain electrophysiological testing (EIT) focuses on changes in brain conductivity. However, the skull significantly impedes electrical current, resulting in only a small portion of the current flowing through brain tissue during excitation measurements, greatly reducing the sensitivity to changes in brain conductivity. We define the sensitivity S of brain conductance measurement (SMC) to changes in brain conductivity as...

[0128]

[0129] This is the sensitivity row vector corresponding to SMC. A change of one unit in electrical conductivity within the brain represents a change of zero in electrical conductivity outside the brain. c This refers to the measured voltage change of the SMC. To maximize S, this invention proposes a Max-I SMP (Dual-M SMP) for maximizing S, calculated as follows:

[0130] For an N-electrode EIT system, the total number of possible SMCs is: ,

[0131]

[0132] in and Let represent the order number. The superposition theorem makes the positive and negative excitations or positive and negative measurement electrodes equivalent after interchange. The reciprocity theorem makes the excitation pair and measurement pair equivalent after interchange. Therefore... Down to,

[0133]

[0134] in and Represents the number of combinations. Generates a full-excitation measurement mode SMP. total ,Include For each SMC, calculate the S of all SMCs and sort them in descending order based on S. Starting with the SMC with the largest S, add the SMCs sequentially to the preset SMP. Use the corresponding SM matrix to determine the independence of the newly added SMCs. If they are independent, keep them; if they are redundant, remove them. Continue until all independent SMCs are added to obtain the Dual-M SMP.

[0135] The calculation of S for all SMCs and the numerous SMC independence checks may raise concerns for operators regarding computational costs. To reduce computational costs, the present invention provides the following optimizations:

[0136] For the calculation of S, based on the adjacent excitation and adjacent measurement mode (the excitation electrode also participates in the measurement), SMP is used. ads This indicates the change in voltage y when the brain's electrical conductivity changes by one unit. ads . y of any SMC c All can be passed through SMP ads of y ads The corresponding index is generated by superimposing the indices, and the absolute value is the corresponding S.

[0137] To address the issue of a large number of SMC independence checks, multiple SMCs are added at once with a large step size. The rank of the SM matrix is ​​then calculated to determine if all independent SMCs have been included. If not, more SMCs are added until all independent SMCs are included. Finally, Gaussian elimination is used to filter the independent SMCs. The Gaussian elimination method employs column-major order scanning and a partial pivot selection strategy to ensure that this method is identical to the Dual-M SMP generated when using a single SMC for independence checks.

[0138] In one specific embodiment, the algorithm flow of Dual-M SMP is as follows: Figure 4 As shown, the SMP is obtained through SMC. SMC is based on the circuit superposition theorem and reciprocity theorem, which abstract the independent redundancy relationship of SMC into a linear correlation relationship sm between matrix vectors. A set of sm vectors from N electrodes yields the SM vector matrix. After calculating the rank of the SM vector matrix or performing Gaussian elimination, the number of SMCs or the set of SMCs is obtained, which is the Max-I SMP. Excitation is applied to all SMCs of N electrodes to obtain the total SMP. Sensitivity calculation is performed on each SMC in the total SMC. The SMC is added to the Max-I SMP, and the independence of the added Max-I SMP is determined by calculating the excitation measurement vector matrix SM. The addition and determination steps are repeated to obtain the maximum sensitive excitation measurement Dual-M SMP.

[0139] In one specific embodiment, the 16-9-7 triple-ring head electrode model (TREM) is designed based on the classic EIT 16-electrode lead ring and EEG 10-20-electrode lead ring, surrounding the entire brain tissue for imaging changes in brain conductivity. It retains the 16-electrode interface to achieve dual-mode 2D and 3D imaging. Electrode positions are indicated by En, where n represents the electrode number; there are 32 in total, as detailed below:

[0140] 1) Draw a main line on the scalp surface, L, connecting the root of the nose to the occipital protuberance. NI The midpoint of the line is E25.

[0141] 2) Let L NI The length is 100%, along L NI The position 10% backward from the root of the nose is E1. An electrode position is set every 20% backward from E1, namely E1, E17, E25, E21 and E9. Continue backward for another 10% to the external occipital protuberance, which is E29.

[0142] 3) The L1 curve is the line connecting E1 and E9 and perpendicular to the midsagittal plane (L... NI The intersection of the plane (where it is located) and the scalp surface, E1-E16 are equidistantly distributed on L1 in a clockwise direction (view from the foot side to the head side), and form the first 16-electrode ring R1.

[0143] 4) Curve L2 is the intersection of the plane defined by E3, E17, and E15 with the scalp surface. The length of L2 is set to 100%. Electrode positions are set every 25% of the distance from E3 to the right, namely E3, E18, E17, E24, and E15. Similarly, curve L3 is the intersection of the plane defined by E5, E25, and E13 with the scalp surface, and L3 sequentially defines E5, E19, E25, E23, and E13; curve L4 is the intersection of the plane defined by E7, E21, and E11 with the scalp surface, and L4 sequentially defines E7, E20, E21, E22, and E11. Among them, E17-E25 form the second 9-electrode ring R2.

[0144] 5) E26-E32 were identified through clear anatomical locations. E26 and E32 are located at the left and right temples, respectively; E27 and E31 are located at the left and right mastoid processes of the temporal bones, respectively. The L5 curve is the intersection of the plane defined by E27, E29, and E31 with the scalp surface. The length of L5 is set to 100%. Electrode positions are placed every 25% of the distance from E27 to the right, in the following order: E27, E28, E29, E30, and E31. E26-E32 form the third 7-electrode ring R3.

[0145] The above constructs a 16-9-7 three-ring head electrode model, as follows: Figure 5 As shown.

[0146] Due to the complex distribution of electrical conductivity in the head, the head was abstracted into a four-layer structure—scalp, skull, cerebrospinal fluid, and brain tissue—to construct a numerical model. The geometric information of these four layers was obtained from the EIDORS EIT toolkit. Electrodes with a 1cm diameter were configured based on the 16-9-7 three-ring electrode model, incorporating a double-layer structure (Ag / AgCl electrode and conductive paste).

[0147] The skull's resistance to electric current is significant. To construct a physical model that more closely resembles the actual conductivity distribution of the human head, plaster casts with similar conductivity were used to simulate the skull. The water tank was divided into three layers by the skull: the outer layer representing the scalp, the inner layer representing the brain tissue, and the conductivity of the scalp and brain tissue layers simulated using an electrolyte solution. Electrodes were configured based on a 16-9-7 three-ring electrode model, made of copper, with 1cm diameter circles as electrode contact surfaces.

[0148] In one specific embodiment, Dual-M SMP is a universal SMP for 2D and 3D imaging. Considering the advantages of dual-mode imaging using the 16-9-7 three-ring electrode model, this invention evaluates the performance of 2D Dual-M SMP based on the 16-electrode ring R1 and with the classic Skip-n SMP as a reference, and evaluates the performance of 3D Dual-M SMP based on the three-ring electrode model and with Max J SMP as a reference.

[0149] The core of the Dual-M SMP algorithm is to compute the SMP. total Measurement sensitivity and independent SMC determination. This paper uses the forward solver of the EIDORS EIT toolkit to calculate measurement sensitivity. The computing environment configuration is as follows: CPU: Intel Core i7-14700KF 20 cores; RAM: 48 GB. For the four-layer structure head finite element model in section 2.3.2, 16-electrode SMP... total The measurement sensitivity calculation time is approximately 3.3 seconds for a 32-electrode SMP. total The measurement sensitivity calculation time is approximately 4.5 seconds.

[0150] To obtain Dual-M SMP, a large number of independent SMCs need to be determined. To optimize the time complexity, this paper proposes an SM matrix to represent the superposition theorem and the reciprocity theorem. Each row of the sensitivity matrix J corresponds to the sensitivity vector of each SMC. Therefore, theoretically, J can replace the SM matrix, but compared to SM, J has certain limitations. This section explains the necessity of the SM matrix by comparing explicit computational cost, time complexity, implicit computational cost, and computational stability.

[0151] In terms of time complexity, the time complexity T for both rank calculation and Gaussian elimination is 1.

[0152]

[0153] Where n and m are the rows and columns of the matrix. The dimension of SM is The dimension of J is Based on formula (10), SM and J are used for the time cost of SMC independence judgment, T SM and TJ The ratio is,

[0154]

[0155] SMP total The number of measurements n meas and n elem Typically much larger than M max Therefore, compared to J, SM can significantly optimize time complexity.

[0156] When using SM for SMC independence determination, the time cost to obtain the Dual-M SMP corresponding to 16 electrodes is 0.1s, and the time cost to obtain the Dual-M SMP corresponding to 32 electrodes is 22s. The specific distribution of Dual-M SMP is as follows: Figure 6 As shown.

[0157] From a computational stability perspective, the actual rank of J may be less than its theoretical rank under the following conditions: 1. The model meshing accuracy is too low or the meshing is uneven; 2. The electrode distances in the model are too close; 3. The number of electrodes in the model is too large. In contrast, the independence of SM is determined solely by the superposition theorem and the reciprocity theorem, and is independent of the specific model, thus exhibiting stronger stability.

[0158] Singular value decomposition (SVD) for rank determination has the advantage of high numerical stability. This method obtains the rank of a matrix by counting the number of singular values ​​greater than the tolerance. The formula for calculating the tolerance (tol) is as follows:

[0159]

[0160] Where max(size(A)) is the maximum dimension of matrix A, norm(A) is the 2 norm of matrix A, and eps returns the machine precision of the parameter. Figure 7 The results show that, compared to J, SM has stronger stability—the minimum singular value is further away from the tolerance, and increasing the number of electrodes does not significantly reduce the size of the singular value.

[0161] In one specific embodiment, the measurement sensitivity of Dual-M SMP:

[0162] The brain measurement sensitivity of the numerical model is calculated by formula (7), and the brain measurement sensitivity of the physical model is obtained by adding electrolytes to achieve a change in brain conductivity of 1 S / m, and then by subtracting the measurement voltage before and after the change. Figure 8 As shown, Dual-M SMP has an absolute advantage in brain measurement sensitivity compared to Skip-n SMP.

[0163] In one specific embodiment, the impact of Dual-M SMP on solving the inverse problem is as follows:

[0164] By analyzing the singular values, condition number, and number of independent SMCs of J corresponding to Dual-M SMP and Skip-n SMP, the impact of SMP on solving the EIT inverse problem is compared. Figure 9 As shown, the Dual-M SMP has larger singular values, which means stronger numerical stability and robustness in the main solution direction. Dual-M SMP also has the smallest condition number and the largest number of independent SMCs, implying weaker ill-conditioning and ill-posedness. Therefore, Dual-M SMP is more advantageous for solving the EIT inverse problem.

[0165] In one specific embodiment, the effect of Dual-M SMP on the uniformity of sensitivity:

[0166] EIT exhibits a characteristic of strong sensitivity in boundary regions and weak sensitivity in interior regions. Dual-M SMP enhances the sensitivity of brain measurements, meaning more electrical current flows through the brain, which helps reduce the sensitivity differences between interior and exterior regions, resulting in a more uniform sensitivity distribution and, consequently, improving the uniformity of AR in EIT. We assessed the impact of SMP on the uniformity of sensitivity using the coefficient of variation (CV) of unit sensitivity. Figure 10 As shown in (a), the Dual-M SMP exhibits a more uniform sensitivity distribution compared to Skip-n SMP. Figure 10 As shown in (b), the sensitivity of Dual-M SMP is more concentrated, that is, the difference in sensitivity between the boundary region and the interior region is smaller.

[0167] In addition to comparing with the Skip-n mode, we also compared the Dual-M SMP (32 electrodes) with the Max-J SMP (32 electrodes). The Max-J SMP generation method is as follows: For an N-electrode system, the electrode above the root of the nose is designated as the reference electrode, and other electrodes are measured based on the reference electrode (the number of measurements is N-3); excluding the reference electrode, the number of possible excitation combinations for the remaining electrodes is... The brain current density for each excitation was calculated. Using the excitation electrodes as vertices and excitation combinations as edges, the negative value of the corresponding brain current density was assigned as the edge weight. The optimal excitation measurement pattern (N-2 excitations and (N-2)*(N-3) total SMCs) was obtained using the minimum spanning tree method. Table 1 shows that, compared to Max J SMP, 3D Dual-M SMP performs better in terms of SMP redundancy, measurement sensitivity, inverse problem solving, and sensitivity uniformity.

[0168] Table 1. Comparison results between Dual-M SMP (32 electrodes) and Max-J SMP (32 electrodes)

[0169]

[0170] In one specific embodiment, Dual-M SMP and AUEIT are proposed for the basic components of 3D brain EIT: excitation measurement and image reconstruction, respectively. Dual-M SMP achieves theoretical maximization of brain measurement sensitivity through the SM matrix. AUEIT improves the AR uniformity of 3D brain EIT through sensitivity compensation and improves the RNG of 3D brain EIT through a single-step feedback strategy.

[0171] Measurement sensitivity is an important indicator for evaluating SMP performance. This invention discovers that Dual-M SMP maximizes measurement sensitivity with the following characteristics:

[0172] Based on a 2D circular region with 16 electrodes, a Dual-M SMP (Sensitive Measurement Multiprocessing) is generated to maximize the measurement sensitivity of the entire region. Figure 11 (As shown). From Figure 11 As can be seen from (b), the characteristics of the SMC with the greatest measurement sensitivity can be described as: 1) small interval between the excitation pair and the measurement pair, and 2) large interval between the two electrodes involved in the excitation or measurement.

[0173] Feature 1) places the positive and negative measuring electrodes near the positive and negative excitation electrodes, respectively, i.e., locations with higher and lower electric field strengths. Feature 2) allows the current to pass through a greater impedance, resulting in a higher potential distribution. The measuring electrodes are in different electric field environments; when the internal conductivity changes, the electric field environment will exhibit greater differences, i.e., greater measurement sensitivity. Compared to Skip-0 SMP, Skip-7 SMP (opposite electrode excitation, adjacent electrode measurement) is generally considered to have greater measurement sensitivity. However, according to the reciprocity theorem, Skip-7 SMP does not escape the framework of adjacent excitation, equivalent to the adjacent excitation and opposite measurement mode, contradicting feature 2). Therefore, the obtained measurement sensitivity is far inferior to Dual-M SMP.

[0174] In Dual-M SMP, the introduction of the SM matrix is ​​a key innovation of this invention. The SM cleverly abstracts the independent redundancy relationship of SMCs into a linear correlation between matrix vectors, achieving efficient screening of independent SMCs. Furthermore, the computation time of SMPtotal measurement sensitivity is shortened using SMPads, successfully compressing the computation time of Dual-M SMP within an acceptable range (30 seconds). Experimental results show that compared to the classic Skip-n mode and Max J SMP, Dual-M SMP exhibits superior performance in SMP redundancy, measurement sensitivity, inverse problem solving, and uniformity.

[0175] In summary, Dual-M SMP performs better in terms of measurement sensitivity optimization and computation time optimization, and minimizes the number of SMCs, providing a speed guarantee for real-time dynamic monitoring of the 3D brain.

[0176] In one specific embodiment, the proof of Dual-M SMP:

[0177] The SMP generated by the above method is denoted as SMP1. This invention uses proof by contradiction to prove that SMP1 is the largest independent SMP with the highest measurement sensitivity.

[0178] Suppose that SMPDM is the largest independent SMP with the greatest measurement sensitivity, and SMP1 does not have the greatest measurement sensitivity, that is, there is at least one SMC in SMP1 that does not belong to SMPDM, denoted as SMC1.

[0179] In an SMPDM, there exists a set of minimum number of SMCs, denoted as G. G can represent SMC1. If SMC1 is added to G, denoted as G1, then the SMCs in G1 can represent each other. G1 is independent of other SMCs in the SMPDM.

[0180] SMC1 is the SMC with the lowest measurement sensitivity in G1. Because if it is not the lowest, removing the lowest SMC and keeping SMC1 as the new G would result in a maximum independent SMP with even higher measurement sensitivity.

[0181] When generating SMP1, independent SMCs are included in descending order of measurement sensitivity. Therefore, when including the independent space represented by G1, SMC1 has the lowest measurement sensitivity and will be included last. However, at this point, SMC1 is no longer independent, so SMC1 does not belong to SMP1. With assumption contradiction.

[0182] Therefore, SMP1 has the greatest measurement sensitivity.

[0183] In one embodiment, EIT measurement voltage change data is obtained based on the Dual-M SMP maximum sensitive excitation measurement mode, and EIT image reconstruction is performed based on the measurement transformer data. The image reconstruction algorithm is AUEIT.

[0184] The AUEIT reconstruction process is as follows:

[0185] Acquire EIT measurement voltage change data;

[0186] A reconstruction matrix R is constructed, and the reconstruction result is obtained by calculating the measured voltage change data through the reconstruction matrix R.

[0187] Wherein, the reconstruction matrix R is the error calculation that minimizes the change in desired conductivity and the change in reconstructed conductivity;

[0188] The desired conductivity change is obtained by first acquiring conductivity change data and constructing a modification matrix D, and then modifying the conductivity change data using the modification matrix D. The modification matrix D modifies the conductivity change using consensus indicators of the EIT image. The consensus indicators include one or more of the following: AR, PE, RNG, RES, and SD.

[0189] In one embodiment, the modification matrix D is the desired modification of the EIT measurement unit by a point spread function, where the EIT measurement unit is a tissue unit at different locations in the measurement site.

[0190] In one embodiment, the point diffusion function divides the organizational units at different locations into regions, including a first region and a second region. The first region is the region where the organizational units at different locations are less than or equal to the diffusion radius from the diffusion center, and the second region is the region where the organizational units at different locations are greater than the diffusion radius from the diffusion center. The first region and the second region are then modified as desired.

[0191] The formula for constructing the modification matrix D is:

[0192]

[0193]

[0194] in, It is a point spread function, p0 is The diffusion center, p represents the center position of the FEM element in the finite element model, and r is diffusion radius, The matrix represents the modification matrix, where i and j represent the electrodes of the EIT, s is a fuzzy control parameter, and b represents the control parameter for the positive compensation amplitude.

[0195] In one embodiment, the modification matrix is ​​modified by RNG through... Positive compensation of RNG yields the results of changes in conductivity after RNG modification.

[0196] In one embodiment, the AR in the consensus index further includes sensitivity compensation, through which uniform AR is obtained, and conductivity variation is modified by uniform AP through a modification matrix.

[0197] Optionally, a uniform AR can be obtained by constructing a sensitivity compensation matrix to perform sensitivity compensation on the AP.

[0198] In one embodiment, the control parameter c for sensitivity compensation is used to control the loss in the internal region, which is a region whose distance from the edge is greater than the diffusion radius, and the diffusion radius is the radius of the diffusion function of the modification matrix.

[0199] Optionally, the control parameters for sensitivity compensation are calculated by training the reconstruction magnitude of the internal region;

[0200] Optionally, the process of reconstructing the magnitude of the training internal region is as follows:

[0201] Obtain the sensitivity vector of each unit in the internal region;

[0202] Sort the unit sensitivity vectors and select n units from the sorted unit sensitivity vectors as training points, where n is a natural number greater than 1.

[0203] A perturbation is generated centered on each training point, and the measured voltage at each training point is calculated.

[0204] By setting the control parameters for initial sensitivity compensation, and calculating the reconstruction matrix based on the disturbance, measured voltage, and initial sensitivity compensation control parameters, the reconstructed conductivity change is obtained.

[0205] The maximum reconstruction amplitude of each disturbance is obtained based on the reconstructed conductivity change.

[0206] The control parameter c for sensitivity compensation is obtained by calculating the loss between the maximum and minimum values ​​of the maximum reconstruction amplitude;

[0207] Optionally, the loss is calculated by constructing a loss function between the maximum and minimum values ​​of the maximum reconstruction amplitude and the control parameter c of sensitivity compensation, and then solving for the control parameter c of sensitivity compensation.

[0208] In one specific embodiment, the GERIT algorithm is consistent with the idea of ​​Wiener filtering; it seeks an optimal filter or reconstruction matrix R. The change in conductivity recovered by the measured voltage change y , The change in conductivity is related to the expected change. The mean squared error is the smallest. This is the result of modifying the change in conductivity distribution x based on expert consensus on EIT imaging, using a matrix. Describe the modification process. Assume x follows a distribution. The noise n of SMC follows a distribution The calculation of R is as follows:

[0209]

[0210] R satisfies, ,Right now

[0211]

[0212] Because x and n are independent , , The expression for R is,

[0213]

[0214] In this paper, for the sake of universality, it is assumed that the elements of x and n are independent of each other, the conductivity of x is set to be uniform, and cell volume correction is performed to make its effect on R the same, i.e. , Let I be the identity matrix of the corresponding dimension. Formula (13) can be written as:

[0215]

[0216] Regularization parameters A larger R value means that more noise is taken into account, resulting in stronger suppression of n during imaging. However, the fidelity of other image parameters will decrease. These are key parameters for reconstruction.

[0217] Noise Figure (NF) is used as a tool The choice of NF is now widely accepted. NF is the signal-to-noise ratio of the filter R output. Compared with input signal-to-noise ratio The ratio,

[0218]

[0219] The input signal is y, which is caused by a one-unit change in brain conductivity. , The measurement noise follows a normal distribution with a standard deviation of 0.5. As input noise , and The reconstruction results are respectively used as output signals and output noise .

[0220] The present invention notes that in formula (14) This refers to the average measurement sensitivity S of SMP. SMP ,therefore, ( This can be eliminated during the calculation of NF, therefore let ),get,

[0221]

[0222] The larger the value of R, the stronger the suppression of n. Therefore, under stable noise levels, about Monotonically increasing. Given a suitable NF, an approximate solution to equation (15) can be obtained using the bisection method. At this time This is the desired or acceptable outcome. For Skip-0 SMP, Andy's group recommends NF = 0.5, meaning that at this point... It is recommended. When this invention applies Dual-MSMP, it is achieved through S... SMP Correcting NF ensures that the algorithm comparison is performed here. The following will proceed.

[0223] In one specific embodiment, the paradigmatic expression of D and the single-step feedback RNG suppression method are as follows:

[0224] The R of the classic regularized reconstruction algorithm is,

[0225]

[0226] in, It is a diagonal matrix, and the diagonal elements are the weights of the corresponding SMCs. It is a regularization constraint on x. It is the regularization parameter. When , , When the equations are equal, equations (13) and (16) are equal (the difference between the two equations equals 0). It can be seen that the improvement of GREIT is mainly achieved through matrix D, so D is another key parameter for reconstruction.

[0227] D modifies x using consensus metrics from EIT images. These metrics, ranked by importance, are: 1) uniform AR, 2) small and uniform position error (PE), 3) small RNG, 4) small and uniform resolution (RES), and 5) small shape variability (SD). Bartłomiej Grychtol constructed a paradigmatic expression for D based on the Sigmoid function, which to some extent achieves the modification of consensus metrics. For further optimization, this invention constructs a new paradigmatic expression for D based on an improved Mexican Hat function.

[0228]

[0229]

[0230] in, It is a point spread function, p0 is The diffusion center, p represents the center position of the FEM element. r is... diffusion radius, , hour, It reflects the characteristics of the desired image. s is a fuzz control parameter; the larger s is, the clearer the EIT image, but RNG will increase. hour, Positive compensation is applied to RNG, with the compensation magnitude set as a. In formula (17), b is the control parameter for positive compensation. The relationship between a and b is...

[0231]

[0232] Changes in brain electrical conductivity are more complex, and opposite changes in conductivity often indicate lesions of different natures, which has a significant impact on clinical judgment. Therefore, inhibiting RNG in brain EIT is necessary. To inhibit RNG, we propose a single-step feedback strategy: Let... Obtain the temporary reconstruction matrix R t Calculate R t The resulting average RNG, -a t (a t This is the normalized amplitude referenced to the maximum positive AR. Then, let... To obtain a new reconstruction matrix R, R is obtained while maintaining R t While improving clarity, it will also enhance RNG's suppression capabilities.

[0233] In one specific embodiment, AR uniformity is improved through sensitivity compensation:

[0234] Uniform AR (Area of ​​Resonance) is the most important consensus indicator for EIT (Enhanced Imaging Technology). This invention analyzes the causes of EIT AR non-uniformity from the perspective of regularization parameter selection. Definition , , express eigenvalues, calculate the reconstruction matrix ,

[0235]

[0236] It is the right inverse of J, assuming the noise is 0, that is ,

[0237]

[0238] We obtained the desired change in conductivity. However, the maximum number of independent numbers of J is only Much smaller than n elemEquations (20) and (21) are not well-posed. To make them well-posed, Greater than It is necessary. This invention defines... , ,

[0239]

[0240]

[0241] Formula (23) shows that, The larger, by That is, the greater the sensitivity, the worse the AR uniformity.

[0242] EIT sensitivity distribution is obtained through the unit sensitivity vector s. describe,

[0243]

[0244] Where i is the cell number, j is the SMC number, and s represents the sum of squares of the measured voltage changes when the conductivity of the cell changes by one unit. Since the measured voltage originates from the boundary, the sensitivity distribution is characterized by strong sensitivity in the boundary region and weak sensitivity in the interior region. To improve uniformity, this invention introduces sensitivity compensation, constructing a sensitivity compensation matrix S. ,

[0245]

[0246] Where c is a control parameter for sensitivity compensation, generally... , A uniform consensus image reconstruction algorithm (AUEIT) based on S is constructed.

[0247]

[0248] The selection of 'c' is an optimization process: considering the loss caused by the point spread function in the edge region, in the inner region, i.e., the region farther from the edge than the spread radius 'r', based on the unit sensitivity vector 's', after sorting by sensitivity, we uniformly select n corresponding training points at intervals, and generate a perturbation centered on each training point. And calculate the corresponding measured voltage. Given an initial value c0, calculate R and This allows us to obtain the maximum reconstruction amplitude for each training perturbation. Construct the loss function.

[0249]

[0250] The loss(c) approximates to be a concave function with respect to c. A stable solution can be quickly obtained by applying common optimization algorithms. During the optimization process, the normal function (NF) remains unchanged.

[0251] In one specific embodiment, the AUEIT image reconstruction algorithm is evaluated based on the above numerical and physical models: NF determines the key parameters of EIT and significantly affects AR uniformity. For Skip-0 SMP, NF = 0.5 is recommended. By comparing the sensitivity of Skip-0 SMP and Dual-M SMP, the measurement sensitivity of Dual-M SMP is about 50 times that of Skip-0 SMP. According to formula (15), the NF of 2D Dual-M SMP is corrected to 0.01. The experimental experience of this invention shows that 3D and 2D Dual-M SMP are consistent in the choice of NF, that is, when NF = 0.01, the SNRout of 3D brain EIT images is acceptable. A series of NFs were set around 0.01, namely 0.001, 0.0025, 0.005, 0.01, 0.025, and 0.05, to study the effect of NF on AR uniformity. Experiments showed that as NF increases, AR uniformity deteriorates, but compared to GREIT, AUEIT exhibits smaller AR differences within and at the edges, maintaining relatively stable uniformity.

[0252] 500-600 perturbations were uniformly applied to the brain. The quality of 3D images reconstructed by EIT (Electronic Image Processing) was evaluated using consensus metrics (AR, PE, RES, SD, RNG) for GREIT (GREIT-S) based on the Sigmoid function, GREIT (GREIT-M) based on the Mexican Hat function, and AUEIT. NF=0.01, and to ensure unbiased evaluation, the evaluation was first conducted against a uniform conductivity background. Experimental results show that GREIT-S, GREIT-M, and AUEIT have similar performance in terms of positional error, resolution, and shape variation. The specific difference in RNG distribution between GREIT-M and GREIT-S is that GREIT-M's RNG decreased from 0.531 to 0.335, a reduction of 37%, compared to GREIT-S. AUEIT enhances RNG to some extent, but due to its single-step feedback strategy, it still shows some improvement over GREIT-S. The study results show the specific differences between AUEIT and GREIT-S in AR distribution. Compared with GREIT-S, the coefficient of variation of AR in AUEIT decreased from 0.103 to 0.0685, a reduction of 33%.

[0253] In one specific embodiment, a simulation experiment was conducted to evaluate the uniformity of AR in AUEIT under the background of cranial conductivity. Using GREIT-S as a reference, a numerical model was used to comprehensively evaluate the brain AR uniformity of AUEIT under the background of cranial conductivity. Experimental results showed that, compared to GREIT-S, the coefficient of variation of AR in AUEIT decreased from 0.1822 to 0.1183, a reduction of 35%, demonstrating that the AR uniformity of AUEIT is still superior to GREIT-S under the background of cranial conductivity.

[0254] The present invention also discloses a computer program product or system, including a computer program that, when executed by a processor, implements the above-described EIT stimulus measurement construction method steps.

[0255] Figure 2 The schematic diagram of the EIT excitation measurement construction system provided in this embodiment of the invention specifically includes: N EIT electrodes, where N is a natural number greater than 1;

[0256] Building unit: Construct a full-rank column electrode matrix;

[0257] Excitation unit: The excitation measurement vector matrix is ​​obtained by generating the excitation measurement vector based on the column full-rank electrode matrix;

[0258] SMC unit: Calculates the maximum set of excitation measurement channels from the excitation measurement matrix;

[0259] SMP unit: The maximum excitation measurement mode of the EIT electrode is obtained based on the maximum excitation measurement channel set.

[0260] Figure 3 An embodiment of the present invention provides a schematic diagram of a computer device, specifically including:

[0261] A memory and a processor; the memory is used to store program instructions; the processor is used to invoke the program instructions, when any of the above-described EIT stimulus measurement construction methods are executed.

[0262] The present invention also discloses a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, is any of the above-described EIT stimulus measurement construction methods.

[0263] The verification results of this verification embodiment show that assigning inherent weights to indications can improve the performance of this method compared to the default settings. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated; the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of this embodiment. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0264] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0265] The computer device provided by the present invention has been described in detail above. For those skilled in the art, there will be changes in the specific implementation and application scope based on the ideas of the embodiments of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for constructing EIT excitation measurements, characterized in that, include: There are N EIT electrodes, where N is a natural number greater than 1; Construct a full-rank column electrode matrix; The excitation measurement vector matrix is ​​obtained by generating the excitation measurement vector based on the full-rank column electrode matrix; The maximum set of excitation measurement channels is obtained by calculating the excitation measurement matrix; The maximum excitation measurement mode of the EIT electrode is obtained based on the maximum excitation measurement channel set.

2. The EIT excitation measurement construction method according to claim 1, characterized in that, The construction process of the full-rank electrode matrix is ​​as follows: The first column of the full-rank matrix is ​​generated using a random method; The first excitation measurement vector matrix is ​​obtained by exciting and measuring the EIT electrode based on the first column full-rank matrix and through the first excitation measurement mode; Calculate the rank of the first excitation vector matrix. When the rank of the first excitation vector matrix is ​​equal to the maximum number of independent excitation measurements of the first excitation measurement, the current first excitation vector matrix is ​​a column-full-rank electrode matrix. Optionally, the first excitation measurement mode includes any one of the following: adjacent excitation measurement, relative excitation measurement, and interphase excitation measurement.

3. The EIT excitation measurement construction method according to claim 1, characterized in that, The excitation measurement vector is generated by calculating the excitation vector and measurement vector using a column full-rank electrode matrix, and the excitation vector and measurement vector are multiplied together to obtain the excitation measurement vector. Optionally, the calculation of the excitation vector is expressed as: Where s represents the excitation electrode vector. This represents the positive independent component of the excitation electrode. This indicates that the excitation electrode is a negative independent component; Optionally, the calculation of the measurement vector is expressed as: Where m represents the measuring electrode vector. This represents the positive independent component of the measuring electrode. This represents the negative independent component of the measuring electrode; Optionally, an excitation measurement vector matrix is ​​obtained by generating excitation measurement vectors for N EIT electrodes based on the full-rank column electrode matrix; Optionally, the method further includes calculating the number of independent excitation measurement channels by calculating the rank of the excitation measurement matrix; Optionally, the maximum set of excitation measurement channels is represented as a maximal linearly independent set of the excitation measurement matrix.

4. The EIT excitation measurement construction method according to claim 1, characterized in that, The method also includes maximum sensitivity calculation, which maximizes the measurement sensitivity of the change in conductivity at the measurement site when performing excitation measurement on the maximum excitation measurement mode, and obtains the maximum sensitive excitation measurement mode; Optionally, the maximum sensitive excitation measurement mode is obtained by calculating and sorting the sensitivity of the excitation measurement channels in the EIT electrode, and adding the excitation measurement channels with high sensitivity to the maximum excitation measurement mode. Optionally, the calculation process for the maximum sensitive excitation measurement mode is as follows: S1. Obtain all excitation measurement channels for N EIT electrodes; S2. Calculate the sensitivity of each excitation measurement channel and sort the sensitivities. S3. Based on sensitivity, starting from the excitation measurement channel corresponding to the first sensitivity matrix, the excitation measurement channels are added sequentially to the maximum excitation measurement mode to obtain the second excitation measurement mode. S4. Calculate the excitation measurement vector matrix of the second excitation measurement mode; S5. Determine the independence of the added excitation measurement channels based on the excitation measurement vector matrix. If the determination result is independent, retain them. Conversely, removing the added excitation measurement channels, Repeat steps S3-S5 to obtain the measurement mode with the maximum sensitivity to excitation. Optionally, two-dimensional EIT measurement and / or three-dimensional EIT measurement are performed based on the maximum sensitive excitation measurement mode; Optionally, three-dimensional EIT measurements are performed based on the maximum sensitive excitation measurement mode.

5. The EIT excitation measurement construction method according to claim 4, characterized in that, The calculation of the measurement sensitivity to maximize the change in conductivity at the measurement site is obtained by calculating the change in measurement voltage of the excitation measurement channel, the change in conductivity at the measurement site, and the sensitivity of the excitation measurement channel. Optionally, the calculation of maximizing the measurement sensitivity of the change in conductivity at the measurement site further includes correction. The measurement voltage change of the first excitation measurement mode is calculated to obtain the first measurement voltage change. The absolute values ​​of the corresponding indices of the measurement voltage change of any excitation measurement channel and the first measurement voltage change are taken and added to obtain the corrected measurement voltage change. The measurement sensitivity of maximizing the change in conductivity at the measurement site is calculated based on the corrected measurement voltage change of the excitation measurement channel, the change in conductivity at the measurement site, and the sensitivity of the excitation measurement channel. Optionally, the measurement site includes one or more of the following: chest, brain.

6. The EIT excitation measurement construction method according to claim 4, characterized in that, The addition of the excitation measurement channel is to obtain the second excitation measurement mode by adding one excitation measurement channel at a time. Optionally, the excitation measurement channel is added by adding L excitation measurement channels at a time, where L is a natural number greater than 1, to obtain a third excitation measurement mode. The excitation measurement vector matrix of the third excitation measurement mode is calculated and the independence is judged to obtain the maximum sensitive excitation measurement mode.

7. The EIT excitation measurement construction method according to claim 6, characterized in that, The excitation measurement vector matrix of the third excitation measurement mode also includes independent excitation measurement channel screening. The selected independent excitation measurement channels are obtained by calculating the excitation measurement vector matrix, thus obtaining the maximum sensitive excitation measurement mode. Optionally, the calculation involves performing elimination on the excitation measurement vector matrix to obtain the selected independent excitation measurement channels; Optionally, the elimination includes one or more of the following: Gaussian elimination, LU decomposition, and column pivoting elimination; Optionally, the elimination is Gaussian elimination.

8. A computer program product comprising a computer program or instructions, characterized in that, The computer program or instructions are executed by a processor to implement the EIT excitation measurement construction method according to any one of claims 1-7.

9. A computer device comprising a memory, a processor, and a computer program or instructions stored in the memory, characterized in that, The computer program or instructions are executed by a processor to implement the EIT excitation measurement construction method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, The computer program or instructions are executed by a processor to implement the EIT excitation measurement construction method according to any one of claims 1-7.

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