A method, device, and program product for constructing a discretized model of a region of human tissue

Abstract

This application relates to the field of intelligent healthcare, specifically to a method, device, and program product for constructing a discretized model of human tissue regions. It includes: S1, acquiring an image of human tissue, calculating the minimum bounding box of the tissue region (MUB), and obtaining a region boundary map; S2, generating a mesh covering the region boundary map based on any vertex of the region boundary map, and obtaining a region mesh map; S3, filtering the pixels of the region mesh map for effective pixel blocks to obtain effective pixel block areas; S4, calculating the total area of ​​the region mesh map and minimizing the absolute value of the difference between the total effective pixel block area and the total area of ​​the region mesh map; S5, updating vertices in the neighborhood of the current vertex to obtain updated vertices, and repeating S2-S4 based on the updated vertices to obtain an absolute value set; S6, filtering the region mesh map corresponding to the vertex with the largest absolute value in the absolute value set. This application has significant application value.

Description

Technical Field

[0001] This application relates to the field of intelligent healthcare, specifically to a method, device, program product, and computer-readable storage medium for constructing a discretized model of human tissue regions. Background Technology

[0002] The EIT system assesses physiological and pathological changes in the human body based on changes in electrical impedance. Therefore, the accuracy of impedance measurement in the EIT system directly affects the assessment results. Traditional EIT accuracy assessment devices use a resistor network to evaluate or calibrate the EIT system's accuracy. However, traditional devices can only periodically change the overall resistance value to simulate changes in lung respiration, failing to represent the depth of breathing or the breathing patterns associated with lung diseases. Therefore, they cannot verify the accuracy of the EIT system at the algorithmic level. Furthermore, the distribution of the impedance network in traditional calibration plates is not based on real human data, so its measurement results cannot accurately correspond to disease outcomes. Summary of the Invention

[0003] To address the above problems, this invention provides a method for constructing a discretized model of human tissue regions, specifically including: S1. Obtain human tissue images, calculate the minimum bounding box of the tissue region, and obtain the region boundary map; S2. Generate a mesh covering the region boundary map based on any vertex of the region boundary map to obtain a region mesh map; S3. Filter the pixels of the region grid map to obtain the effective pixel block area; S4. Calculate the total area of ​​the region grid map and minimize the absolute value of the difference between the total area of ​​the effective pixel blocks and the total area of ​​the region grid map; S5. Update the vertices in the neighborhood of the current vertex to obtain the updated vertex. Repeat S2-S4 based on the updated vertex to obtain the absolute value set. S6. Filter the region mesh map corresponding to the vertex with the largest absolute value in the absolute value set to obtain the discretized model of human tissue region.

[0004] Optionally, the effective pixel block refers to a block in which the area of ​​the chest region accounts for more than 50%. Optionally, the effective block pixel area filtering process is as follows: Obtain the grid diagram; Traverse each pixel block in the grid and calculate the intersection area between the pixels in each pixel block and the chest region; Calculate the area ratio of the intersection area to the area of ​​each pixel block. When the area ratio is 50%, it is marked as a valid pixel block. Optionally, the method further includes resolution setting, determining the number of pixels on the horizontal axis or the number of pixels on the vertical axis based on the horizontal axis length or the vertical axis length of the region boundary map to obtain the resolution; the grid generated by the region boundary map is calculated by the horizontal axis length / vertical axis length and the resolution. Optionally, the generation starts from a vertex and calculates the side length using the horizontal axis length / vertical axis length and resolution to obtain a mesh covering the boundary map of the region; Optionally, the tissue region includes any one or more of the following: thoracic region, abdominal region, and brain region; Optionally, the tissue region is the thoracic cavity region.

[0005] The purpose of this invention is to provide a method for discretizing human tissue regions, comprising: Acquire images of human tissue regions; The human tissue region image is input into the human tissue region discretization model in the above-mentioned method for constructing the human tissue region discretization model to obtain a region grid map, which is used as the region discretization result map.

[0006] The purpose of this invention is to provide a grid-based impedance calculation method, comprising: Obtain a region grid map, which contains N grids, and obtain the conductivity of each grid, where N is a natural number greater than 1; Choose any grid and calculate the grid stiffness matrix based on the electrical conductivity of the grid; The impedance between any two points in the mesh is obtained by inverting the stiffness matrix.

[0007] Optionally, the calculation process of the stiffness matrix is ​​as follows: Obtain the mesh and connect any two diagonal vertices of the mesh to obtain two triangular elements; Calculate the grid electric field using grid conductivity; The electric field of the mesh is solved based on the triangular element to obtain the current and potential between the three vertices of the triangular element; The first stiffness matrix is ​​constructed based on the current and potential between the three vertices. The stiffness matrix of the triangular element is obtained by solving the first stiffness matrix using Taylor series. The mesh stiffness matrix is ​​obtained based on the stiffness matrices of two triangular elements; Optionally, the grid is a square grid.

[0008] The purpose of this invention is to provide a method for simulating impedance distribution based on real tissue conductivity, comprising: Obtain a mesh map of human tissue regions and the electrical conductivity of actual human tissue regions; The region is divided into grid cells corresponding to each organization based on the regional grid map. Each grid cell corresponding to each organization includes L grids, where L is a natural number greater than 1. The impedance of each grid is calculated based on the conductivity of the real human tissue region and the above-mentioned grid-based impedance calculation method to obtain the impedance distribution of the human tissue region. Optionally, the tissue region includes any one or more of the following: thoracic region, abdominal region, and brain region; Optionally, the tissue region is the thoracic cavity region.

[0009] The purpose of this invention is to provide a circuit system for simulating regional impedance changes, comprising: Power supply module: Used to provide power to the impedance module; Electrical impedance module: includes a standard electrical impedance network module and a variable electrical impedance network module, wherein the standard electrical impedance network module and the variable electrical impedance network module are arranged in a region grid obtained by the above-mentioned human tissue region discretization method; Control module: The impedance distribution of the human tissue region is obtained by simulating the impedance distribution based on the actual tissue conductivity as described above, and the resistance values ​​of the standard impedance network module and the variable resistor network in the impedance module are controlled.

[0010] The purpose of this invention is to provide a computer program product, which includes a computer program or instructions, wherein the computer program or instructions are executed by a processor to implement the above-mentioned method for constructing a human tissue region discretization model, or to implement the above-mentioned method for discretizing human tissue regions, or to implement the above-mentioned grid-based impedance calculation method, or to implement the above-mentioned method for simulating impedance distribution based on real tissue conductivity.

[0011] The purpose of this invention is to provide a computer device, which includes a memory, a processor, and a computer program or instructions stored in the memory. The computer program or instructions are executed by the processor to implement the above-described method for constructing a human tissue region discretization model, or to implement the above-described method for discretizing human tissue regions, or to implement the above-described grid-based impedance calculation method, or to implement the above-described method for simulating impedance distribution based on real tissue conductivity.

[0012] The purpose of this invention is to provide a computer-readable storage medium storing a computer program or instructions, wherein the computer program or instructions are executed by a processor to implement the above-described method for constructing a discretized model of human tissue regions, or to implement the above-described method for discretizing human tissue regions, or to implement the above-described grid-based impedance calculation method, or to implement the above-described method for simulating impedance distribution based on real tissue conductivity.

[0013] Advantages of this invention: 1. To address the inability of traditional calibration boards to verify the accuracy of EIT systems at the algorithm level, this invention constructs a human body impedance model composed of variable resistors and variable capacitors. This model accurately simulates the impedance changes of human tissues under different conditions, such as lung respiration. The respiratory impedance varies with the depth of each breath and in cases of lung disease. The key technical challenge is accurately simulating these impedance changes. To address this, this invention proposes a method for constructing a discretized human tissue model. This method discretizes the human tissue, forming the smallest computational unit, which allows for dynamic and precise control of the impedance changes within this smallest computational unit, thereby improving simulation accuracy.

[0014] 2. For discretized human tissue regions, the technical problem that needs to be solved is how to construct the equivalent circuit of the smallest unit to represent the impedance change of the smallest computational unit. To this end, the present invention provides a method for calculating grid impedance. The impedance of a grid is obtained by calculating the stiffness matrix of the grid and then inverting it, thereby obtaining the impedance distribution of the entire human tissue region. The impedance of each grid is an equivalent circuit constructed by variable resistors or variable capacitors. By dynamically changing the resistance and / or capacitance to form different impedances, the accuracy of the simulated impedance is effectively improved.

[0015] 3. To improve the reliability of the simulated impedance, this invention calculates the stiffness matrix based on the conductivity of the actual human tissue region when simulating the impedance of the human tissue region, thus forming an accurate and reliable simulated impedance distribution. Attached Figure Description

[0016] 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.

[0017] Figure 1 A schematic diagram of the construction method of the human tissue region discretization model provided in the embodiment of the present invention; Figure 2 A schematic diagram of a grid-based impedance calculation method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a method for simulating impedance distribution based on real tissue conductivity provided in an embodiment of the present invention; Figure 4 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 5 The present invention provides a positive fractal segmentation based on the contours of the thoracic cavity and lungs, and an equivalent circuit of one of the square units; Figure 6 This is a schematic diagram of the electrical impedance network module provided in an embodiment of the present invention; Figure 7 The present invention provides an embodiment of the EIT image obtained by acquiring the EIT data of the constructed circuit and performing image reconstruction. Detailed Implementation

[0018] 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.

[0019] 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.

[0020] Figure 1 A schematic diagram of the method for constructing a discretized model of human tissue regions provided in this embodiment of the invention, specifically including: S1. Obtain human tissue images, calculate the minimum bounding box of the tissue region, and obtain the region boundary map; In one embodiment, the tissue region includes any one or more of the following: thoracic region, abdominal region, and brain region; In one embodiment, the tissue region is the thoracic cavity region.

[0021] In one embodiment, the smallest circumscribed quadrilateral is a square, resulting in a square region boundary map. A mesh covering the square region boundary map is generated based on any vertex of the square region boundary map, resulting in a region mesh map.

[0022] S2. Generate a mesh covering the region boundary map based on any vertex of the region boundary map to obtain a region mesh map; In one embodiment, a mesh covering the region boundary map is generated starting from any one of the four boundary vertices of the region boundary map; Optionally, a grid covering the region boundary map is generated starting from the lower left corner vertex of the region boundary map.

[0023] S3. Filter the pixels of the region grid map to obtain the effective pixel block area; In one embodiment, the effective pixel block refers to a block in which the area of ​​the chest region accounts for more than 50%. Optionally, the effective block pixel area filtering process is as follows: Obtain the grid diagram; Traverse each pixel block in the grid and calculate the intersection area between the pixels in each pixel block and the chest region; Calculate the area ratio of the intersection area in each pixel block. When the area ratio is 50%, it is marked as a valid pixel block.

[0024] In one embodiment, the method further includes resolution setting, determining the number of pixels on the horizontal axis or the number of pixels on the vertical axis based on the horizontal axis length or the vertical axis length of the region boundary map to obtain the resolution; the grid generated by the region boundary map is calculated by the horizontal axis length / vertical axis length and the resolution.

[0025] Optionally, the generation starts from a vertex and calculates the side length using the horizontal axis length / vertical axis length and resolution to obtain a mesh covering the boundary map of the region.

[0026] S4. Calculate the total area of ​​the region grid map and minimize the absolute value of the difference between the total area of ​​the effective pixel blocks and the total area of ​​the region grid map; S5. Update the vertices in the neighborhood of the current vertex to obtain the updated vertex. Repeat S2-S4 based on the updated vertex to obtain the absolute value set. S6. Filter the region mesh map corresponding to the vertex with the largest absolute value in the absolute value set to obtain the discretized model of human tissue region.

[0027] In one specific embodiment, the discretization of a real human thoracic cavity is based on square segmentation: Using the distribution of tissues in the human thoracic cavity (including the heart, lungs, etc.) as a reference, square units corresponding to various tissues are determined, such as... Figure 5 As shown. The core steps of discretization are as follows: 1) Boundary box determination: Calculate the minimum bounding box B of the chest region.

[0028] 2) Resolution setting: Based on the horizontal axis length L of the bounding box B, determine the number of pixels r (resolution) on the horizontal axis.

[0029] 3) Mesh initialization: Starting from the bottom left vertex O of bounding box B. initial Starting from the origin, generate a side with length of A square grid G ​​covers the bounding box B.

[0030] 4) Valid block filtering: Traverse each pixel block P in grid G: Calculate the area of ​​intersection between P and the chest region S, |P ∩ S|.

[0031] Retention conditions: If If the area of ​​the chest region within the block is greater than 50%, then P is marked as a valid block. .

[0032] 5) Mesh optimization: Objective function: Minimize the total area of ​​the effective block and the total area S of the chest region. total The absolute value of the difference η: . Optimization variable: y-coordinate of the grid starting point.

[0033] Optimization process: In O initial Adjust y within the neighborhood, repeat steps 3)-4) to generate a new grid and calculate its η, iteratively search for the optimal starting point O that maximizes η. optimal .

[0034] 6) Final segmentation: Using the optimal starting point O optimal The final mesh G is generated using resolution r. final The effective block is the final square discretization result of the chest region.

[0035] In one embodiment, when the human tissue is the thoracic cavity region, the process of constructing the discretized model of the thoracic cavity is as follows: S1. Obtain an image of human thoracic cavity tissue, calculate the minimum bounding box of the chest region, and obtain the thoracic cavity boundary map. S2. Generate a mesh covering the thoracic cavity boundary map based on any vertex of the thoracic cavity boundary map to obtain a thoracic cavity mesh map; S3. Filter the effective pixel blocks of the thoracic mesh to obtain the effective pixel block area; S4. Calculate the total area of ​​the thoracic mesh map and minimize the absolute value of the difference between the total area of ​​the effective pixel blocks and the total area of ​​the thoracic mesh map; S5. Update the vertices in the neighborhood of the current vertex to obtain the updated vertex. Repeat S2-S4 based on the updated vertex to obtain the absolute value set. S6. Based on the set of absolute values, filter the vertices corresponding to the largest absolute values, and obtain the thoracic mesh diagram based on the corresponding vertices.

[0036] This invention provides a method for discretizing human body regions, including: Acquire images of human tissue regions; The human tissue region image is input into the human tissue region discretization model in the above-mentioned method for constructing the human tissue region discretization model to obtain a region grid map, which serves as the region discretization result map.

[0037] In one embodiment, the human tissue region includes one or more of the following: the thoracic region, the abdominal region, and the brain region; In one embodiment, the tissue region is the thoracic cavity region.

[0038] Figure 2 A grid-based impedance calculation method provided for this embodiment includes: Obtain a region grid map, which contains N grids, and obtain the conductivity of each grid, where N is a natural number greater than 1; In one embodiment, the region grid map is obtained by the above-described human body region discretization method or by the above-described human tissue region discretization model construction method.

[0039] Choose any grid and calculate the grid stiffness matrix based on the electrical conductivity of the grid; The impedance between any two points in the mesh is obtained by inverting the stiffness matrix.

[0040] In one embodiment, the stiffness matrix is ​​calculated as follows: Obtain the mesh and connect any two diagonal vertices of the mesh to obtain two triangular elements; Calculate the grid electric field using grid conductivity; The electric field of the mesh is solved based on the triangular element to obtain the current and potential between the three vertices of the triangular element; The first stiffness matrix is ​​constructed based on the current and potential between the three vertices. The stiffness matrix of the triangular element is obtained by solving the first stiffness matrix using Taylor series. The mesh stiffness matrix is ​​obtained based on the stiffness matrices of two triangular elements; Optionally, the grid is a square grid.

[0041] Figure 3 This invention provides a method for simulating impedance distribution based on real tissue conductivity, comprising: Obtain a mesh map of human tissue regions and the electrical conductivity of actual human tissue regions; The region is divided into grid cells corresponding to each organization based on the regional grid map. Each grid cell corresponding to each organization includes L grids, where L is a natural number greater than 1. The impedance of each grid is calculated based on the conductivity of the real human tissue region and the above-mentioned grid-based impedance calculation method to obtain the impedance distribution of the human tissue region. In one embodiment, the tissue region includes any one or more of the following: thoracic region, abdominal region, and brain region; Optionally, the tissue region is the thoracic cavity region.

[0042] In one embodiment, the human tissue region mesh map is obtained by the above-described human region discretization method or by the above-described human tissue region discretization model construction method.

[0043] In one specific embodiment, a circuit model is constructed based on the actual impedance distribution of the chest: By filling the square cell containing the corresponding tissue with the actual electrical conductivity characteristics of human thoracic cavity tissue, a thoracic cavity model with a realistic impedance distribution is obtained.

[0044] For any square element in the thoracic cavity model Its four vertices are labeled a, b, c, and d, and the conductivity of this unit is... To further illustrate how the square conductivity distribution can be equivalent to the resistance between the four sides, auxiliary lines are added between vertices a and c, such as... Figure 5 As shown.

[0045] According to Laplace's principle, the electric field of EIT can be expressed as follows:

[0046] in, For conductivity distribution, As coordinates, For potential distribution, It is the normal vector. The current density injected into the electrode.

[0047] Solving the Laplace equation using the variational principle yields the relationship between the potential and current of the triangular unit with vertices abc (and acd).

[0048] in, Let be the stiffness matrix, describing the relationship between the electric potential and current between the three vertices a, b, and c. Its composition is as follows: , , Let be the linear coefficients of the Taylor series expansion at position (x,y), according to It can be known that , , . Let the electric potential at the three vertices be . . The current at the three vertices, It is determined by the applied current density Decision A e This refers to a square, where i and j are the sequence numbers of the triangular units.

[0049] The above analysis shows that the stiffness matrix Determines the electric potential and current The relationship between these elements, i.e., the impedance relationship, requires further expansion of the elements in the stiffness matrix.

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] in, Let be the area of ​​the triangular unit (vertex abc).

[0056] As can be seen from the above formula, due to the use of square discretization, the stiffness matrix elements between vertices a and c are... This is manifested in actual physics as an impedance of 0 ohms between two nodes.

[0057] Similarly, the stiffness matrix of the ACD triangular element at the vertex can be obtained. Then, through node assembly, i.e., the stiffness matrix elements between two identical nodes are the sum of the stiffness matrix element values ​​calculated by the two adjacent elements, the stiffness matrix of the entire thoracic cavity model can be obtained. The relationship between the nodal potentials of all square cell elements and the applied boundary current.

[0058] in, It is a current vector that includes the EIT boundary current excitation.

[0059] Then, by analyzing the stiffness matrix Find the reverse. This allows us to obtain the impedance value between any two vertices of a square within the entire thoracic cavity. These impedance values ​​can be represented in circuit terms as the potential distribution caused by the actual impedance of the entire thoracic cavity tissue.

[0060] This invention provides a circuit system for simulating regional impedance changes, comprising: Power supply module: Used to provide power to the impedance module; Electrical impedance module: includes a standard electrical impedance network module and a variable electrical impedance network module, wherein the standard electrical impedance network module and the variable electrical impedance network module are arranged using a region mesh obtained by the above-mentioned human thoracic cavity discretization method; Control module: The impedance distribution of the human tissue region is obtained by simulating the impedance distribution based on the actual tissue conductivity as described above, and the resistance values ​​of the standard impedance network module and the variable resistor network in the impedance module are controlled.

[0061] In one embodiment, the system is used to simulate impedance changes in the lung region, initialize system data, acquire breathing commands, determine the breathing mode based on the breathing commands, and the control module controls the resistance values ​​of the standard impedance network module and the variable resistance network in the impedance module to obtain the breathing data (simulated lung breathing impedance data) corresponding to the breathing mode. The breathing mode includes any one or more of the following: shallow breathing mode, deep breathing mode, pendulum breathing mode, and lesion breathing mode.

[0062] In one embodiment, the impedance network module includes a standard impedance network module and a variable impedance network module. The variable impedance network module includes N variable resistors and S variable capacitors, and the standard impedance network module includes L standard resistors. N, S, and L are natural numbers greater than 1. The standard impedance network module and the variable impedance network module are arranged in a simulated manner based on the actual lung region to obtain a simulated lung. The standard impedance network module and the variable impedance network module in the simulated lung are controlled based on the breathing pattern to obtain simulated lung breathing data. Optionally, the impedance network module further includes an electrode contact impedance module disposed around the periphery of the simulated lung to simulate impedance changes on the skin surface. Based on the breathing pattern, the electrode contact impedance module, the standard impedance network module, and the variable impedance network module in the simulated lung are controlled to obtain simulated lung breathing data.

[0063] In one embodiment, the electrode contact impedance module is connected to a standard impedance network module, the standard impedance network module is connected to a variable impedance network module, the standard impedance network module surrounds the variable impedance network module, and the electrode contact impedance module is located on the outermost periphery. Figure 6 As shown.

[0064] In one embodiment, the breathing cycle includes: an inhalation period, an exhalation period, and a apnea period.

[0065] In one embodiment, when the breathing mode is shallow breathing mode, the control module controls the impedance network module to simultaneously generate periodic shallow breathing data for the simulated left and right lungs; when the breathing mode is deep breathing mode, the control module controls the impedance network module to simultaneously generate periodic deep breathing data for the simulated left and right lungs; when the breathing mode is pendulum breathing mode, the control module controls the impedance network module to simultaneously generate breathing data for both lungs after delaying the left lung data by m seconds, where m is a natural number greater than or equal to 1; when the breathing mode is lesion breathing mode, the control module controls the impedance network module to generate breathing data for the simulated left and right lungs, and the impedance value of the simulated lesion point remains unchanged; Optionally, the periodic shallow breathing data and periodic deep breathing data are simulated by increasing the resistance and capacitance values ​​of the variable resistor and variable capacitor in the variable impedance network module; the increase in the resistance and capacitance values ​​of the periodic deep breathing data is greater than that of the periodic shallow breathing data. Optionally, the right lung data is delayed by m seconds after the left lung data is delayed by m seconds; then the respiratory impedance changes of the left and right lungs are simulated by increasing the resistance value of the variable resistor and the capacitance value in the variable impedance network module. Optionally, the lesion breathing mode first determines the location of the lesion in the simulated lung, keeping the impedance value of the lesion location unchanged, and then simulates the breathing data of the right and left lungs excluding the lesion location by increasing the variable resistance value and variable capacitance value in the variable impedance network module.

[0066] In one embodiment, when the breathing mode is shallow breathing mode, the resistance of each variable resistor in the variable impedance network module is increased from 100 ohms to 300 ohms, and the capacitance of each variable capacitor is increased from 7 pF to 10 pF.

[0067] In one embodiment, when the breathing mode is deep breathing mode, the resistance of each variable resistor in the variable impedance network module is increased from 100 ohms to 1000 ohms, and the capacitance of each variable capacitor is increased from 7 pF to 13 pF.

[0068] In one embodiment, when the breathing mode is pendulum breathing mode, the right lung data phase lags the left lung data by 1 second, the resistance of each variable resistor in the right and left lungs is increased from 100 ohms to 300 ohms, and the capacitance of each variable capacitor is increased from 7 pF to 10 pF.

[0069] In one embodiment, when the breathing mode is the lesion breathing mode, the lesion location is obtained, and the lesion area of ​​the impedance network module is determined based on the lesion location. The resistance value of each variable resistor in the right lung and the left lung is increased from 100 ohms to 300 ohms, and the capacitance value of each variable capacitor is increased from 7 pF to 10 pF. The variable resistance or variable capacitance of the lesion area remains unchanged.

[0070] In one specific embodiment, the circuit system for simulating regional impedance changes provided by the present invention enables the calibration board to switch between shallow breathing mode, deep breathing mode, pendulum breathing mode, and lung nodule breathing mode, wherein... 1. Shallow breathing mode: In shallow breathing mode, the resistance of each variable resistor changes from 100 ohms to 300 ohms, and the capacitance of each variable capacitor changes from 7pF to a maximum of 10pF. The impedance switching frequency is 100Hz per second.

[0071] 2. Deep Breathing Mode: In deep breathing mode, the resistance of each variable resistor changes from 100 ohms to 1000 ohms, and the capacitance of each variable capacitor changes from 7pF to a maximum of 13pF. The impedance switching frequency is 100Hz per second.

[0072] 3. Pendulum Breathing Pattern: In pendulum breathing mode, the resistance of each variable resistor in the left and right lungs varies from 100 ohms to 300 ohms, and the capacitance of each variable capacitor varies from 7pF to a maximum of 10pF. The right lung data is adjusted to lag the left lung data by 1 second, and its impedance switching frequency is 100Hz per second.

[0073] 4. Breathing patterns at the lesion site: In the lesion-based breathing mode, the resistance of each variable resistor in the left and right lungs varies from 100 ohms to 300 ohms, and the capacitance of each variable capacitor varies from 7 pF to a maximum value of 10 pF. During respiration, the impedance data at the lesion location remains constant.

[0074] In one specific embodiment, a circuit simulating lung impedance changes is designed, comprising three parts: an impedance network module, a control module, and a power supply module. The impedance network module consists of an electrode contact impedance module, a standard impedance network module, and a variable impedance network. The variable impedance network module comprises a digital potentiometer and an electrically controlled variable capacitor. In this invention, the digital potentiometer is selected from Analog Devices' AD8403 chip, and the electrically controlled variable capacitor is selected from Analog Devices' MAX1474 chip. The control module writes all resistance and capacitance data to different digital potentiometers via an SPI interface within one cycle. In this embodiment, the frequency of a complete breath is 4 seconds, and the data writing frequency is 100Hz; therefore, the size of the array is 4. 100 186.

[0075] Finally, the various breathing modes of the present invention are realized. When the device is working in shallow breathing mode, the control module controls the resistance value to change from 100Ω to 300Ω and the capacitance value to change from 7pF to 10pF; both lungs start to change at the same time, and one breathing cycle is 4s.

[0076] When the device is in deep breathing mode, the control module controls the resistance value to change from 100Ω to 1000Ω and the capacitance value to change from 7pF to 13pF; both lungs begin to change simultaneously, and one breathing cycle is 4s.

[0077] In one specific embodiment, the breathing mode of the present invention is set. When the device is working in pendulum breathing mode, the control module first loads shallow breathing data, and then the control module performs phase transformation on the right side data, that is, the right lung data phase is delayed by 1 second, and then the bilateral lung data are changed synchronously.

[0078] When this device operates in lesion breathing mode, in this embodiment, a digital potentiometer and a variable capacitor on the left are selected as the lesion area. The control module first loads shallow breathing data, and then controls the impedance network (excluding the digital potentiometer and variable capacitor) to change. This mode can be used to test the EIT system's ability to detect small lesions.

[0079] In one embodiment, the circuit system simulating regional impedance changes further includes an EIT system evaluation, which involves performing EIT imaging on simulated respiratory data and evaluating the accuracy of the EIT system through the EIT imaging.

[0080] Imaging of the EIT system yielded the following results: Figure 7 As shown, it can be seen that it accurately reflects the contours of the lungs. This greatly helps in evaluating the accuracy of the EIT system. Since the conductivity distribution of the thoracic cavity is known, the impedance distribution ratio of the generated EIT image is fixed, thus allowing for the measurement of the EIT system's accuracy. 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 method for constructing a human tissue region discretization model, or executes the above-described method for discretizing human tissue regions, or executes the above-described grid-based impedance calculation method, or executes the above-described method steps for simulating impedance distribution based on real tissue conductivity.

[0081] The human thoracic cavity discretization system provided in this embodiment of the invention specifically includes: Acquisition Unit: Acquires images of human tissue regions Discrete unit: The human thoracic cavity tissue image is input into the human tissue region discretization model in the above-mentioned human tissue region discretization model construction method to obtain a region grid map, which serves as the region discretization result map.

[0082] This invention provides a system for simulating impedance distribution based on real tissue conductivity, comprising: Acquisition module: Acquires a mesh map of human tissue regions and the electrical conductivity of actual human tissue regions; Grid module: Based on the regional grid map, the region is divided into grid cells corresponding to each organization. Each grid cell corresponding to each organization includes L grids, where L is a natural number greater than 1. Impedance module: Based on the conductivity of the real human tissue region and the above-mentioned grid-based impedance calculation method, the impedance of each grid is calculated to obtain the impedance distribution of the human tissue region.

[0083] Figure 4 An embodiment of the present invention provides a schematic diagram of a computer device, specifically including: The system includes a memory and a processor; the memory is used to store program instructions; the processor is used to invoke the program instructions, which are executed when any of the above-mentioned methods for constructing a human tissue region discretization model are executed, or the above-mentioned methods for discretizing human tissue regions are executed, or the above-mentioned grid-based impedance calculation method is executed, or the above-mentioned method for simulating impedance distribution based on real tissue conductivity is executed.

[0084] The present invention also discloses a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs any of the above-described methods for constructing a human tissue region discretization model, or performs the above-described human tissue region discretization method, or performs the above-described grid-based impedance calculation method, or performs the above-described method for simulating impedance distribution based on real tissue conductivity.

[0085] 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.

[0086] 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.

[0087] 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 a discretized model of human tissue regions, characterized in that, include: S1. Obtain human tissue images, calculate the minimum bounding box of the tissue region, and obtain the region boundary map; S2. Generate a mesh covering the region boundary map based on any vertex of the region boundary map to obtain a region mesh map; S3. Filter the pixels of the region grid map to obtain the effective pixel block area; S4. Calculate the total area of ​​the region grid map and minimize the absolute value of the difference between the total area of ​​the effective pixel blocks and the total area of ​​the region grid map; S5. Update the vertices in the neighborhood of the current vertex to obtain the updated vertex. Repeat S2-S4 based on the updated vertex to obtain the set of absolute values. S6. Filter the region mesh map corresponding to the vertex with the largest absolute value in the absolute value set to obtain the discretized model of human tissue region.

2. The method for constructing a discretized model of human tissue regions according to claim 1, characterized in that, The effective pixel block refers to a block in which the area of ​​the chest region accounts for more than 50%. Optionally, the effective block pixel area filtering process is as follows: Obtain the grid diagram; Traverse each pixel block in the grid and calculate the intersection area between the pixels in each pixel block and the chest region; Calculate the area ratio of the intersection area to the area of ​​each pixel block. When the area ratio is 50%, it is marked as a valid pixel block. Optionally, the method further includes resolution setting, determining the number of pixels on the horizontal axis or the number of pixels on the vertical axis based on the horizontal axis length or the vertical axis length of the region boundary map to obtain the resolution; the grid generated by the region boundary map is calculated by the horizontal axis length / vertical axis length and the resolution. Optionally, the generation starts from a vertex and calculates the side length using the horizontal axis length / vertical axis length and resolution to obtain a mesh covering the boundary map of the region; Optionally, the tissue region includes any one or more of the following: thoracic region, abdominal region, and brain region; Optionally, the tissue region is the thoracic cavity region.

3. A method for discretizing human tissue regions, characterized in that, include: Acquire images of human tissue regions; The human tissue region image is input into the human tissue region discretization model in the method for constructing the human tissue region discretization model according to any one of claims 1-2 to obtain a region mesh map, which is used as the region discretization result map.

4. A grid-based impedance calculation method, characterized in that, include: Obtain a region grid map, which contains N grids, and obtain the electrical conductivity of each grid, where N is a natural number greater than 1; Choose any grid and calculate the grid stiffness matrix based on the electrical conductivity of the grid; The impedance between any two points in the mesh is obtained by inverting the stiffness matrix.

5. The grid-based impedance calculation method according to claim 4, characterized in that, The calculation process of the stiffness matrix is ​​as follows: Obtain the mesh and connect any two diagonal vertices of the mesh to obtain two triangular elements; Calculate the grid electric field using grid conductivity; The electric field of the mesh is solved based on the triangular element to obtain the current and potential between the three vertices of the triangular element; The first stiffness matrix is ​​constructed based on the current and potential between the three vertices. The stiffness matrix of the triangular element is obtained by solving the first stiffness matrix using Taylor series. The mesh stiffness matrix is ​​obtained based on the stiffness matrices of two triangular elements; Optionally, the grid is a square grid.

6. A method for simulating impedance distribution based on real tissue conductivity, characterized in that, include: Obtain a mesh map of human tissue regions and the electrical conductivity of actual human tissue regions; The region is divided into grid cells corresponding to each organization based on the regional grid map. Each grid cell corresponding to each organization includes L grids, where L is a natural number greater than 1. The impedance of each grid is calculated based on the conductivity of the real human tissue region and the grid-based impedance calculation method described in any one of claims 4-5, thereby obtaining the impedance distribution of the human tissue region. Optionally, the tissue region includes any one or more of the following: thoracic region, abdominal region, and brain region; Optionally, the tissue region is the thoracic cavity region.

7. A circuit system for simulating regional impedance changes, characterized in that, include: Power supply module: Used to provide power to the impedance module; Electrical impedance module: includes a standard electrical impedance network module and a variable electrical impedance network module, wherein the standard electrical impedance network module and the variable electrical impedance network module are arranged using a region mesh obtained by the human tissue region discretization method described in claim 3; Control module: The impedance distribution of the human tissue region is obtained by the method of simulating impedance distribution based on real tissue conductivity as described in claim 6, and the resistance values ​​of the standard impedance network module and the variable resistor network in the impedance module are controlled.

8. A computer program product comprising a computer program or instructions, characterized in that, The computer program or instructions are executed by the processor to implement the method for constructing a human tissue region discretization model according to any one of claims 1-2, or to implement the human tissue region discretization method according to claim 3, or to implement the grid-based impedance calculation method according to any one of claims 4-5, or to implement the method for simulating impedance distribution based on real tissue conductivity according to claim 6.

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 the processor to implement the method for constructing a human tissue region discretization model according to any one of claims 1-2, or to implement the human tissue region discretization method according to claim 3, or to implement the grid-based impedance calculation method according to any one of claims 4-5, or to implement the method for simulating impedance distribution based on real tissue conductivity according to claim 6.

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 the processor to implement the method for constructing a human tissue region discretization model according to any one of claims 1-2, or to implement the human tissue region discretization method according to claim 3, or to implement the grid-based impedance calculation method according to any one of claims 4-5, or to implement the method for simulating impedance distribution based on real tissue conductivity according to claim 6.

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