Modular electrical impedance lung imaging method, device, computer device and storage medium

Abstract

The present application relates to the field of electrical impedance imaging, and discloses a modular electrical impedance lung imaging method and device, computer equipment and storage medium, the method comprising: adjusting the gas supply state of a gas supply device according to a first ventilation control parameter; the first ventilation control parameter is generated by a fuzzy control component; the gas supply device is used for supplying gas to the lungs; collecting first electrical impedance data of the lungs through the gas supply device; generating a first lung electrical impedance image according to the electrical impedance data; processing the first lung electrical impedance image through a preset neural network to generate a first network output parameter; inputting the first network output parameter into the fuzzy control component to generate a second ventilation control parameter, and generating a second lung electrical impedance image according to the second ventilation control parameter. The present application can improve the resolution and accuracy of lung electrical impedance imaging.

Description

Technical Field

[0001] This invention relates to the field of electrical impedance imaging, and more particularly to a modular electrical impedance lung imaging method, apparatus, computer equipment, and storage medium. Background Technology

[0002] Electrical Impedance Tomography (EIT) is a non-invasive technique that reconstructs images of tissues within the human body by targeting the resistivity distribution inside the body. The human body is a large bioelectric conductor, and each tissue and organ has a certain impedance. When a local organ in the human body becomes diseased, the impedance of that local area will inevitably be different from that of other parts. Therefore, disease of human organs can be diagnosed by measuring impedance.

[0003] However, current electrical impedance tomography (EIT) devices all use external electrodes for data acquisition. Even small perturbations in the boundary voltage can cause significant changes in the output image, necessitating high precision in EIT devices. Another challenge lies in the limited amount of information. While increasing the number of electrodes can increase the amount of measurement data, current algorithms would rapidly increase the computational load. Furthermore, the most achievable physiological impedance change currently only causes a 10% variation in the measurement voltage; therefore, it is generally considered that the measurement hardware of an EIT device must have at least 0.1% accuracy. Achieving high precision and high resolution is the main challenge of EIT technology. Summary of the Invention

[0004] Therefore, it is necessary to provide a modular electrical impedance tomography (EIT) method, apparatus, computer equipment, and storage medium to address the aforementioned technical problems, thereby improving imaging resolution and accuracy.

[0005] A modular electrical impedance tomography (EIT) method for lung imaging includes:

[0006] The gas delivery status of the gas delivery device is adjusted according to a first ventilation control parameter; the first ventilation control parameter is generated by a fuzzy control component; the gas delivery device is used to deliver gas to the lungs;

[0007] The first electrical impedance data of the lungs is collected through the gas delivery device;

[0008] A first lung electrical impedance image is generated based on the first electrical impedance data;

[0009] The first lung electrical impedance image is processed by a preset neural network to generate the first network output parameters;

[0010] The first network output parameters are input into the fuzzy control component to generate second ventilation control parameters, and a second pulmonary electrical impedance image is generated based on the second ventilation control parameters.

[0011] A modular electrical impedance tomography (EIT) lung imaging device, comprising:

[0012] The device adjustment module is used to adjust the gas delivery status of the gas delivery device according to the first ventilation control parameter; the first ventilation control parameter is generated by the fuzzy control component; the gas delivery device is used to deliver gas to the lungs;

[0013] The data acquisition module is used to acquire the first electrical impedance data of the lungs through the gas delivery device;

[0014] A first imaging module is used to generate a first lung electrical impedance image based on the first electrical impedance data;

[0015] The parameter generation module is used to process the first lung electrical impedance image through a preset neural network and generate the first network output parameters;

[0016] The second imaging module is used to input the output parameters of the first network into the fuzzy control component to generate second ventilation control parameters, so as to generate a second pulmonary electrical impedance image based on the second ventilation control parameters.

[0017] A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor, when executing the computer-readable instructions, implements the modular electrical impedance tomography (EI) method described above.

[0018] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described modular electrical impedance tomography (EIA) method for lung imaging.

[0019] The aforementioned modular electrical impedance tomography (EIT) method, apparatus, computer equipment, and storage medium adjust the gas delivery state of the gas delivery device through a first ventilation control parameter; thereby acquiring first electrical impedance data of the lungs through the gas delivery device; generating a first lung electrical impedance image based on the electrical impedance data; processing the first lung electrical impedance image through a preset neural network to generate a first network output parameter; inputting the first network output parameter into a fuzzy control component to generate a second ventilation control parameter; and finally generating a second lung electrical impedance image based on the second ventilation control parameter. This method of generating ventilation control parameters through a preset neural network and a fuzzy control component continuously generates lung electrical impedance images, thereby improving the resolution and accuracy of the lung electrical impedance images. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the 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.

[0021] Figure 1 This is a schematic flowchart of a modular electrical impedance tomography method for lung imaging according to an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of a modular electrical impedance tomography (EIT) lung imaging device according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In one embodiment, such as Figure 1 As shown, a modular electrical impedance tomography (EI) method for lung imaging is provided, including the following steps S10-S50.

[0026] S10. Adjust the gas delivery status of the gas delivery device according to the first ventilation control parameter; the first ventilation control parameter is generated by the fuzzy control component; the gas delivery device is used to deliver gas to the lungs.

[0027] Understandably, the modular electrical impedance tomography (EIT) lung imaging device includes a fuzzy control component, an EIT data acquisition component, and an EIT imaging component. The fuzzy control component includes artificial neural networks and fuzzy PID control components. The EIT data acquisition component can refer to the component that sends, receives, and processes data from the gas delivery system. The EIT imaging component can refer to the component that constructs an image based on the data output from the EIT data acquisition component.

[0028] The first ventilation control parameter can refer to the control parameter generated by the fuzzy control component, which is used to control the turbine speed and / or proportional valve ratio of the gas delivery device. The gas delivery device can refer to a device connected to a modular electrical impedance tomography (TEB) system for delivering gas, including but not limited to ventilators, anesthesia machines, and monitors.

[0029] The fuzzy control component generates the first ventilation control parameters based on the image generated by the EIT imaging component. The gas delivery status can be represented by the pressure value of the output gas.

[0030] Specifically, the first ventilation control parameter is input into the gas transmission equipment, and the turbine speed and / or proportional valve control value of the gas transmission equipment are controlled according to the first ventilation control parameter to adjust the pressure value of the gas output by the gas transmission equipment.

[0031] S20. Collect the first electrical impedance data of the lungs through the gas delivery device.

[0032] S30. Generate a first lung electrical impedance image based on the first electrical impedance data.

[0033] Understandably, the gas delivery device can be used to acquire the boundary voltage of the lungs and generate first electrical impedance data. A modular electrical impedance lung imaging device can generate a first lung electrical impedance image based on the first electrical impedance data. The first lung electrical impedance image can refer to an EIT boundary voltage curve, a lung imaging image, and a curve showing the dynamic area parameter changes in the lung imaging.

[0034] Specifically, the modular electrical impedance tomography (EIT) lung imaging device includes an EIT data acquisition component and an EIT imaging component. The EIT data acquisition component receives the boundary voltage (first impedance data) of the lungs sent by the gas delivery device, calculates the lung voltage information, lung conductivity, and lung imaging area value, etc., based on the boundary voltage, and then plots a first lung impedance image based on the first impedance intermediate data. The first lung impedance image includes, but is not limited to, an EIT boundary voltage curve, a lung imaging image, and a curve showing the dynamic area parameter change of the lung imaging.

[0035] S40. Process the first lung electrical impedance image through a preset neural network to generate the first network output parameters.

[0036] S50. Input the first network output parameters into the fuzzy control component to generate second ventilation control parameters, so as to generate a second pulmonary electrical impedance image based on the second ventilation control parameters.

[0037] Understandably, the preset neural network can be configured according to actual needs, such as an RBF neural network (Radial Basis Function). The first network output parameters can refer to parameters affecting tidal volume, control values ​​of proportional valves, and ventilation pressure values, etc.

[0038] Specifically, the first pulmonary electrical impedance image is processed by a preset artificial neural network to obtain first network output parameters. These parameters are then input into a fuzzy control component, which processes them to obtain second ventilation control parameters. These second ventilation control parameters then control the air delivery status of the gas delivery device. The gas delivery device also collects second electrical impedance data, and a second pulmonary electrical impedance image is generated based on this data. Repeating steps S10-S50 continuously generates new pulmonary electrical impedance images.

[0039] This embodiment processes previous lung electrical impedance images using a preset neural network to generate control parameters. These parameters are then used to control the gas delivery status of the gas delivery device. The gas delivery device collects new electrical impedance data and generates corresponding lung electrical impedance images. This process is repeated continuously to generate new lung electrical impedance images, thereby improving the resolution and accuracy of lung electrical impedance imaging.

[0040] Optionally, step S20, namely, collecting the first electrical impedance data of the lungs through the gas delivery device, includes:

[0041] S201. Detect the network connectivity status of the gas transmission equipment.

[0042] Understandably, network connectivity status can refer to the data connection status between the gas transmission equipment and the EIT data acquisition component.

[0043] Specifically, the EIT data acquisition component sends a connection status detection signal to the gas transmission equipment and detects the data connection status between the gas transmission equipment and the EIT data acquisition component based on the feedback information received in response to the connection status detection signal.

[0044] S202. If the gas transmission equipment is in a network connection state, then the acquisition parameters are sent to the gas transmission equipment so that the gas transmission equipment can obtain the first impedance data according to the acquisition parameters.

[0045] Specifically, if the gas transmission equipment and the EIT data acquisition component are connected, the EIT data acquisition component sends a data acquisition parameter signal to the gas transmission equipment. The gas transmission equipment receives the signal and acquires the required parameters to obtain the first impedance data. The EIT data acquisition component receives the first impedance data from the gas transmission equipment and processes it using an algorithm to obtain intermediate first impedance data. If the gas transmission equipment and the EIT data acquisition component are not connected, the EIT data acquisition component resends a connection request to the gas transmission equipment and issues a reminder signal to the user that the gas transmission equipment and the EIT data acquisition component are currently disconnected.

[0046] This embodiment ensures the connectivity between the EIT data acquisition component and the gas transmission equipment by detecting the network connectivity status of the EIT data acquisition component.

[0047] Optionally, step S20, namely, collecting the first electrical impedance data of the lungs through the gas delivery device, further includes:

[0048] S203. Send a collection command to the gas transmission equipment.

[0049] S204. The gas delivery device responds to the acquisition command and acquires the boundary voltage of the lungs according to the first acquisition parameters to obtain the first electrical impedance data.

[0050] Understandably, the first acquisition parameter can refer to the pre-input excitation current value. The boundary voltage can refer to the boundary voltage under the action of the excitation current.

[0051] Specifically, the EIT data acquisition component obtains specific information about the gas delivery equipment. Based on the specific information about the gas delivery equipment, the EIT data acquisition component obtains a preset excitation current value. The EIT data acquisition component sends an acquisition command and the preset excitation current value to the gas delivery equipment. The lung boundary voltage acquired through the acquisition command and the preset excitation current value obtained by the gas delivery equipment is the first impedance data.

[0052] In this embodiment, the EIT data acquisition component sends acquisition commands to the gas transmission equipment. The gas transmission equipment executes the acquisition commands according to the first acquisition parameters, thereby realizing the connectivity between the EIT equipment and different gas transmission equipment and improving the convenience of the EIT equipment.

[0053] Optionally, step S30, where the first pulmonary electrical impedance image includes a first conductivity change image, and the generation of the first pulmonary electrical impedance image based on the first electrical impedance data includes:

[0054] S301. Determine whether the number of times the impedance data is collected is greater than one.

[0055] S302. If the number of times the impedance data is collected is greater than one, the change value of the field conductivity is calculated, and the first conductivity change image is generated.

[0056] Understandably, the change in field conductivity can refer to the change in lung conductivity compared to the previous lung conductivity when the number of impedance data acquisitions is greater than one. For example, in the Nth (N>1)th acquisition of impedance data, the EIT device (i.e., modular electrical impedance imaging device) processes the data using an algorithm to obtain the Nth lung conductivity based on the Nth acquisition of impedance data, and generates the Nth conductivity change map based on the change in lung conductivity between the Nth and N-1th acquisitions.

[0057] Specifically, if the EIT device determines that the number of times the impedance data is collected is greater than one, the EIT device calculates the current lung conductivity based on the collected impedance data using an algorithm, and generates a conductivity change image based on the change value between the current lung conductivity and the previous lung conductivity, and outputs it to the EIT imaging component.

[0058] This embodiment generates the first conductivity change image on the EIT device, which more intuitively shows the change in lung conductivity.

[0059] Optionally, step S30, i.e., after the first pulmonary electrical impedance image includes a first dynamic area change curve and the calculation of the change value of field conductivity is completed and the first conductivity change image is generated, further includes:

[0060] S303. Calculate the change value of the target surface and generate the first dynamic area change curve.

[0061] Understandably, the target area can refer to the size of the lung area imaged on the EIT device. The dynamic area change curve can refer to the dynamic change curve of the lung imaging area on the EIT device. For example, in the case of the Nth (N>1) acquisition of impedance data, the EIT device processes the data using an algorithm to obtain the Nth lung image based on the Nth acquisition of impedance data, and generates the Nth dynamic area change curve based on the change values ​​of the lung imaging area in the Nth and N-1th acquisitions.

[0062] This embodiment generates the first dynamic area change curve on the EIT device, which more intuitively shows the change in lung imaging area.

[0063] Optionally, step S30, after determining whether the number of times the impedance data is collected is greater than one, further includes:

[0064] S304. If the number of times the impedance data is collected is equal to one, then the initial voltage is set according to the impedance data, and a voltage curve is generated.

[0065] Specifically, if the EIT device determines that the number of impedance data acquisitions is equal to one, the EIT device extracts the lung voltage at this time based on the acquired impedance data, which is the initial voltage, and generates a voltage curve based on the initial voltage and outputs it to the EIT imaging component.

[0066] This embodiment generates the voltage curve on the EIT device, which more intuitively shows the changes in lung voltage values.

[0067] Optionally, step S10, where the fuzzy control component includes a tuning algorithm component and a PID control component, involves inputting the first network output parameters into the fuzzy control component to generate second ventilation control parameters, including:

[0068] S101. Obtain an idealized image, compare the idealized image with the first lung electrical impedance image, and generate a first image comparison result.

[0069] S102. The first network output parameters are processed by the tuning algorithm component to generate the first tuning parameters.

[0070] S103. The first image comparison result and the first tuning parameter are processed by the PID control component to generate the second ventilation control parameter.

[0071] Understandably, the idealized image refers to the lung imaging image that best matches the measured lung image, obtained by an algorithm after acquiring the initial lung imaging image and initial parameters in an artificial neural network. The first image comparison result refers to the difference obtained after comparing the first lung electrical impedance image with the idealized image.

[0072] Specifically, the artificial neural network acquires an initial lung imaging image and initial parameters, calculates an idealized image using an algorithm, acquires a first lung electrical impedance image, and compares the idealized image with the first lung electrical impedance image to generate a first image comparison result. The first network output parameters are processed by a tuning algorithm component to generate first tuning parameters. Finally, the PID control component processes the first image comparison result and the first tuning parameters to generate second ventilation control parameters.

[0073] This embodiment improves the resolution and accuracy of lung electrical impedance imaging by continuously comparing idealized and actual images through an artificial neural network and adjusting parameters through a fuzzy control component.

[0074] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0075] In one embodiment, a modular electrical impedance tomography (EIT) lung imaging device is provided, which corresponds one-to-one with the modular electrical impedance tomography (EIT) lung imaging method described in the above embodiments. For example... Figure 2 As shown, the modular electrical impedance tomography (EIT) lung imaging device includes an equipment adjustment module 10, a data acquisition module 20, a first imaging module 30, a parameter generation module 40, and a second imaging module 50. Detailed descriptions of each functional module are as follows:

[0076] The device adjustment module 10 is used to adjust the gas delivery state of the gas delivery device according to the first ventilation control parameter; the first ventilation control parameter is generated by the fuzzy control component; the gas delivery device is used to deliver gas to the lungs;

[0077] Data acquisition module 20 is used to acquire first electrical impedance data of the lungs through the gas delivery device;

[0078] The first imaging module 30 is used to generate a first lung electrical impedance image based on the first electrical impedance data;

[0079] The parameter generation module 40 is used to process the first lung electrical impedance image through a preset neural network to generate the first network output parameters;

[0080] The second imaging module 50 is used to input the first network output parameters into the fuzzy control component to generate second ventilation control parameters, so as to generate a second pulmonary electrical impedance image based on the second ventilation control parameters.

[0081] Preferably, the data acquisition module 20 includes:

[0082] A network connectivity detection unit is used to detect the network connectivity status of the gas transmission equipment;

[0083] The impedance data acquisition unit is used to send acquisition parameters to the gas transmission equipment if the gas transmission equipment is in a network connection state, so that the gas transmission equipment can acquire the first impedance data according to the acquisition parameters.

[0084] Preferably, the data acquisition module 20 further includes:

[0085] A data acquisition command sending unit is used to send data acquisition commands to the gas transmission equipment;

[0086] The parameter acquisition unit is used by the gas delivery device to respond to the acquisition command, acquire the field boundary voltage of the lung according to the first acquisition parameter, and obtain the first electrical impedance data.

[0087] Preferably, the first imaging module 30 includes:

[0088] A data acquisition count determination unit is used to determine whether the number of times the impedance data is acquired is greater than one.

[0089] The conductivity change image generation unit is used to calculate the change value of the field conductivity and generate the first conductivity change image if the number of times the impedance data is collected is greater than one.

[0090] Preferably, the first imaging module 30 further includes:

[0091] The dynamic area change curve generation unit is used to calculate the change value of the target surface and generate the first dynamic area change curve.

[0092] Preferably, the first imaging module 30 further includes:

[0093] The voltage curve generation unit is used to set an initial voltage and generate a voltage curve based on the impedance data if the number of times the impedance data is collected is equal to one.

[0094] Preferably, the equipment adjustment module 10 includes:

[0095] An image comparison result generation unit is used to acquire an idealized image, compare the idealized image with the first lung electrical impedance image, and generate a first image comparison result;

[0096] A tuning parameter generation unit is used to process the first network output parameters through the tuning algorithm component to generate first tuning parameters;

[0097] The ventilation control parameter generation unit is used to process the first image comparison result and the first tuning parameter through the PID control component to generate the second ventilation control parameter.

[0098] Specific limitations regarding the modular electrical impedance tomography (EIT) lung imaging device can be found in the limitations of the modular EIT lung imaging method described above, and will not be repeated here. Each module in the aforementioned modular EIT lung imaging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0099] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes a readable storage medium and internal memory. The readable storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The database stores data related to the modular electrical impedance tomography (EIT) method. The network interface communicates with external terminals via a network connection. When the computer-readable instructions are executed by the processor, a modular electrical impedance tomography method is implemented. The readable storage medium provided in this embodiment includes both non-volatile and volatile readable storage media.

[0100] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the modular electrical impedance tomography method of the lung described above.

[0101] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the modular electrical impedance tomography method for lung imaging described above.

[0102] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When executed, these computer-readable instructions can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0103] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0104] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A modular electrical impedance tomography method for lung imaging, characterized in that, include: Adjust the gas delivery status of the gas delivery equipment according to the first ventilation control parameter; The first ventilation control parameter is generated by the fuzzy control component; The gas delivery device is used to deliver gas to the lungs; The first electrical impedance data of the lungs is collected through the gas delivery device; A first lung electrical impedance image is generated based on the first electrical impedance data; The first lung electrical impedance image is processed by a preset neural network to generate first network output parameters; wherein, the preset neural network is a radial basis function neural network; The first network output parameters are input into the fuzzy control component to generate second ventilation control parameters, and a second pulmonary electrical impedance image is generated based on the second ventilation control parameters to improve the resolution of the pulmonary electrical impedance image. The fuzzy control component includes a tuning algorithm component and a PID control component; The step of inputting the first network output parameters into the fuzzy control component to generate the second ventilation control parameters includes: An idealized image is acquired, and the idealized image is compared with the first lung electrical impedance image to generate a first image comparison result; wherein, the idealized image refers to the lung imaging image that best matches the measured lung image after the initial lung imaging image and initial parameters are acquired in the preset neural network and calculated by the algorithm. The first network output parameters are processed by the tuning algorithm component to generate the first tuning parameters; The PID control component processes the first image comparison result and the first tuning parameter to generate the second ventilation control parameter.

2. The modular electrical impedance tomography method for lung imaging as described in claim 1, characterized in that, Before collecting the first electrical impedance data of the lungs through the gas delivery device, the method further includes: Detect the network connectivity status of the gas transmission equipment; If the gas transmission equipment is in a network connection state, then the acquisition parameters are sent to the gas transmission equipment so that the gas transmission equipment can obtain the first impedance data according to the acquisition parameters.

3. The modular electrical impedance tomography method for lung imaging as described in claim 1, characterized in that, The method of collecting the first electrical impedance data of the lungs through the gas delivery device further includes: Send a data acquisition command to the gas transmission equipment; The gas delivery device responds to the acquisition command and acquires the boundary voltage of the lungs according to the first acquisition parameters to obtain the first electrical impedance data.

4. The modular electrical impedance tomography method for lung imaging as described in claim 1, characterized in that, The first lung electrical impedance image includes a first conductivity change image; The step of generating a first lung electrical impedance image based on the first electrical impedance data includes: Determine whether the number of times the impedance data is collected is greater than one; If the number of times the impedance data is collected is greater than one, the change in field conductivity is calculated, and the first conductivity change image is generated.

5. The modular electrical impedance tomography method for lung imaging as described in claim 4, characterized in that, The first lung electrical impedance image includes a first dynamic area change curve; After calculating the change in field conductivity and generating the first conductivity change image, the method further includes: Calculate the change value of the target surface and generate the first dynamic area change curve.

6. The modular electrical impedance tomography method for lung imaging as described in claim 4, characterized in that, After determining whether the number of times the impedance data is collected is greater than one, the method further includes: If the number of times the impedance data is collected is equal to one, then the initial voltage is set according to the impedance data, and a voltage curve is generated.

7. A modular electrical impedance tomography (EIT) lung imaging device, characterized in that, include: The equipment adjustment module is used to adjust the gas delivery status of the gas delivery equipment according to the first ventilation control parameter; The first ventilation control parameter is generated by the fuzzy control component; The gas delivery device is used to deliver gas to the lungs; The data acquisition module is used to acquire the first electrical impedance data of the lungs through the gas delivery device; A first imaging module is used to generate a first lung electrical impedance image based on the first electrical impedance data; A parameter generation module is used to process the first lung electrical impedance image through a preset neural network to generate first network output parameters; wherein, the preset neural network is a radial basis function neural network; The second imaging module is used to input the output parameters of the first network into the fuzzy control component to generate second ventilation control parameters, so as to generate a second pulmonary electrical impedance image based on the second ventilation control parameters, thereby improving the resolution of the pulmonary electrical impedance image. The fuzzy control component includes a tuning algorithm component and a PID control component; the equipment adjustment module includes: An image comparison result generation unit is used to acquire an idealized image, compare the idealized image with the first lung electrical impedance image, and generate a first image comparison result; wherein, the idealized image refers to the lung imaging image that best matches the measured lung image after the initial lung imaging image and initial parameters are acquired in a preset neural network and calculated by an algorithm. A tuning parameter generation unit is used to process the first network output parameters through the tuning algorithm component to generate first tuning parameters; The ventilation control parameter generation unit is used to process the first image comparison result and the first tuning parameter through the PID control component to generate the second ventilation control parameter.

8. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that, When the processor executes the computer-readable instructions, it implements the modular electrical impedance tomography method of any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the modular electrical impedance tomography method for lung imaging as described in any one of claims 1 to 6.

Images