A method and apparatus for three-dimensional electrical impedance tomography of the thorax

Abstract

This invention discloses a method and device for three-dimensional electrical impedance tomography (EIT) of the thoracic cavity. The method includes acquiring a three-dimensional point cloud of the thoracic cavity region of the subject to establish a three-dimensional thoracic cavity model; performing device self-test and checking electrode fit; generating a predetermined excitation signal and injecting it into the subject; acquiring voltage signals from each electrode; separating and filtering EIT and ECG signals; performing forward problem calculation on the voltage signals based on the subject's three-dimensional thoracic cavity model to obtain the potentials of each electrode in the three-dimensional thoracic cavity model; and performing three-dimensional reconstruction using the single-step Gauss-Newton method to obtain an electrical impedance tomography map of pulmonary ventilation changes. This invention, by constructing a three-dimensional thoracic cavity model of the subject, performing forward problem calculation on the voltage signals, and further reconstructing the image, can achieve real-time, high-precision three-dimensional dynamic imaging of the subject's thoracic cavity with high imaging accuracy.

Description

Technical Field

[0001] This invention belongs to the technical field of intelligent assisted diagnosis, specifically relating to a method and device for three-dimensional electrical impedance tomography of the thoracic cavity. Background Technology

[0002] The basic principle of Electrical Impedance Tomography (EIT) is to inject an excitation signal into an electrode array deployed on the body surface, and then acquire voltage or current signals at various points to reconstruct an image of the electrical impedance or distribution of electrical impedance changes in the body's tissues and organs. EIT imaging technology has advantages such as fast imaging speed, long-term continuous real-time dynamic monitoring, non-invasive safety, and no radiation, enabling real-time detection of lung health.

[0003] Currently, an EIT system consists of three parts: an electrode array, a signal acquisition system, and an image reconstruction system. For example... Figure 1 The following is the workflow of the existing EIT system: First, an electrode array is laid out on the surface of the thoracic cavity, with 16 electrodes arranged in a circle, and data is collected for two-dimensional electrical impedance imaging; Second, the circuit acquisition system collects voltage or current signals at various points after the excitation current is injected into the human body; Third, the EIT reconstruction algorithm is used to reconstruct the distribution image of electrical impedance or electrical impedance changes on a computer.

[0004] Existing pulmonary electrical impedance tomography (PET) techniques primarily focus on reconstructing two-dimensional real-time images of lung respiration. However, two-dimensional images can only reflect the changes in electrical impedance caused by respiration in the lung cross-section where the electrode bands are located, making it difficult to reflect the real-time respiratory status and health condition of the lungs in three dimensions. Furthermore, existing equipment suffers from slow data acquisition rates and complex reconstruction algorithms, resulting in low real-time imaging frame rates and low pixel counts in software-generated images. Summary of the Invention

[0005] The main objective of this invention is to overcome the shortcomings and deficiencies of existing technologies and provide a method and device for three-dimensional electrical impedance tomography (EIT) of the thoracic cavity. During respiration, the lung impedance exhibits a dynamic change. By acquiring and analyzing these changes in lung impedance, a three-dimensional real-time image of lung exhalation can be achieved. First, an excitation module injects a current signal into the human body through a wearable electrode band. Then, a data acquisition module obtains relevant human body boundary voltage data. Imaging is achieved through data demodulation and image reconstruction. Based on the real-time changes in voltage data from 16 or 32 electrodes, non-invasive and non-destructive continuous monitoring of the real-time three-dimensional lung respiration status can be realized.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] One aspect of the present invention provides a method for three-dimensional electrical impedance tomography of the thoracic cavity, comprising the following steps:

[0008] A three-dimensional point cloud of the chest cavity region of the subject was obtained by three-dimensional laser scanning, and a three-dimensional chest cavity model was established based on surface finite element analysis.

[0009] Determine the parameters of the excitation signal, perform equipment self-test, and check the electrode adhesion.

[0010] The activation module generates a predetermined activation signal and injects it into the test subject.

[0011] The voltage signals of each electrode are acquired by the acquisition module, input into the analog-to-digital converter, and transmitted to the computer.

[0012] The computer uses a signal extraction module to separate the EIT and ECG signals from the voltage signal and then performs filtering.

[0013] Based on the three-dimensional chest cavity model of the subject, the voltage signal is solved by forward problem to obtain the potential of each electrode in the three-dimensional chest cavity model;

[0014] Three-dimensional reconstruction was performed using the single-step Gauss-Newton method to obtain an impedance imaging map of changes in lung ventilation.

[0015] As a preferred technical solution, the step of acquiring a three-dimensional point cloud of the chest cavity region of the subject through three-dimensional laser scanning and establishing a three-dimensional chest cavity model based on surface finite element analysis specifically involves:

[0016] A three-dimensional point cloud of the chest cavity region of the subject is acquired, and the position coordinates of the electrodes are synchronized. The attachment points of the electrodes are marked in the three-dimensional point cloud, and the topological relationship of the electrodes is recorded. The position of the electrodes is projected onto the registered three-dimensional point cloud surface, and the node index of the electrodes is generated as the boundary condition of the three-dimensional chest cavity model. The mesh of the three-dimensional chest cavity model is divided into tetrahedral elements, and local mesh refinement is performed within a set proximity distance of the electrodes.

[0017] As a preferred technical solution, the three-dimensional thoracic cavity model is represented as follows:

[0018] ;

[0019] in s Indicates the conductivity distribution. Indicates the potential distribution. Represents the Hamiltonian operator. Oh Indicates the target field.

[0020] As a preferred technical solution, the device self-test is specifically performed by: sending a detection signal through the excitation module and receiving the detection signal through the acquisition module, determining whether the detection signal matches the preset excitation signal, and then determining whether the device is normal.

[0021] The process of checking the fit of the electrodes is as follows: when the device detects that the subject is wearing electrodes or after the device is activated, the digital-to-analog converter sends an analog signal for detection, which is then input to the computer via the excitation module, channel switching module, and acquisition module. The computer compares the analog signal with preset parameters to determine whether the device is functioning properly and whether the electrode fit is normal. If any abnormality is found, a warning signal is issued.

[0022] As a preferred technical solution, the computer terminal separates the EIT signal and ECG signal through a signal extraction module and performs filtering processing, specifically as follows:

[0023] The mixed signal from the acquisition module is input to a low-pass filter, and then passes through a notch filter and a high-pass filter to separate the ECG signal and the EIT signal respectively.

[0024] The separated ECG signal was subjected to Butterworth low-pass filtering, and the peaks formed by the ECG signal were counted to detect heart rate changes.

[0025] The sinusoidal signal in the stable phase of the separated EIT signal is selected for IQ signal demodulation to obtain the voltage amplitude and phase.

[0026] As a preferred technical solution, the step of performing a forward problem calculation on the voltage signal to obtain the potentials of each electrode in the three-dimensional thoracic cavity model specifically involves:

[0027] The solution to the positive problem includes the following steps:

[0028] Conductivity in the target field s The difference between the measured boundary voltage and the value of the boundary voltage. V The relationship between them is represented as follows:

[0029] V ( s )= Yes + n ;

[0030] in, Represents the Jacobian matrix or sensitivity matrix; n For measuring noise;

[0031] In dynamic solution:

[0032] V ( Board )= JDs ;

[0033] In the formula, Board The distribution of conductivity changes before and after time;

[0034] The difference between the measured voltages before and after the time interval is calculated, and the difference is normalized as shown in the following formula:

[0035] y =( U t2 - U t1 ) / U t1 ;

[0036] In the formula, U t1 For time t The boundary voltage measured at time 1; U t2 For time t The boundary voltage measured at 2 o'clock; y This represents the voltage difference measured before and after time, i.e., the potential of each electrode in the three-dimensional thoracic cavity model.

[0037] As a preferred technical solution, the method of using the single-step Gauss-Newton method for three-dimensional reconstruction to obtain a lung ventilation change impedance imaging map is as follows:

[0038] Introducing a regularization term to calculate the conductivity value, the objective function is expressed as:

[0039] ;

[0040] in, Represents the potential distribution, ||•|| represents the norm, This represents the Jacobian matrix or sensitivity matrix. S n -1 Covariance matrix representing noise S n The reverse, n For measuring noise; s Indicates electrical conductivity. s 0 Indicates the a priori conductivity. S x -1 Represents the prior information covariance matrix S x The reverse;

[0041] Distribution of conductivity changes before and after calculation time Board , represented as:

[0042] Board =( J T WJ + λR ) -1 J T Wy ;

[0043] in, W = s n 2 S n -1 as well as R = s x 2 S x -1 All of these are introduced prior information. W For measurement accuracy model, R For regularization terms, s n To average the measured noise amplitude, s x This represents the a priori amplitude of the change in conductivity. l = s x / s n For regularization hyperparameters, T This is the transpose of the matrix; y The voltage difference before and after the measurement time is the potential of each electrode in the three-dimensional thoracic cavity model.

[0044] Based on the distribution of conductivity changes Board Complete image reconstruction.

[0045] As a preferred technical solution, the three-dimensional point cloud of the chest cavity region of the subject is matched with the surface of the CT image, the CT image data of the subject is imported, and the internal anatomical structure of the chest cavity of the subject is extracted as a priori constraint.

[0046] Another aspect of the present invention provides a three-dimensional electrical impedance tomography device for the thoracic cavity, including a main control module, an excitation module, a channel switching module, an acquisition module, a device self-test and electrode poor adhesion early warning module, a signal extraction module, and an image reconstruction module;

[0047] The main control module is connected to the excitation module and the acquisition module respectively, and is used to: control the excitation module to output an AC signal with a constant frequency and current value; control the opening and closing of the excitation switch and the acquisition switch in the excitation module and the acquisition module to perform cyclic excitation and cyclic acquisition; read the voltage signal acquired by the acquisition module, and then transmit it to the signal extraction module and the image reconstruction module for analysis and imaging;

[0048] The excitation module is used to generate a predetermined excitation signal and inject it into the subject through the channel switching module.

[0049] The channel switching module includes an excitation switch and a data acquisition switch, used to switch between different excitation channels and data acquisition channels; when the excitation switch is turned on, an excitation signal is injected into the subject through the two excitation channels; the data acquisition switch turns on several data acquisition channels in turn, and the voltage signal at the corresponding position is acquired through the data acquisition module.

[0050] The signal extraction module is used to separate the EIT signal and ECG signal from the voltage signal, and transmit them to the image reconstruction module after filtering.

[0051] The device self-test and electrode adhesion failure early warning module is used to perform device self-test and check the electrode adhesion. The device self-test specifically involves: sending a detection signal through the excitation module and receiving the signal through the acquisition module; determining whether the detection signal matches a preset excitation signal to determine if the device is functioning correctly. Checking the electrode adhesion specifically involves: when the device detects that the subject is wearing electrodes or after a device start signal, a digital-to-analog converter sends an analog signal for detection, which is then input to the computer via the excitation module, channel switching module, and acquisition module. The computer compares the analog signal with preset parameters to determine if the device is functioning correctly and whether the electrode adhesion is normal. An early warning signal is issued when a device malfunction or electrode adhesion failure is detected.

[0052] The image reconstruction module is used to perform forward problem calculation on the voltage signal based on the three-dimensional chest cavity model of the subject to obtain the potential of each electrode in the three-dimensional chest cavity model; and to perform three-dimensional reconstruction using the single-step Gauss-Newton method to obtain the lung ventilation change impedance imaging map.

[0053] As a preferred technical solution, in the image reconstruction module, the three-dimensional reconstruction using the single-step Gauss-Newton method to obtain the lung ventilation change impedance imaging map is specifically as follows:

[0054] Introducing a regularization term to calculate the conductivity value, the objective function is expressed as:

[0055] ;

[0056] in, Represents the potential distribution, ||•|| represents the norm, This represents the Jacobian matrix or sensitivity matrix. S n -1 Covariance matrix representing noise S n The reverse, n For measuring noise; s Indicates electrical conductivity. s 0 Indicates the a priori conductivity. S x -1 Represents the prior information covariance matrix S x The reverse;

[0057] Distribution of conductivity changes before and after calculation time Board , represented as:

[0058] Board =( J T WJ + λR ) -1 J T Wy ;

[0059] in, W = s n 2 S n -1 as well as R = s x 2 S x -1 All of these are introduced prior information. W For measurement accuracy model, R For regularization terms, s n To average the measured noise amplitude, s x This represents the a priori amplitude of the change in conductivity. l = s x / s n For regularization hyperparameters, T This is the transpose of the matrix; y The voltage difference before and after the measurement time is the potential of each electrode in the three-dimensional thoracic cavity model.

[0060] Based on the distribution of conductivity changes Board Complete image reconstruction.

[0061] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0062] (1) The three-dimensional electrical impedance tomography device of the thoracic cavity of the present invention uses an excitation switch with low on-resistance, which improves the signal-to-noise ratio of the acquisition system. The average signal-to-noise ratio can reach more than 70dB, which effectively improves the signal quality and improves the imaging accuracy.

[0063] (2) The three-dimensional electrical impedance tomography device of the thoracic cavity of the present invention uses two data acquisition loops, with a fast data acquisition rate, simple algorithm, and real-time high frame rate imaging;

[0064] (3) The hardware of the three-dimensional electrical impedance tomography device of the thoracic cavity of the present invention has added a detection circuit, which has the function of early warning to identify poor electrode patch adhesion or equipment hardware failure;

[0065] (4) The power supply mode of the three-dimensional electrical impedance tomography device of the present invention can be switched. It can be powered by an external power source or by a battery, thus avoiding the situation where the device is unusable when the power is off. It has an intelligent battery monitoring function to read the real-time battery power, provide a low battery warning, and pause charging after charging is completed. It can also charge the battery while using an external power source, simplifying the charging process.

[0066] (5) The three-dimensional electrical impedance tomography device of the present invention analyzes the data collected from 32 points and reconstructs the three-dimensional electrical impedance tomography of the thoracic cavity.

[0067] (6) The present invention can provide constraints through CT chest X-rays, and can construct more accurate three-dimensional chest impedance images.

[0068] (7) The three-dimensional electrical impedance tomography device of the present invention can display respiratory rate and electrocardiogram signal changes while dynamically imaging the lungs, which makes it easy to judge the respiratory status and heart health of the tested human body. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the existing EIT system workflow;

[0070] Figure 2 This is a flowchart of a three-dimensional electrical impedance tomography method for the thoracic cavity according to an embodiment of the present invention;

[0071] Figure 3 This is a schematic diagram illustrating the electrode adhesion warning system according to an embodiment of the present invention;

[0072] Figure 4 This is a schematic diagram of dynamic frequency modulation and amplitude modulation of the excitation signal according to an embodiment of the present invention;

[0073] Figure 5 This is a schematic diagram illustrating the separation of EIT and ECG signals according to an embodiment of the present invention;

[0074] Figure 6This is a schematic diagram of the structure of a three-dimensional electrical impedance tomography device for the thoracic cavity according to an embodiment of the present invention;

[0075] Figure 7 This is a schematic diagram of hardware-side data acquisition according to an embodiment of the present invention;

[0076] Figure 8 A schematic diagram of the device self-test circuit according to an embodiment of the present invention. Detailed Implementation

[0077] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0078] Example 1:

[0079] like Figure 2 As shown, this embodiment provides a method for three-dimensional electrical impedance tomography of the thoracic cavity, including the following steps:

[0080] S1. Obtain three-dimensional point cloud data of the chest cavity region of the subject through three-dimensional laser scanning, and establish a three-dimensional digital model of the chest cavity based on surface finite element analysis.

[0081] In one or more preferred embodiments, the step of acquiring a three-dimensional point cloud of the chest cavity region of the subject through three-dimensional laser scanning and establishing a three-dimensional chest cavity model based on surface finite element analysis specifically involves:

[0082] A three-dimensional point cloud of the chest cavity region of the subject is acquired, and the position coordinates of the electrodes are synchronized. The attachment points of the electrodes are marked in the three-dimensional point cloud, and the topological relationship of the electrodes is recorded. The position of the electrodes is projected onto the registered three-dimensional point cloud surface, and the node index of the electrodes is generated as the boundary condition of the three-dimensional chest cavity model. The mesh of the three-dimensional chest cavity model is divided into tetrahedral elements, and local mesh refinement is performed within a set proximity distance of the electrodes.

[0083] The three-dimensional thoracic cavity model is represented as follows:

[0084] ;

[0085] in s Indicates the conductivity distribution. Indicates the potential distribution. Represents the Hamiltonian operator. Oh Indicates the target field.

[0086] S2. Determine the parameters of the excitation signal to be injected into the human body (subject), complete the equipment self-test, and check the adhesion of the electrodes (electrode sheets).

[0087] In one or more preferred embodiments, the completion of the device self-test specifically involves: sending a detection signal through the excitation module and receiving the detection signal through the acquisition module, determining whether the detection signal matches the preset excitation signal, and then determining whether the device is normal.

[0088] In one or more preferred embodiments, the inspection of electrode fit specifically involves: when the subject is detected wearing electrodes, or after the device is activated, a digital-to-analog converter sends an analog signal for detection, which is then input to a computer via an excitation module, a channel switching module, and a data acquisition module; the computer compares the analog signal with preset parameters to determine whether the device is functioning properly and whether the electrode fit is normal; if any abnormality is found, a warning signal is issued. Figure 3 This is a diagram illustrating the early warning system for electrode adhesion.

[0089] S3. Generate the determined excitation signal through the excitation module and inject it into the subject.

[0090] In one or more preferred embodiments, the excitation module includes an amplitude modulation (AM) device, a frequency modulation (FM) device, and a Holland voltage-controlled constant current source. The main control module sends AM and FM signals to the AM and FM devices, converts the constant voltage signal into a constant current signal, and outputs the required excitation signal through the Holland voltage-controlled constant current source. Figure 4 This is a schematic diagram of dynamic frequency and amplitude modulation of the excitation signal.

[0091] S4. The voltage signals of each electrode are acquired through the acquisition module, input into the analog-to-digital converter (ADC), and transmitted to the computer.

[0092] S5. On the computer side, the EIT signal and ECG signal in the voltage signal are separated by the signal extraction module and then filtered.

[0093] In one or more preferred embodiments, the step of separating the EIT signal and ECG signal by the signal extraction module and performing filtering processing specifically involves:

[0094] like Figure 5 As shown, the mixed signal from the acquisition module is input to a low-pass filter, and then passes through a notch filter and a high-pass filter to separate the ECG signal and the EIT signal respectively.

[0095] The separated ECG signal was subjected to Butterworth low-pass filtering, and the peaks formed by the ECG signal were counted to detect heart rate changes.

[0096] The sinusoidal signal in the stable phase of the separated EIT signal is selected for IQ signal demodulation to obtain the voltage amplitude and phase.

[0097] S6. Based on the three-dimensional chest cavity model of the subject, perform forward problem calculation on the voltage signal to obtain the potential of each electrode in the three-dimensional chest cavity model.

[0098] In one or more preferred embodiments, the step of performing a forward problem calculation on the voltage signal to obtain the potentials of each electrode in the three-dimensional thoracic cavity model specifically involves:

[0099] The solution to the positive problem includes the following steps:

[0100] Conductivity in the target field s The difference between the measured boundary voltage and the value of the boundary voltage. V The relationship between them is represented as follows:

[0101] V ( s )= Yes + n ;

[0102] in, Represents the Jacobian matrix or sensitivity matrix; n For measuring noise;

[0103] In dynamic solution:

[0104] V ( Board )= JDs ;

[0105] In the formula, Board The distribution of conductivity changes before and after time;

[0106] The difference between the measured voltages before and after the time interval is calculated, and the difference is normalized as shown in the following formula:

[0107] y =( U t2 - U t1 ) / U t1 ;

[0108] In the formula, U t1 For time t The boundary voltage measured at time 1; U t2 For time t The boundary voltage measured at 2 o'clock; y This represents the voltage difference measured before and after time, i.e., the potential of each electrode in the three-dimensional thoracic cavity model.

[0109] S7. Three-dimensional reconstruction was performed using the single-step Gauss-Newton method to obtain an impedance imaging map of changes in lung ventilation.

[0110] In one or more preferred embodiments, the step of using the single-step Gauss-Newton method for three-dimensional reconstruction to obtain a lung ventilation change impedance imaging map specifically involves:

[0111] Introducing a regularization term to calculate the conductivity value, the objective function is expressed as:

[0112] ;

[0113] in, Represents the potential distribution, ||•|| represents the norm, This represents the Jacobian matrix or sensitivity matrix. S n -1 Covariance matrix representing noise S n The reverse, n For measuring noise; s Indicates electrical conductivity. s 0 Indicates the a priori conductivity. S x -1 Represents the prior information covariance matrix S x The reverse;

[0114] Distribution of conductivity changes before and after calculation time Board , represented as:

[0115] Board =( J T WJ + λR ) -1 J T Wy ;

[0116] in, W = s n 2 S n -1 as well as R = s x 2 S x -1 All of these are introduced prior information. W For measurement accuracy model, R For regularization terms, s n To average the measured noise amplitude, s x This represents the a priori amplitude of the change in conductivity. l = s x / s n For regularization hyperparameters, T This is the transpose of the matrix; y The voltage difference before and after the measurement time is the potential of each electrode in the three-dimensional thoracic cavity model.

[0117] Based on the distribution of conductivity changes Board Complete image reconstruction.

[0118] Example 2:

[0119] like Figure 6 As shown in this embodiment, a three-dimensional electrical impedance tomography device for the thoracic cavity is provided, including a main control module, an excitation module, a channel switching module, an acquisition module, a device self-test and electrode poor adhesion early warning module, a signal extraction module, and an image reconstruction module.

[0120] The main control module is connected to both the excitation module and the acquisition module, and is used to: control the excitation module to output an AC signal with a constant frequency and current value; control the opening and closing of the excitation and acquisition switches in the excitation and acquisition modules to perform cyclic excitation and cyclic acquisition; and control the acquisition module to read the acquired voltage signal and then transmit it to the subsequent signal extraction module and image reconstruction module for analysis and imaging.

[0121] The excitation module is used to: first generate a corresponding constant voltage AC signal using a digital-to-analog converter (DAC) according to the instructions of the main control module, and then generate a corresponding constant current AC signal using a Howland voltage-controlled constant current source. This signal is then injected into the subject through the channel switching module.

[0122] The channel switching module includes an excitation switch and a data acquisition switch, used to switch between different excitation and data acquisition channels. When the excitation switch is turned on, excitation signals are injected into the subject through several (preferably 2) excitation channels. The data acquisition switch sequentially activates several (preferably 32) data acquisition channels, and the data acquisition module acquires voltage signals at corresponding locations. The specific activation method is described in detail in the operation steps below. A large switch on-resistance value will increase the data acquisition error and increase the imaging difficulty. Therefore, a switch with low on-resistance is selected, which effectively improves the signal-to-noise ratio of the device. Figure 7 This is a schematic diagram of data acquisition on the hardware side.

[0123] The acquisition module is used to modulate the acquired analog signal, and the main control module controls the analog-to-digital converter (ADC) to acquire the modulated analog signal.

[0124] In a preferred embodiment, since each data point needs to undergo a sequential data acquisition cycle, and only one channel performs analog-to-digital conversion, the data acquisition efficiency is low. To improve data acquisition capabilities, two acquisition channels are added for simultaneous data acquisition.

[0125] The signal extraction module selects a stable sinusoidal signal from the voltage signal transmitted back from the main control module, performs IQ signal demodulation to obtain the voltage amplitude and phase, separates the EIT voltage signal and ECG electrocardiogram signal, and transmits them to the image reconstruction module after filtering. The signal stability is monitored in real time by the computer-side circuit inspection module.

[0126] The device self-test and electrode adhesion failure early warning module is used to perform device self-test and check the electrode adhesion. Specifically, the device self-test involves: sending a detection signal through the excitation module and receiving the signal through the acquisition module; determining whether the detection signal matches a preset excitation signal to determine if the device is functioning correctly; the device self-test circuit is as follows: Figure 8 As shown. The process of checking the electrode fit is as follows: when the device detects that the subject is wearing electrodes or after the device is activated, the digital-to-analog converter sends an analog signal for detection, which is then input to the computer via the excitation module, channel switching module, and acquisition module. The computer compares the analog signal with preset parameters to determine whether the device is functioning properly and whether the electrode fit is correct. When a device malfunction or poor electrode fit is detected, a warning signal is issued.

[0127] The image reconstruction module is used to: establish a three-dimensional chest cavity model of the subject on the computer and solve a forward problem for the voltage signal; extract the EIT voltage and phase signals and perform a three-dimensional reconstruction algorithm using the single-step Gauss-Newton method to obtain real-time lung imaging; divide the lung imaging into "cross" or "horizontal layered" sections according to the region of interest and calculate the impedance distribution, real-time respiratory curve, and respiratory rate of the region of interest; extract electrocardiogram signals and display heart rate changes.

[0128] In a preferred embodiment, the step of using the single-step Gauss-Newton method for three-dimensional reconstruction to obtain a lung ventilation change impedance imaging map specifically involves:

[0129] Introducing a regularization term to calculate the conductivity value, the objective function is expressed as:

[0130] ;

[0131] in, Represents the potential distribution, ||•|| represents the norm, This represents the Jacobian matrix or sensitivity matrix. S n -1 Covariance matrix representing noise S n The reverse, n For measuring noise; s Indicates electrical conductivity. s 0 Indicates the a priori conductivity. S x -1 Represents the prior information covariance matrix S x The reverse;

[0132] Distribution of conductivity changes before and after calculation time Board , represented as:

[0133] Board =( J T WJ + λR ) -1 J T Wy ;

[0134] in, W = s n 2 S n -1 as well as R = s x 2 S x -1 All of these are introduced prior information. W For measurement accuracy model, R For regularization terms, s n To average the measured noise amplitude, s x This represents the a priori amplitude of the change in conductivity. l = s x / s n For regularization hyperparameters, T This is the transpose of the matrix; y The voltage difference before and after the measurement time is the potential of each electrode in the three-dimensional thoracic cavity model.

[0135] Based on the distribution of conductivity changes Board Complete image reconstruction.

[0136] In one or more preferred embodiments, the power supply mode of the three-dimensional electrical impedance tomography device for the chest cavity is switchable, and it can be powered by an external power source or by a battery to avoid the situation where the device is unavailable due to power failure; it has an intelligent battery monitoring function to read the real-time battery level, provide a low battery warning, and pause charging after charging is completed; and it can charge the battery while using an external power source, simplifying the charging process.

[0137] It should be noted that the device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above. The system is a three-dimensional electrical impedance tomography method for the thoracic cavity applied to the above embodiments.

[0138] Example 3:

[0139] To enable those skilled in the art to better understand the technical solution of this application, the working process of a three-dimensional electrical impedance tomography device for the thoracic cavity described in Embodiment 2 is explained in this embodiment.

[0140] S0: Three-dimensional data of the chest cavity region's shape is acquired through 3D laser scanning, and a digitized 3D chest cavity model is established based on surface finite element analysis. The 3D electrical impedance tomography (EIT) system can perform rapid 3D scanning of the human body, acquiring 3D point cloud data of the chest cavity's shape, and simultaneously locating the electrode positions fixed within the subject's chest cavity. After acquiring the 3D point cloud data, the 3D chest cavity's curved surface is constructed through finite element analysis. Additionally, existing CT chest X-rays of the patient are imported to calculate the constraint matrix for subsequent chest EIT imaging.

[0141] S1: Injection excitation signal selection and instrument self-test. Power on the device and start it: When using an adapter, the battery will be charging and will stop charging when fully charged. It can also operate normally using battery power without an adapter. Then select the injection excitation signal: Different injection currents and frequencies can be selected for different situations. The current range is 0.1-10mA, and the frequency range is 20kHz-250kHz.

[0142] Finally, the device performs self-testing and checks the fit of the wearable electrodes: A detection loop is built on the core circuit board. The loop consists of an excitation module whose output signal is directly received by the acquisition module, bypassing the channel switching module. When abnormal impedance is detected at the electrode test sites, the device will perform the following actions depending on the situation: 1. If a few abnormal impedances are found at the electrode test sites, the device will alert the user to poor electrode fit. 2. If most test sites show abnormal impedance, the device can activate the detection loop for self-testing to determine if there is a malfunction in the device itself.

[0143] S2: Acquire 3D-EIT data. By wearing the matching electrode straps and connecting them to the EIT device, an alternating current of known value and frequency is injected into the human body being tested. Then, the boundary voltage of the human body is acquired at 32 measurement points (labeled 1, 2, 3...31, 32). Figure 4 As shown, the excitation module outputs a constant current and constant frequency AC signal, which is then injected into the human body at two adjacent points (1 and 2) selected by the excitation switch. After injecting the signal at points 1 and 2, data is acquired at all 32 points. After data acquisition at all points is completed, the injection point is switched to points 2 and 3, and the data acquisition operation at all points is repeated. This process continues until data acquisition at points 32 and 1 is completed, completing one acquisition cycle and starting the next cycle. After data acquisition, the data is converted into a digital signal by an ADC and then input to the subsequent system processor. Currently, the sampling frequency of the EIT device is 1MHz, with a limit of 4MHz.

[0144] S3: Process the acquired data and complete the 3D-EIT reconstruction algorithm. The CT chest scan data of the subject is processed, and the contours of the chest cavity and lungs are extracted using the algorithm. Finite element segmentation is then performed to establish a 3D model of the chest cavity and lungs corresponding to the subject. The subject wears matching electrode straps; each electrode strap has sixteen electrodes, and two electrode straps form an EIT test electrode array, arranged above and below the fourth rib. Voltage signals are acquired from the subject's chest to obtain EIT measurement data.

[0145] S4. Electromagnetic field mathematical model derived by EIT. In the three-dimensional case, the mathematical model of the electrical impedance imaging field satisfying Laplace's equation is obtained:

[0146] ;

[0147] in s Indicates the conductivity distribution within the region. Indicates the potential distribution. Represents the Hamiltonian operator. Oh Indicates the target field.

[0148] In the positive problem of EIT research, the conductivity within the target field is... s The distribution and the measured boundary voltage difference V The relationship between them is represented as follows:

[0149] V ( s )= Yes + n ;

[0150] in, This is the Jacobian matrix or sensitivity matrix; n For measuring noise.

[0151] In dynamic solution:

[0152] V ( Board )= JDs ;

[0153] In the formula, Board This represents the distribution of conductivity changes before and after time.

[0154] The difference between the measured voltages before and after the time interval is calculated, and the difference is normalized as shown in the following formula:

[0155] y =( U t2 - U t1 ) / U t1 ;

[0156] In the formula, U t1 For time t The boundary voltage measured at time 1; U t2 For time t The boundary voltage measured at 2 o'clock; y This represents the voltage difference measured before and after time, i.e., the potential of each electrode in the three-dimensional thoracic cavity model.

[0157] To calculate the accurate conductivity value, a regularization term is introduced, and the objective function is obtained as follows:

[0158] ;

[0159] in, Represents the potential distribution, ||•|| represents the norm, This represents the Jacobian matrix or sensitivity matrix. S n -1 Covariance matrix representing noise Sn The reverse, n For measuring noise; s Indicates electrical conductivity. s 0 Indicates the a priori conductivity. S x -1 Represents the prior information covariance matrix S x The inverse; the distribution of conductivity changes before and after calculation time. Board , represented as:

[0160] ;

[0161] in, W = s n 2 S n -1 as well as R = s x 2 S x -1 All of these are introduced prior information. W For measurement accuracy model, R As a regularization term, it is often obtained from empirical values; s n To average the measured noise amplitude, s x This represents the a priori amplitude of the change in conductivity.

[0162] Let the hyperparameter of regularization l = s x / s n Then, simplifying the above formula, we get the following formula:

[0163] Board =( J T WJ + λR ) -1 J T Wy ;

[0164] in, T This is the transpose of the matrix; y The voltage difference before and after the measurement time is the potential of each electrode in the three-dimensional thoracic cavity model.

[0165] The distribution of conductivity variation is obtained by updating the above formula. BoardThe image reconstruction is completed.

[0166] S5. The electrical signals received by electrodes in the thoracic cavity include pulmonary ventilation signals and cardiac signals. Although cardiac-related signals are significantly weaker and often masked by noise or affected by the more dominant pulmonary ventilation signals, their accurate separation has important clinical value. It allows for the analysis of lung lesions and the statistical analysis of respiratory rate through lung imaging, as well as the rapid recording of current heart rate changes.

[0167] The frequency range of an electrocardiogram (ECG) signal is 0.05~100Hz, while an electroencephalogram (EIT) signal is primarily an alternating current signal of 20kHz~250kHz injected into the body surface, belonging to the high-frequency range. The separation of EIT and ECG signals is as follows: Figure 6 As shown in the diagram, at the circuit acquisition end, two AD converters separately acquire EIT and ECG signals. The acquired ECG signal is then subjected to a Butterworth low-pass filter to primarily remove noise, power line interference, baseline drift, electromyography interference, and random noise. By statistically analyzing the peaks formed by the ECG signal and detecting heart rate changes, the ECG signal at a specific electrode can be displayed.

[0168] Based on lung reconstruction images, the distribution of impedance of interest, respiratory rate, and electrocardiogram signals are analyzed to complete lung health detection.

[0169] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0170] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for three-dimensional electrical impedance tomography of the thoracic cavity, characterized in that, Includes the following steps: A three-dimensional point cloud of the chest cavity region was obtained by three-dimensional laser scanning. A three-dimensional chest cavity model was then established based on surface finite element analysis. Specifically: A three-dimensional point cloud of the chest cavity region of the subject is acquired, and the position coordinates of the electrodes are synchronized. The attachment points of the electrodes are marked in the three-dimensional point cloud, and the topological relationship of the electrodes is recorded. The position of the electrodes is projected onto the registered three-dimensional point cloud surface, and the node index of the electrodes is generated as the boundary condition of the three-dimensional chest cavity model. The mesh of the three-dimensional chest cavity model is divided into tetrahedral elements, and local mesh refinement is performed within the set proximity distance of the electrodes. Determine the parameters of the excitation signal, perform equipment self-test, and check the electrode adhesion. The activation module generates a predetermined activation signal and injects it into the test subject. The voltage signals of each electrode are acquired by the acquisition module, input into the analog-to-digital converter, and transmitted to the computer. The computer uses a signal extraction module to separate the EIT and ECG signals from the voltage signal and performs filtering processing, specifically: The mixed signal from the acquisition module is input to a low-pass filter, and then passes through a notch filter and a high-pass filter to separate the ECG signal and the EIT signal respectively. The separated ECG signal was subjected to Butterworth low-pass filtering, and the peaks formed by the ECG signal were counted to detect heart rate changes. The sinusoidal signal in the stable phase of the separated EIT signal is selected for IQ signal demodulation to obtain the voltage amplitude and phase. Based on the three-dimensional chest cavity model of the subject, the voltage signal is solved by forward problem to obtain the potential of each electrode in the three-dimensional chest cavity model; Three-dimensional reconstruction was performed using the single-step Gauss-Newton method to obtain an impedance imaging map of changes in lung ventilation, specifically: Introducing a regularization term to calculate the conductivity value, the objective function is expressed as: ； in, Represents the potential distribution, ||•|| represents the norm, Σ represents the Jacobian matrix or sensitivity matrix. n -1 The covariance matrix Σ represents the noise. n The inverse of , n is the measurement noise; σ represents conductivity, σ 0 Σ represents the a priori conductivity. x -1 Represents the prior information covariance matrix Σ x The reverse; The distribution Δσ of the change in conductivity before and after the calculation time is expressed as: Δσ=(J T WJ+λR) -1 J T Wy; Where W=σ n 2 Σ n -1 And R=σ x 2 Σ x -1 All of these are introduced prior information, W is the measurement accuracy model, R is the regularization term, and σ n To average the measured noise amplitude, σ x λ is the a priori amplitude of the change in conductivity; x / σ n y is the hyperparameter for regularization, T is the transpose of the matrix; y is the voltage difference before and after time, i.e. the potential of each electrode in the three-dimensional thoracic model. Image reconstruction is performed based on the distribution Δσ of conductivity variation.

2. The method for three-dimensional electrical impedance tomography of the thoracic cavity according to claim 1, characterized in that, The three-dimensional thoracic cavity model is represented as follows: ； Where σ represents the conductivity distribution Indicates the potential distribution. Let Ω denote the Hamiltonian operator, and Ω denote the target field.

3. The method for three-dimensional electrical impedance tomography of the thoracic cavity according to claim 1, characterized in that, The device self-test is specifically performed by: sending a detection signal through the excitation module and receiving the detection signal through the acquisition module, determining whether the detection signal matches the preset excitation signal, and then determining whether the device is normal. The process of checking the fit of the electrodes is as follows: when the device detects that the subject is wearing electrodes or after the device is activated, the digital-to-analog converter sends an analog signal for detection, which is then input to the computer via the excitation module, channel switching module, and acquisition module. The computer compares the analog signal with preset parameters to determine whether the device is functioning properly and whether the electrode fit is normal. If any abnormality is found, a warning signal is issued.

4. The method for three-dimensional electrical impedance tomography of the thoracic cavity according to claim 1, characterized in that, The forward problem calculation of the voltage signal yields the potentials of each electrode in the three-dimensional thoracic cavity model, specifically as follows: The solution to the positive problem includes the following steps: The relationship between the conductivity σ within the target field and the measured boundary voltage difference V is expressed as: V(σ) = Jσ + n; in, This represents the Jacobian matrix or sensitivity matrix; n is the measurement noise. In dynamic solution: V(Δσ) = JΔσ; In the formula, Δσ represents the distribution of conductivity changes before and after time; The difference between the measured voltages before and after the time interval is calculated, and the difference is normalized as shown in the following formula: y=(U t2 -U t1 ) / U t1 ; In the formula, U t1 U is the boundary voltage measured at time t1; t2 y is the boundary voltage measured at time t2; y is the voltage difference measured before and after time, i.e., the potential of each electrode in the three-dimensional thoracic cavity model.

5. The method for three-dimensional electrical impedance tomography of the thoracic cavity according to claim 1, characterized in that, The three-dimensional point cloud of the subject's chest cavity region is matched with the surface of the CT image. The subject's CT image data is imported, and the internal anatomical structure of the subject's chest cavity is extracted as a priori constraint.

6. A three-dimensional electrical impedance tomography (EIT) device for the thoracic cavity, characterized in that, It includes a main control module, an excitation module, a channel switching module, an acquisition module, a device self-test and electrode poor adhesion early warning module, a signal extraction module, and an image reconstruction module; The main control module is connected to the excitation module and the acquisition module respectively, and is used to: control the excitation module to output an AC signal with a constant frequency and current value; control the opening and closing of the excitation switch and the acquisition switch in the excitation module and the acquisition module to perform cyclic excitation and cyclic acquisition; The voltage signal acquired by the acquisition module is read and then transmitted to the signal extraction module and image reconstruction module for analysis and imaging. The excitation module is used to generate a predetermined excitation signal and inject it into the subject through the channel switching module. The channel switching module includes an excitation switch and a data acquisition switch, used to switch between different excitation channels and data acquisition channels; when the excitation switch is turned on, an excitation signal is injected into the subject through the two excitation channels; the data acquisition switch turns on several data acquisition channels in turn, and the voltage signal at the corresponding position is acquired through the data acquisition module. The signal extraction module is used to separate the EIT signal and ECG signal from the voltage signal, and transmit them to the image reconstruction module after filtering. The device self-test and electrode adhesion failure early warning module is used to perform device self-test and check the electrode adhesion. Specifically, the device self-test involves: sending a detection signal through the excitation module and receiving the signal through the acquisition module; determining whether the detection signal matches a preset excitation signal to determine if the device is functioning correctly. Checking the electrode adhesion involves: when the device detects that the subject is wearing electrodes or after a device start signal, a digital-to-analog converter sends an analog signal for detection, which is then input to the computer via the excitation module, channel switching module, and acquisition module. The computer compares the analog signal with preset parameters to determine if the device is functioning correctly and whether the electrode adhesion is proper. When an equipment malfunction or poor electrode adhesion is detected, a warning signal is issued; The image reconstruction module is used to perform forward problem calculation on the voltage signal based on the three-dimensional chest cavity model of the subject to obtain the potential of each electrode in the three-dimensional chest cavity model; and to perform three-dimensional reconstruction using the single-step Gauss-Newton method to obtain the lung ventilation change impedance imaging map.

7. A three-dimensional electrical impedance tomography device for the thoracic cavity according to claim 6, characterized in that, In the image reconstruction module, the single-step Gauss-Newton method is used for three-dimensional reconstruction to obtain a lung ventilation change impedance imaging map, specifically: Introducing a regularization term to calculate the conductivity value, the objective function is expressed as: ； in, Represents the potential distribution, ||•|| represents the norm, Σ represents the Jacobian matrix or sensitivity matrix. n -1 The covariance matrix Σ represents the noise. n The inverse of , n is the measurement noise; σ represents conductivity, σ 0 Σ represents the a priori conductivity. x -1 Represents the prior information covariance matrix Σ x The reverse; The distribution Δσ of the change in conductivity before and after the calculation time is expressed as: Δσ=(J T WJ+λR) -1 J T Wy; Where W=σ n 2 Σ n -1 And R=σ x 2 Σ x -1 All of these are introduced prior information, W is the measurement accuracy model, R is the regularization term, and σ n To average the measured noise amplitude, σ x λ is the a priori amplitude of the change in conductivity; x / σ n y is the hyperparameter for regularization, T is the transpose of the matrix; y is the voltage difference before and after time, i.e. the potential of each electrode in the three-dimensional thoracic model. Image reconstruction is performed based on the distribution Δσ of conductivity variation.

Images