Method and apparatus for calculating matching index of ventilation and perfusion electrical impedance change images

Abstract

A method and apparatus for calculating a matching index of ventilation and perfusion electrical impedance change images. The method comprises: acquiring data by means of an electrical impedance tomography device to obtain electrical impedance signals of a thoracic cavity of a human body (110); extracting ventilation-related signals and perfusion-related signals from the electrical impedance signals (120); generating a ventilation-related electrical impedance change image according to the ventilation-related signals, and generating a perfusion-related electrical impedance change image according to the perfusion-related signals (130); and calculating a matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image according to the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image (140).

Description

Method and device for calculating ventilation and perfusion impedance change image matching index

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] The present disclosure claims priority to Chinese patent application CN202311577040.8, filed on November 23, 2023, entitled “Method and device for calculating ventilation and perfusion electrical impedance change image matching index,” the entire contents of which are incorporated into the present disclosure by reference. Technical Field

[0003] The present disclosure relates to the technical field of electrical impedance imaging, and in particular to a method and device for calculating a matching index of ventilation and perfusion electrical impedance change images. Background Art

[0004] Electrical impedance tomography (EIT) is a non-invasive medical imaging technology that applies a safe current to the human body and then measures the corresponding voltage on the body surface to reconstruct the electrical impedance distribution of internal tissues, thereby providing new physiological information.

[0005] The impedance changes within the human chest cavity captured by electrical impedance imaging have ventilation-related and perfusion-related components. The ventilation-related component primarily reflects the state of lung ventilation, while the perfusion-related component primarily reflects the state of pulmonary blood flow. By extracting these two components, the regional distribution of ventilation and blood flow within the lungs can be reflected in real time. However, the evaluation of the matching of related pulmonary ventilation and blood flow is mostly based on empirical and subjective judgment, and there is still a lack of simple, easy-to-use, and reasonable and objective quantitative indicators. This field has the technical problem of insufficient objectivity in the evaluation of the matching of pulmonary ventilation and blood flow.

[0006] Summary of the Invention

[0007] Based on this, it is necessary to provide a method and device for calculating the matching index of ventilation and perfusion electrical impedance change images to address the above technical issues.

[0008] A method for calculating a matching index of ventilation and perfusion electrical impedance change images comprises: acquiring data through an electrical impedance imaging device to obtain an electrical impedance signal of a human chest cavity; extracting a ventilation-related signal and a perfusion-related signal from the electrical impedance signal; generating a ventilation-related electrical impedance change image based on the ventilation-related signal, and generating a perfusion-related electrical impedance change image based on the perfusion-related signal; and calculating a matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

[0009] In one embodiment, the step of extracting ventilation-related signals and perfusion-related signals from the electrical impedance signal includes: based on a signal extraction algorithm, extracting ventilation-related components in the electrical impedance signal that reflect the respiratory signal to obtain a ventilation-related signal; based on a signal extraction algorithm, extracting perfusion-related components in the electrical impedance signal that reflect the blood flow signal to obtain a perfusion-related signal.

[0010] In one embodiment, the signal extraction algorithm includes hypertonic saline angiography, frequency domain filtering, principal component analysis, and a neural network-based method.

[0011] In one embodiment, the steps of generating a ventilation-related electrical impedance change image based on ventilation-related signals and generating a perfusion-related electrical impedance change image based on perfusion-related signals include: generating a ventilation-related electrical impedance change image using ventilation-related signals based on an image reconstruction algorithm; generating a perfusion-related electrical impedance change image using perfusion-related signals based on an image reconstruction algorithm.

[0012] In one embodiment, the image reconstruction algorithm includes a linear difference imaging algorithm and a neural network-based image reconstruction algorithm.

[0013] In one embodiment, in the step of calculating the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image, the calculation formula for the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image is:

[0014] Wherein, LHI is the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image; V(i) is the i-th pixel point of the ventilation-related electrical impedance change image; P(i) is the i-th pixel point of the perfusion-related electrical impedance change image; and N is the number of pixels in the electrical impedance change image.

[0015] In one embodiment, the step of calculating the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image according to the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image comprises: dividing the ventilation-related electrical impedance change image into M L The perfusion-related electrical impedance change image is divided into M regions. H Area, M H The value of M L The values ​​of are equal; when M L =M H = 1, the entire ventilation-related electrical impedance change image and the entire perfusion-related electrical impedance change image are calculated to obtain the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the entire lung; when M L =M HWhen φ > 1, each divided ventilation-related electrical impedance change image and each divided perfusion-related electrical impedance change image are calculated respectively to obtain the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the local lung in the divided area.

[0016] A device for calculating the matching index of ventilation and perfusion electrical impedance change images comprises: an electrical impedance signal acquisition module configured to acquire data through an electrical impedance imaging device to obtain an electrical impedance signal of a human chest cavity; a related signal extraction module configured to extract ventilation-related signals and perfusion-related signals from the electrical impedance signals; an electrical impedance signal imaging module configured to generate a ventilation-related electrical impedance change image based on the ventilation-related signals and to generate a perfusion-related electrical impedance change image based on the perfusion-related signals; and a matching index calculation module configured to calculate the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

[0017] A computer device includes a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the following steps when executing the computer program: acquiring data through an electrical impedance imaging device to obtain an electrical impedance signal of a human chest cavity; extracting a ventilation-related signal and a perfusion-related signal from the electrical impedance signal; generating a ventilation-related electrical impedance change image based on the ventilation-related signal, and generating a perfusion-related electrical impedance change image based on the perfusion-related signal; and calculating a matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

[0018] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps: acquiring data through an electrical impedance imaging device to obtain an electrical impedance signal of the human chest cavity; extracting ventilation-related signals and perfusion-related signals from the electrical impedance signal; generating a ventilation-related electrical impedance change image based on the ventilation-related signal, and generating a perfusion-related electrical impedance change image based on the perfusion-related signal; and calculating a matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG1 is a flow chart of a method for calculating a matching index of ventilation and perfusion electrical impedance change images in one embodiment;

[0020] FIG2 is a schematic diagram showing calculation results of matching indexes of ventilation-related electrical impedance change images and perfusion-related electrical impedance change images in one embodiment;

[0021] FIG3 is a flow chart of a method for calculating a matching index between a ventilation-related electrical impedance change image and a perfusion-related electrical impedance change image in one embodiment;

[0022] FIG4 is a structural block diagram of a device for calculating a matching index between a ventilation-related electrical impedance change image and a perfusion-related electrical impedance change image in one embodiment;

[0023] FIG5 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not intended to limit the present disclosure.

[0025] Electrical impedance tomography (EIT) is a non-invasive medical imaging technique that applies a safe current to the body and then measures the resulting voltage on the body surface to reconstruct the electrical impedance distribution of internal tissues, providing novel physiological information. EIT offers the advantages of low cost, no radiation, and simple operation, enabling continuous, real-time, dynamic monitoring at the bedside. Therefore, EIT holds broad application prospects in areas such as respiratory monitoring and cardiovascular disease diagnosis.

[0026] Under normal circumstances, the human body should have appropriate pulmonary ventilation and pulmonary blood flow, and the spatial distribution of ventilation and blood flow should be relatively uniform to ensure effective gas exchange in all regions of the lungs. Disorders in the matching of pulmonary ventilation and blood flow can reflect many diseases, such as pulmonary embolism, acute respiratory distress syndrome, chronic obstructive pulmonary disease, and pulmonary edema. Therefore, in the diagnosis and treatment of lung diseases, it is crucial to evaluate the matching of pulmonary ventilation and blood flow.

[0027] Currently, the matching of pulmonary ventilation and blood flow can be assessed using methods such as radionuclide ventilation perfusion imaging and electrical impedance tomography. Radionuclide ventilation perfusion imaging involves the introduction of radionuclide-labeled imaging agents into the body via intravenous injection or inhalation. These agents participate in the body's physiological metabolic processes and produce images of lung perfusion and ventilation. However, radionuclide ventilation perfusion imaging carries the risk of radiation exposure and requires high patient compliance. Furthermore, its use is limited in pregnant women, the elderly, and critically ill patients.

[0028] The impedance changes within the human chest, captured by electrical impedance imaging, have ventilation-related and perfusion-related components. The ventilation-related component primarily reflects the state of lung ventilation, while the perfusion-related component primarily reflects the state of pulmonary blood flow. By extracting these two components, the regional distribution of ventilation and blood flow within the lungs can be reflected in real time. However, existing evaluations of the matching of pulmonary ventilation and blood flow are mostly based on empirical and subjective judgments, lacking simple, feasible, and objective quantitative indicators. Therefore, it is necessary to propose a method for calculating the matching index between ventilation-related and perfusion-related electrical impedance change images to accurately quantify the matching of pulmonary ventilation and blood flow.

[0029] In the following embodiments, a matching index is obtained by performing a matching calculation on a ventilation-related electrical impedance change image and a perfusion-related electrical impedance change image of the human lung. This matching index can reflect the matching of ventilation and blood flow in the human lung, thereby assisting in the diagnosis of lung diseases.

[0030] Example 1

[0031] In this embodiment, as shown in FIG1 , a method for calculating a ventilation and perfusion impedance change image matching index is provided, which includes steps 110 to 140 .

[0032] Step 110: Collect data using an electrical impedance imaging device to obtain an electrical impedance signal of the human chest cavity.

[0033] In this embodiment, the human chest is used as the measurement area. The measurement area is stimulated using an electrode array, and the resulting stimulation response is collected to obtain an electrical impedance signal. In this embodiment, the electrical impedance imaging device uses an electrode array that surrounds and is fixed to the measurement area. The electrode array can be arranged in a two-dimensional plane or in three-dimensional space.

[0034] In this embodiment, to obtain a 2D dynamic cross-sectional image of the lungs, a single electrode belt is secured around the subject's chest. This belt contains 16 electrodes. Current is applied alternately to each electrode to stimulate it, and the response voltage data is measured at each of the other electrodes. One frame of measurement data contains 104 data points. To obtain a 3D dynamic image of the lungs, two electrode belts are secured around the subject's chest. Each belt contains 16 electrodes. Current is applied alternately to each electrode to stimulate it, and the response voltage data is measured at each of the other electrodes. One frame of measurement data contains 416 data points.

[0035] Step 120: extract ventilation-related signals and perfusion-related signals from the electrical impedance signal.

[0036] In this embodiment, a signal extraction algorithm is used to extract ventilation-related components reflecting respiration and perfusion-related components from the electrical impedance signal, respectively, to obtain ventilation-related and perfusion-related signals. Available signal extraction algorithms include hypertonic saline angiography, frequency domain filtering, principal component analysis, and neural network-based methods.

[0037] Step 130 : Generate a ventilation-related electrical impedance change image based on the ventilation-related signal, and generate a perfusion-related electrical impedance change image based on the perfusion-related signal.

[0038] In this embodiment, a ventilation-related electrical impedance change image is generated based on the ventilation-related signal extracted in step 120 based on an image reconstruction algorithm. Similarly, a perfusion-related electrical impedance change image is generated based on the perfusion-related signal extracted in step 120 based on an image reconstruction algorithm.

[0039] In an exemplary embodiment, the image reconstruction algorithm may be linear or nonlinear, iterative or non-iterative, stochastic or deterministic. Depending on the distribution of the electrode array, the reconstructed image may be a two-dimensional dynamic cross-sectional image of the lungs or a three-dimensional dynamic image of the lungs.

[0040] The ventilation-related electrical impedance change image is an image reflecting the electrical impedance change in the human body region to be measured caused by ventilation, and the perfusion-related electrical impedance change image is an image reflecting the electrical impedance change in the human body region to be measured caused by blood perfusion.

[0041] Step 140 : Calculate a matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

[0042] In this embodiment, the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image is obtained by the following calculation formula:

[0043] Wherein, LHI is the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image; V(i) is the i-th pixel point of the ventilation-related electrical impedance change image; P(i) is the i-th pixel point of the perfusion-related electrical impedance change image; and N is the number of pixels in the electrical impedance change image.

[0044] Example 2

[0045] In this embodiment, a method for calculating a matching index of ventilation and perfusion electrical impedance change images is provided, which includes the following steps 1 to 4.

[0046] Step 1: Collect data through electrical impedance imaging equipment to obtain the electrical impedance signal of the human chest cavity.

[0047] In this step, the human chest cavity is selected as the test area. An electrode belt with 16 electrodes is fixed around the chest cavity of the test subject. Current is applied to each electrode in turn to stimulate it, and the response voltage data is measured at each electrode in turn. One frame of measurement data has 104 data points. The response voltage data is measured for each data point to obtain the electrical impedance signal.

[0048] Step 2: Extract ventilation-related signals and perfusion-related signals from the electrical impedance signal.

[0049] In this step, frequency domain filtering is used to extract ventilation-related and perfusion-related signals from the electrical impedance signal. In one exemplary embodiment, the frequency domain filtering method uses a low-pass filter to extract ventilation-related signals from the electrical impedance signal, and a band-pass filter to extract perfusion-related signals from the electrical impedance signal. The parameters of these two filters are dynamic and are adjusted based on the physiological indicators of the subject.

[0050] In one exemplary embodiment, the physiological indicator of the human body being measured is heart rate. The cutoff frequency of the low-pass filter is slightly less than the heart rate, the lower cutoff frequency of the band-pass filter coincides with the cutoff frequency of the low-pass filter, and the upper cutoff frequency of the band-pass filter is slightly greater than three times the heart rate.

[0051] Step three: generating a ventilation-related electrical impedance change image based on the ventilation-related signal, and generating a perfusion-related electrical impedance change image based on the perfusion-related signal.

[0052] In this step, a linear differential imaging algorithm is used as the image reconstruction algorithm. The principle of EIT (Electrical Impedance Tomography) differential imaging is to select a reference time and generate ventilation-related electrical impedance change images and perfusion-related electrical impedance change images based on the dynamic changes of ventilation-related signals and perfusion-related signals at each time relative to the reference time. The time domain form of the electrical impedance signal is denoted as u(t), where t is the time variable. EIT differential imaging can then be expressed as the following least squares problem:

[0053] Where J is the Jacobian matrix; δu=u(t)-u(t ref ) is the electrical impedance signal at time t relative to the reference time t ref Changes in δσ=σ(t)-σ(t ref ) is the conductivity distribution of the area to be measured at time t relative to the reference time t ref The change of is defined in the discretized model; α is the regularization parameter; R is the regularization matrix. The solution to the above problem is: δσ=(J T J+αRT ·R) -1 ·J T ·δu

[0054] The above δσ is the calculated electrical impedance change image. When δu is the change of ventilation-related signals in the electrical impedance signal, the obtained δσ is the ventilation-related electrical impedance change image; when δu is the change of perfusion-related signals in the electrical impedance signal, the obtained δσ is the perfusion-related electrical impedance change image.

[0055] In one embodiment, ventilation-related signals and perfusion-related signals with a duration of 30 seconds are taken, and a two-dimensional linear differential imaging algorithm is used to obtain a dynamic two-dimensional cross-sectional image of the lungs, which includes a ventilation-related electrical impedance change image and a perfusion-related electrical impedance change image of the target area.

[0056] Step 4: Calculate the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

[0057] In this step, the ventilation-related electrical impedance change image is divided into M L The perfusion-related electrical impedance change image is divided into M regions. H Area, M H The value of M L The values ​​of are equal, and M L and M H are integers greater than or equal to 1.

[0058] When taking M L =M H =1, the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image are substituted into the above calculation formula for calculation. The calculated matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image can reflect the matching of ventilation and blood flow in the whole lung.

[0059] When taking M L =M H =6, that is, the lung is divided into the left lung and the right lung, and the left lung and the right lung are divided into three regions each according to the ventral edge and the dorsal edge of the lung. Then, the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image corresponding to these six regions are respectively substituted into the above calculation formula for calculation; each calculated ventilation-related electrical impedance change image and perfusion-related electrical impedance change image matching index is configured to reflect the matching of ventilation and blood flow in the lung area, that is, to reflect the matching of local lung ventilation and blood flow. In an exemplary embodiment, the lung is divided into the upper left lung, the middle left lung, the lower left lung, the upper right lung, the middle right lung, and the lower right lung.

[0060] Referring to Figure 2, in this embodiment, the image on the upper left is a superposition of all end-inspiratory ventilation images within 30 seconds, and the image on the upper right is a superposition of all end-systolic perfusion images within 30 seconds. Both images are two-dimensional cross-sectional images of the lungs. The table at the bottom of the figure shows the calculated matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image. The table includes the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image within each region, as well as the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image for the entire lung region.

[0061] Example 3

[0062] In this embodiment, as shown in FIG3 , a method for calculating a ventilation and perfusion impedance change image matching index is provided, comprising the following steps S01 to S04 .

[0063] Step S01: Acquisition of electrical impedance signals. In an exemplary embodiment, an electrode array is used to stimulate the area to be measured, and the response generated thereby is collected to obtain an electrical impedance signal.

[0064] In this embodiment, an electrode array is used that is fixed around the area to be measured. The electrode array can be arranged in a two-dimensional plane or in a three-dimensional space.

[0065] In one embodiment, the human chest cavity is used as the test area, and two electrode belts are fixed around the upper and lower ends of the subject's chest. Each electrode belt has 16 electrodes. Current excitation is applied to the electrodes in turn, and the response voltage data is measured on other electrodes in turn. One frame of measurement data has a total of 416 data points.

[0066] Step S02: Extracting ventilation-related signals and perfusion-related signals from the electrical impedance signal. Specifically, a signal extraction algorithm is used to extract the ventilation-related component reflecting the respiratory signal and the perfusion-related component reflecting the blood flow signal from the electrical impedance signal, respectively, to obtain ventilation-related signals and perfusion-related signals.

[0067] In an exemplary embodiment, the signal extraction algorithm includes hypertonic saline angiography, frequency domain filtering, principal component analysis, and a neural network-based approach.

[0068] In one embodiment, step S02 is implemented using a frequency-domain filtering method. In one exemplary embodiment, the frequency-domain filtering method uses a low-pass filter to extract ventilation-related signals from the electrical impedance signal; and a band-pass filter to extract perfusion-related signals from the electrical impedance signal. The parameters of both filters are set as dynamic parameters and are adjusted based on the physiological indicators of the subject.

[0069] In one exemplary embodiment, the physiological indicator of the human body being measured is heart rate. The cutoff frequency of the low-pass filter is slightly less than the heart rate, the lower cutoff frequency of the band-pass filter coincides with the cutoff frequency of the low-pass filter, and the upper cutoff frequency of the band-pass filter is slightly greater than three times the heart rate.

[0070] Step S03: Generate a ventilation-related electrical impedance change image and a perfusion-related electrical impedance change image based on the ventilation-related signal and the perfusion-related signal. In one exemplary embodiment, an image reconstruction algorithm is used to simultaneously reconstruct the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image. The ventilation-related electrical impedance change image reflects electrical impedance changes within the human body region to be measured caused by ventilation, while the perfusion-related electrical impedance change image reflects electrical impedance changes within the human body region to be measured caused by blood perfusion.

[0071] In an exemplary embodiment, the image reconstruction algorithm can be linear or nonlinear, iterative or non-iterative, stochastic or deterministic. Depending on the distribution of the electrode array, the reconstructed image can be a two-dimensional cross-sectional dynamic image of the lungs or a three-dimensional dynamic image of the lungs.

[0072] In one embodiment, a linear differential imaging algorithm is used as the image reconstruction algorithm. The principle of EIT differential imaging is to select a reference time and generate ventilation-related electrical impedance change images and perfusion-related electrical impedance change images based on the dynamic changes of ventilation-related and perfusion-related signals at each time relative to the reference time. The time domain form of the electrical impedance signal is denoted as u(t), where t is the time variable. EIT differential imaging can be expressed as the following least squares problem:

[0073] Where J is the Jacobian matrix; δu=u(t)-u(t ref ) is the electrical impedance signal at time t relative to the reference time t ref Changes in δσ=σ(t)-σ(t ref ) is the conductivity distribution of the area to be measured at time t relative to the reference time t ref The change of is defined in the discretized model; α is the regularization parameter; R is the regularization matrix. The solution to the above problem is: δσ=(J T J+αR T ·R) -1 ·J T ·δu

[0074] The above δσ is the calculated electrical impedance change image. When δu is the change of ventilation-related signals in the electrical impedance signal, the obtained δσ is the ventilation-related electrical impedance change image; when δu is the change of perfusion-related signals in the electrical impedance signal, the obtained δσ is the perfusion-related electrical impedance change image.

[0075] In one embodiment, ventilation-related signals and perfusion-related signals with a duration of 30 seconds are taken, and a two-dimensional linear differential imaging algorithm is used to obtain a dynamic two-dimensional cross-sectional image of the lungs, which includes a ventilation-related electrical impedance change image and a perfusion-related electrical impedance change image of the target area.

[0076] Step S04, calculating the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the target area. In an exemplary embodiment, the ventilation-related electrical impedance change image is divided into M L The perfusion-related electrical impedance change image is divided into M regions. H The matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image was calculated based on the following expression:

[0077] Wherein, LHI is the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image; V(i) is the i-th pixel point of the ventilation-related electrical impedance change image; P(i) is the i-th pixel point of the perfusion-related electrical impedance change image; and N is the number of pixels in the electrical impedance change image.

[0078] In an exemplary embodiment, in step S04, M H The value of M L are equal, and M L ≥1, M H ≥1.

[0079] In one embodiment, when M is taken L =M H =1, calculate the matching of overall lung ventilation and blood flow; when M L =M H = 6, the matching of global / regional lung ventilation and blood flow is calculated simultaneously; L =M H = 1, calculate the matching of local lung ventilation and blood flow. L =M H =1, the calculated matching index of ventilation-related electrical impedance change image and perfusion-related electrical impedance change image reflects the matching of overall lung ventilation and blood flow; L =M H =6, the calculated matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image reflects the matching of local lung ventilation and blood flow.

[0080] In an exemplary embodiment, in M L =M H=6, the lung is divided into the left lung and the right lung, and three regions are divided into each of the left lung and the right lung according to the ventral edge and the dorsal edge of the lung to reflect the matching of local lung ventilation and blood flow; in an exemplary embodiment, the local lung is the upper left lung, the middle left lung, the lower left lung, the upper right lung, the middle right lung or the lower right lung.

[0081] Referring to Figure 2, in one embodiment, the image on the upper left is a superposition of all end-inspiratory ventilation images within 30 seconds, and the image on the upper right is a superposition of all end-systolic perfusion images within 30 seconds. Both images are two-dimensional cross-sectional images of the lungs. The table below shows the calculated matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image. The table includes the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image within each region, as well as the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image for the entire lung region.

[0082] Example 4

[0083] In this embodiment, as shown in FIG4 , a device for calculating the matching index of ventilation and perfusion electrical impedance change images is provided, including an electrical impedance signal acquisition module 210 , a related signal extraction module 220 , an electrical impedance signal imaging module 230 , and a matching index calculation module 240 .

[0084] The electrical impedance signal acquisition module 210 is configured to acquire data through an electrical impedance imaging device to obtain an electrical impedance signal of the human chest cavity;

[0085] a related signal extraction module 220 configured to extract ventilation-related signals and perfusion-related signals from the electrical impedance signal;

[0086] The electrical impedance signal imaging module 230 is configured to generate a ventilation-related electrical impedance change image based on the ventilation-related signal and to generate a perfusion-related electrical impedance change image based on the perfusion-related signal;

[0087] The matching index calculation module 240 is configured to calculate the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

[0088] In one embodiment, the related signal extraction module includes: a ventilation-related signal extraction unit, configured to extract the ventilation-related components for reflecting the respiratory signal from the electrical impedance signal based on a signal extraction algorithm to obtain a ventilation-related signal; a perfusion-related signal extraction unit, configured to extract the perfusion-related components for reflecting the blood flow signal from the electrical impedance signal based on a signal extraction algorithm to obtain a perfusion-related signal.

[0089] In one embodiment, the signal extraction algorithm includes hypertonic saline angiography, frequency domain filtering, principal component analysis, and a neural network-based approach.

[0090] In one embodiment, the electrical impedance signal imaging module includes: a ventilation-related electrical impedance imaging unit, configured to generate a ventilation-related electrical impedance change image based on an image reconstruction algorithm using ventilation-related signals; and a ventilation-perfusion electrical impedance imaging unit, configured to generate a perfusion-related electrical impedance change image based on an image reconstruction algorithm using perfusion-related signals.

[0091] In one embodiment, the image reconstruction algorithm includes a linear difference imaging algorithm and a neural network-based image reconstruction algorithm.

[0092] In one embodiment, the calculation formula for the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image is:

[0093] Wherein, LHI is the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image; V(i) is the i-th pixel point of the ventilation-related electrical impedance change image; P(i) is the i-th pixel point of the perfusion-related electrical impedance change image; and N is the number of pixels in the electrical impedance change image.

[0094] In one embodiment, the matching index calculation module includes: a region division unit configured to divide the ventilation-related electrical impedance change image into M L The perfusion-related electrical impedance change image is divided into M regions. H Area, M H The value of M L The first matching index calculation unit is configured to be equal to M L =M H = 1, the entire ventilation-related electrical impedance change image and the entire perfusion-related electrical impedance change image are calculated to obtain the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the entire lung; the second matching index calculation unit is configured to: L =M H When φ > 1, each divided ventilation-related electrical impedance change image and each divided perfusion-related electrical impedance change image are calculated respectively to obtain the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the local lung in the divided area.

[0095] Regarding the specific limitations of the calculation device for the ventilation and perfusion electrical impedance change image matching index, please refer to the limitations of the calculation method for the ventilation and perfusion electrical impedance change image matching index above, and will not be repeated here. Each unit in the above-mentioned calculation device for the ventilation and perfusion electrical impedance change image matching index can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned units.

[0096] Example 5

[0097] In this embodiment, a computer device is provided. A diagram of its internal structure may be shown in FIG5 . The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program, and the non-volatile storage medium is configured to contain a database for storing ventilation-related signals and perfusion-related signals. The internal memory provides an operating environment for the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with other computer devices configured with application software. When executed by the processor, the computer program implements a method for processing cargo inbound and outbound data. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen. The input device of the computer device may be a touch screen covering the display screen, or may be buttons, a trackball, or a touchpad provided on the computer device housing, or may be an external keyboard, touchpad, or mouse.

[0098] Those skilled in the art will understand that the structure shown in FIG4 is merely a block diagram of a portion of the structure related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0099] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program: acquiring data through an electrical impedance imaging device to obtain an electrical impedance signal of a human chest cavity; extracting ventilation-related signals and perfusion-related signals from the electrical impedance signal; generating a ventilation-related electrical impedance change image based on the ventilation-related signal, and generating a perfusion-related electrical impedance change image based on the perfusion-related signal; and calculating a matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

[0100] In one embodiment, when the processor executes the computer program, it further implements the following steps: based on the signal extraction algorithm, extracting the ventilation-related components in the electrical impedance signal that reflect the respiratory signal to obtain a ventilation-related signal; based on the signal extraction algorithm, extracting the perfusion-related components in the electrical impedance signal that reflect the blood flow signal to obtain a perfusion-related signal.

[0101] In one embodiment, the signal extraction algorithm includes hypertonic saline angiography, frequency domain filtering, principal component analysis, and a neural network-based approach.

[0102] In one embodiment, when the processor executes the computer program, it further implements the following steps: based on the image reconstruction algorithm, using the ventilation-related signal to generate a ventilation-related electrical impedance change image; based on the image reconstruction algorithm, using the perfusion-related signal to generate a perfusion-related electrical impedance change image.

[0103] In one embodiment, the image reconstruction algorithm includes a linear difference imaging algorithm and a neural network-based image reconstruction algorithm.

[0104] In one embodiment, when the processor executes the computer program, the following steps are further implemented: dividing the ventilation-related electrical impedance change image into M L The perfusion-related electrical impedance change image is divided into M regions. H Area, M H The value of M L The values ​​of are equal; when M L =M H = 1, the entire ventilation-related electrical impedance change image and the entire perfusion-related electrical impedance change image are calculated to obtain the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the entire lung; when M L =M H When φ > 1, each divided ventilation-related electrical impedance change image and each divided perfusion-related electrical impedance change image are calculated respectively to obtain the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the local lung in the divided area.

[0105] Example 6

[0106] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: acquiring data through an electrical impedance imaging device to obtain an electrical impedance signal of the human chest cavity; extracting ventilation-related signals and perfusion-related signals from the electrical impedance signal; generating a ventilation-related electrical impedance change image based on the ventilation-related signal, and generating a perfusion-related electrical impedance change image based on the perfusion-related signal; and calculating a matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image based on the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

[0107] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the signal extraction algorithm, the ventilation-related components used to reflect the respiratory signal in the electrical impedance signal are extracted to obtain a ventilation-related signal; based on the signal extraction algorithm, the perfusion-related components used to reflect the blood flow signal in the electrical impedance signal are extracted to obtain a perfusion-related signal.

[0108] In one embodiment, the signal extraction algorithm includes hypertonic saline angiography, frequency domain filtering, principal component analysis, and a neural network-based approach.

[0109] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: based on the image reconstruction algorithm, ventilation-related signals are used to generate ventilation-related electrical impedance change images; based on the image reconstruction algorithm, perfusion-related signals are used to generate perfusion-related electrical impedance change images.

[0110] In one embodiment, the image reconstruction algorithm includes a linear difference imaging algorithm and a neural network-based image reconstruction algorithm.

[0111] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: dividing the ventilation-related electrical impedance change image into M L The perfusion-related electrical impedance change image is divided into M regions. H Area, M H The value of M L The values ​​of are equal; when M L =M H = 1, the entire ventilation-related electrical impedance change image and the entire perfusion-related electrical impedance change image are calculated to obtain the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the entire lung; when M L =M HWhen φ > 1, each divided ventilation-related electrical impedance change image and each divided perfusion-related electrical impedance change image are calculated respectively to obtain the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the local lung in the divided area.

[0112] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present disclosure can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0113] The aforementioned method and device for calculating the ventilation and perfusion electrical impedance change image matching index compares ventilation and blood flow matching in the human lungs using the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image according to the aforementioned formula. This calculation method incorporates information about the spatial relationship and intensity of ventilation and perfusion signals, offering advantages such as simplicity, reasonable accuracy, and a high degree of quantification. It provides a highly feasible quantitative assessment scheme for the matching of pulmonary ventilation and blood flow, facilitating the diagnosis, grading, and treatment of abnormal ventilation and perfusion.

[0114] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this disclosure.

[0115] The above-described embodiments merely represent several implementation methods of the present disclosure. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art could make various modifications and improvements without departing from the spirit of the present disclosure, all of which fall within the scope of protection of the present disclosure. Therefore, the scope of protection of the present patent shall be determined by the appended claims.

Claims

1. A method for calculating the matching index of ventilation and perfusion impedance change images, comprising the following steps: The electrical impedance signal of the human chest cavity is obtained by collecting data through electrical impedance imaging equipment; extracting ventilation-related signals and perfusion-related signals from the electrical impedance signal; generating a ventilation-related electrical impedance change image according to the ventilation-related signal, and generating a perfusion-related electrical impedance change image according to the perfusion-related signal; A matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image is calculated according to the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

2. The method according to claim 1, wherein: The step of extracting ventilation-related signals and perfusion-related signals from the electrical impedance signal comprises: Based on a signal extraction algorithm, a ventilation-related component reflecting a respiratory signal is extracted from the electrical impedance signal to obtain the ventilation-related signal; Based on a signal extraction algorithm, the perfusion-related component reflecting the blood flow signal in the electrical impedance signal is extracted to obtain the perfusion-related signal.

3. The method according to claim 2, wherein: The signal extraction algorithms include hypertonic saline angiography, frequency domain filtering, principal component analysis, and neural network-based methods.

4. The method according to claim 1, wherein: The step of generating a ventilation-related electrical impedance change image according to the ventilation-related signal and generating a perfusion-related electrical impedance change image according to the perfusion-related signal comprises: Based on an image reconstruction algorithm, generating the ventilation-related electrical impedance change image using the ventilation-related signal; The perfusion-related electrical impedance change image is generated using the perfusion-related signal based on an image reconstruction algorithm.

5. The method according to claim 4, wherein: The image reconstruction algorithm includes a linear differential imaging algorithm and an image reconstruction algorithm based on a neural network.

6. The method according to claim 1, wherein: In the step of calculating the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image, The calculation formula for the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image is: Wherein, LHI is the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image; V(i) is the i-th pixel point of the ventilation-related electrical impedance change image; P(i) is the i-th pixel point of the perfusion-related electrical impedance change image; and N is the number of pixels of the electrical impedance change image.

7. The method according to claim 1, wherein: The step of calculating the matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image according to the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image comprises: The ventilation-related electrical impedance change image is divided into M L The perfusion-related electrical impedance change image is divided into M regions. H area, the M H The value of M L The values ​​of are equal; When M L =M H =1, the entire ventilation-related electrical impedance change image and the entire perfusion-related electrical impedance change image are calculated to obtain a matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the entire lung; When M L =M H >1, each of the divided ventilation-related electrical impedance change images and each of the divided perfusion-related electrical impedance change images are calculated respectively to obtain a matching index of the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image of the local lung in the divided area.

8. A device for calculating ventilation and perfusion impedance change image matching index, comprising: An electrical impedance signal acquisition module is configured to acquire data through an electrical impedance imaging device to obtain an electrical impedance signal of a human chest cavity; A related signal extraction module, configured to extract ventilation related signals and perfusion related signals from the electrical impedance signal; an electrical impedance signal imaging module, configured to generate a ventilation-related electrical impedance change image according to the ventilation-related signal, and to generate a perfusion-related electrical impedance change image according to the perfusion-related signal; The matching index calculation module is configured to calculate the matching index between the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image according to the ventilation-related electrical impedance change image and the perfusion-related electrical impedance change image.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for calculating the ventilation and perfusion electrical impedance change image matching index as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method for calculating the ventilation and perfusion electrical impedance change image matching index according to any one of claims 1 to 7 are implemented.

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