Method and apparatus for calculating matching index of ventilation and blood perfusion

Abstract

The present disclosure provides a method and apparatus for calculating a matching index of ventilation and blood perfusion, wherein the method comprises the following steps: using an electrical impedance tomography device to acquire electrical impedance signals from the thoracic cavity of a human body; extracting ventilation-related signals and blood perfusion-related signals from the electrical impedance signals, and plotting a ventilation-related electrical impedance image and a blood perfusion-related electrical impedance image on the basis of the ventilation-related signals and the blood perfusion-related signals, respectively; and calculating the matching index of ventilation and blood perfusion according to pixel point values of the ventilation-related electrical impedance image and the blood perfusion-related electrical impedance image.

Description

A method and apparatus for calculating the matching index of ventilation and blood perfusion.

[0001] Cross-reference to related applications

[0002] This disclosure claims priority to Chinese patent application CN202410663715.9, filed on May 27, 2024, entitled “A method and apparatus for calculating matching indices of ventilation and perfusion”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of electrical impedance imaging technology, and in particular to a method and apparatus for calculating the matching index of ventilation and blood perfusion. Background Technology

[0004] Electrical Impedance Tomography (EIT) equipment acquires electrical impedance changes within the human thoracic cavity, including ventilation-related signals and perfusion-related signals. Ventilation-related signals primarily reflect the ventilation status of the lungs, while perfusion-related signals primarily reflect the blood perfusion status. By extracting these two signals, the regional distribution of ventilation and blood perfusion in the lungs can be reflected in real time. However, the matching of lung ventilation and blood perfusion in this technology is mainly based on subjective, empirical judgments of the electrical impedance signals acquired by the EIT equipment, lacking simple, easily implemented, reasonable, and objective quantitative indicators. Summary of the Invention

[0005] To address the aforementioned issues, embodiments of this disclosure provide a method and apparatus for calculating the matching index of ventilation and blood perfusion.

[0006] In a first aspect, embodiments of this disclosure provide a method for calculating the matching index of ventilation and blood perfusion, the method comprising the following steps:

[0007] S1. Acquire electrical impedance signals from the human thoracic cavity using electrical impedance imaging equipment; S2. Extract ventilation-related signals and blood perfusion-related signals from the electrical impedance signals, and draw ventilation-related electrical impedance images and blood perfusion-related electrical impedance images based on the ventilation-related signals and blood perfusion-related signals, respectively; S3. Calculate the matching index of ventilation and blood perfusion based on the pixel values ​​of the ventilation-related electrical impedance images and blood perfusion-related electrical impedance images.

[0008] In some embodiments, the matching metric includes a matching index, which is calculated as follows:

[0009] Where MI is the matching index; v(i) is the value of the i-th pixel in the ventilation-related impedance image; p(i) is the value of the i-th pixel in the blood perfusion-related impedance image; and N is the number of pixels.

[0010] In some embodiments, the matching indices further include the dead space ventilation index and the intrapulmonary shunt index, wherein the dead space ventilation index is calculated as follows:

[0011] Where DI is the dead space ventilation index; K is the normalization factor.

[0012] In some embodiments, the formula for calculating the intrapulmonary shunt index is:

[0013] SI stands for intrapulmonary shunt.

[0014] In some embodiments, the normalization factor K is calculated as follows:

[0015] in, The maximum pixel value of the ventilation-related impedance image; The maximum pixel value of the blood perfusion-related electrical impedance image; It is the average pixel value of the ventilation-related impedance image; It is the average pixel value of the blood perfusion-related electrical impedance image.

[0016] In some embodiments, MI = (1-DI) × (1-SI).

[0017] In some embodiments, a matching index for ventilation and perfusion is calculated based on pixel values ​​of ventilation-related impedance images and perfusion-related impedance images, including: dividing the ventilation-related impedance image into R... v The blood perfusion-related electrical impedance image is divided into sub-regions, R p There are several sub-regions; among them, R v and R p The values ​​of R are equal; when R... v =R p When R = 1, the pixel values ​​of the entire ventilation-related impedance image and the entire blood perfusion-related impedance image are calculated to obtain the matching index of ventilation and blood perfusion of the entire lung; when R = 1, v =R p When >1, the R values ​​in the ventilation-related impedance image are divided. v R is divided into sub-regions and blood perfusion-related electrical impedance images. p The pixel values ​​of each sub-region are calculated to obtain the matching index of ventilation and blood perfusion in the local lungs.

[0018] Secondly, this disclosure also provides a device for calculating a ventilation-perfusion image matching index. The device includes: an impedance signal acquisition module configured to acquire impedance signals of the human thoracic cavity using an impedance imaging device; an impedance signal imaging module configured to extract ventilation-related signals and perfusion-related signals from the impedance signals, and to plot ventilation-related impedance images and perfusion-related impedance images based on the ventilation-related signals and perfusion-related signals, respectively; and an index calculation module configured to calculate a ventilation-perfusion matching index based on the pixel values ​​of the ventilation-related impedance images and perfusion-related impedance images.

[0019] Thirdly, this disclosure also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for calculating the matching index of ventilation and blood perfusion as described in any of the above embodiments.

[0020] Fourthly, this disclosure also provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements a method for calculating the matching index of ventilation and blood perfusion as described in any of the above embodiments.

[0021] The beneficial effects of this disclosure are as follows: This disclosure proposes a method for calculating the matching index of ventilation and perfusion. This method can selectively calculate the overall and / or local ventilation and perfusion matching index of the human lungs based on the pixel values ​​of ventilation-related impedance images and perfusion-related impedance images. The matching index includes a ventilation-perfusion matching index, which accurately and objectively reflects the matching degree of ventilation and perfusion in the human lungs. Alternatively, the matching index includes a ventilation-perfusion matching index, a dead space ventilation index, and an intrapulmonary shunt index. Among these three indices, the ventilation-perfusion matching index, dead space ventilation index, and intrapulmonary shunt index can more accurately reflect the matching degree of ventilation and perfusion. Compared to related technologies where the evaluation of the matching degree of ventilation and perfusion in the human lungs is mainly based on subjective judgment and experience, this disclosure provides a simple, easy-to-implement, highly accurate, and reasonably objective quantitative index. Attached Figure Description

[0022] Figure 1 is a flowchart illustrating a method for calculating the matching index of ventilation and blood perfusion according to an embodiment of this disclosure.

[0023] Figure 2 is a schematic diagram of the calculation results of a matching index for ventilation and blood perfusion images provided by an embodiment of this disclosure.

[0024] Figure 3 is a schematic diagram of the matching state of pulmonary ventilation and blood perfusion provided by an embodiment of the present disclosure.

[0025] Figure 4 is a structural block diagram of a device for calculating the matching index of ventilation and blood perfusion provided in an embodiment of this disclosure.

[0026] Figure 5 is an internal structure diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this disclosure.

[0028] Electrical Impedance Tomography (EIT) is a non-invasive medical imaging technique that reconstructs the electrical impedance distribution of internal tissues by applying a safe electrical current to the body and then measuring the voltage response at the body surface, thus providing novel physiological information. EIT offers advantages such as low cost, no radiation, and ease of operation, enabling continuous, real-time dynamic monitoring at the bedside. Therefore, EIT has broad application prospects in fields such as respiratory monitoring and cardiovascular disease diagnosis.

[0029] 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 that all areas of the lungs can effectively exchange gases. Mismatch between pulmonary ventilation and blood flow is an important factor reflecting many diseases, such as pulmonary embolism, acute respiratory distress syndrome, chronic obstructive pulmonary disease, and pulmonary edema.

[0030] Currently, methods such as radionuclide lung ventilation-perfusion imaging and electrical impedance tomography (EIA) can be used to assess the matching of ventilation and perfusion in the lungs. Radionuclide lung ventilation-perfusion imaging involves introducing a radiolabeled imaging agent into the body via intravenous or inhalation, allowing it to participate in the body's physiological metabolic processes and obtain lung perfusion and ventilation images. However, radionuclide lung ventilation-perfusion imaging carries the risk of radiation exposure and requires a high degree of patient cooperation. Therefore, its application in assessing the matching of ventilation and perfusion in the human lungs is limited.

[0031] Furthermore, electrical impedance tomography (EIT) captures changes in intrathoracic electrical impedance, including ventilation-related signals and perfusion-related signals. Ventilation-related signals primarily reflect the ventilation status of the lungs, while perfusion-related signals reflect the blood perfusion status. Extracting these two signals allows for real-time visualization of the regional distribution of ventilation and perfusion in the lungs. However, the matching of lung ventilation and perfusion in this technology relies mainly on subjective, empirical judgment based on the electrical impedance signals acquired by EIT, lacking simple, readily applicable, and objective quantitative indicators.

[0032] The following detailed description of some embodiments of this disclosure is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0033] Example 1

[0034] Figure 1 is a flowchart illustrating a method for calculating the matching index of ventilation and perfusion according to an embodiment of this disclosure. As shown in Figure 1, the method for calculating the matching index of ventilation and perfusion images according to an embodiment of this disclosure includes steps S1-S4:

[0035] S1. Acquire electrical impedance signals from the human thoracic cavity using electrical impedance imaging equipment;

[0036] In one example, data is acquired using an electrical impedance imaging device to obtain the electrical impedance signal of the human chest cavity.

[0037] In one example, the electrical impedance tomography device uses an electrode array or electrode strip that is fixed around the chest cavity of a human body. The electrode array or electrode strip can be set in a two-dimensional plane or in three-dimensional space.

[0038] For example, in one instance, an electrode band with 16 electrodes is fixed around the chest cavity using a circular fixation method. Current is then applied to the electrodes in turn to excite them, and the response voltage data is measured sequentially on the other electrodes. One frame of measurement data contains 104 data points, thus yielding the impedance signal.

[0039] For example, in another example, two electrode strips are used to wrap around and fix the patient's chest cavity, with 16 electrodes on each strip. Current is then applied to the electrodes alternately to excite them, and response voltage data is measured sequentially on the other electrodes. One frame of measurement data contains 416 data points, thus yielding the impedance signal.

[0040] S2. Extract ventilation-related signals and blood perfusion-related signals from the impedance signals, and plot ventilation-related impedance images and blood perfusion-related impedance images based on the ventilation-related signals and blood perfusion-related signals, respectively;

[0041] Among them, the pixel values ​​of the ventilation-related electrical impedance image are the ventilation-related electrical impedance changes, and the pixel values ​​of the blood perfusion-related electrical impedance image are the blood perfusion-related electrical impedance changes.

[0042] In one exemplary embodiment, ventilation-related signals and blood perfusion-related signals are extracted from electrical impedance signals based on signal extraction algorithms. These signal extraction algorithms include hypertonic saline contrast imaging, frequency domain filtering, principal component analysis, and neural network-based methods.

[0043] For example, in one instance, frequency domain filtering is used to extract ventilation-related signals and perfusion-related signals from the impedance signal. In an exemplary embodiment, the frequency domain filtering uses a low-pass filter to extract the ventilation-related signal from the impedance signal and a band-pass filter to extract the perfusion-related signal from the impedance signal.

[0044] Both filters have dynamic parameters that are adjusted based on the physiological indicators of the tested human body.

[0045] In one exemplary embodiment, the physiological indicator of the human subject used in this example is heart rate. The cutoff frequency of the low-pass filter is slightly lower 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 3 times the heart rate.

[0046] S3. Calculate the matching index of ventilation and blood perfusion based on the pixel values ​​of the ventilation-related impedance image and the blood perfusion-related impedance image.

[0047] It should be noted that, in one example, ventilation-related impedance images include dynamic images of ventilation-related impedance changes, and blood perfusion-related impedance images include dynamic images of blood perfusion-related impedance changes.

[0048] In an exemplary embodiment, based on an image reconstruction algorithm, dynamic images of ventilation-related impedance changes and dynamic images of blood perfusion-related impedance changes are drawn according to ventilation-related signals and blood perfusion-related signals, respectively. The matching index of ventilation and blood perfusion is calculated based on the pixel values ​​of the dynamic images of ventilation-related impedance changes and blood perfusion-related impedance changes.

[0049] Among them, the dynamic image of ventilation-related impedance changes reflects the dynamic changes in impedance within the human thoracic cavity caused by ventilation, and the dynamic image of blood perfusion-related impedance changes reflects the dynamic changes in impedance within the human thoracic cavity caused by blood perfusion.

[0050] In another example, ventilation-related impedance images include static ventilation images, and blood perfusion-related impedance images include static blood perfusion images.

[0051] In one exemplary embodiment, based on an image reconstruction algorithm, dynamic images of ventilation-related impedance changes and dynamic images of blood perfusion-related impedance changes are drawn according to ventilation-related signals and blood perfusion-related signals, respectively. Static ventilation images and static blood perfusion images are generated based on the dynamic images of ventilation-related impedance changes and dynamic images of blood perfusion-related impedance changes. The matching index of ventilation and blood perfusion is calculated based on the pixel values ​​of the static ventilation images and static blood perfusion images.

[0052] In one exemplary embodiment, the image reconstruction algorithm includes linear or nonlinear, iterative or non-iterative, stochastic or deterministic reconstruction algorithms.

[0053] For example, in one example, the linear differential imaging algorithm (EIT) is used as the image reconstruction algorithm. The differential imaging principle of the linear differential imaging algorithm is to select a reference time and draw dynamic images of ventilation-related impedance changes and blood flow-related impedance changes respectively based on the dynamic changes of ventilation-related signals and blood flow-related impedance signals at each time relative to the reference time.

[0054] The time-domain form of the impedance signal is denoted as μ(t), where t is the time variable. EIT differential imaging can then be expressed as the following least-squares problem:

[0055] Where J is the Jacobian matrix; δμ=μ(t)-μ(t) ref () represents the impedance signal at time t relative to the reference time t. ref change value; δσ=σ(t)-σ(t ref The electrical conductivity distribution of the lungs at time t relative to the reference time t ref The change value 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 ·δμ

[0056] Wherein, δσ is the dynamic image of the calculated impedance change. When δμ is the change value of the ventilation-related signal in the impedance signal, the obtained δσ is the dynamic image of the ventilation-related impedance change; when δμ is the change value of the blood perfusion-related signal in the impedance signal, the obtained δσ is the dynamic image of the blood perfusion-related impedance change.

[0057] In an exemplary embodiment, the method for generating static ventilation images and static blood perfusion images based on the dynamic images of ventilation-related impedance changes and blood perfusion-related impedance changes calculated above can be the linear reference point method, the standard deviation method, etc.

[0058] Among them, static ventilation images and static blood perfusion images reflect the intensity and spatial distribution of lung ventilation and blood perfusion over a short period of time.

[0059] Of course, in some embodiments, the dynamic image of impedance change obtained can be a two-dimensional cross-sectional dynamic image of the lungs or a three-dimensional dynamic image of the lungs, depending on the distribution of the electrode array on the human chest cavity.

[0060] In one exemplary embodiment, the dynamic image of impedance change is a two-dimensional image. For example, in one example, ventilation-related signals and perfusion-related signals with a duration of 30 seconds are taken, and the EIT differential imaging algorithm is used to obtain dynamic images of ventilation-related impedance changes and perfusion-related impedance changes in a two-dimensional cross-section of the target lung region. In this case, the ventilation-perfusion matching index, dead space ventilation index, and intrapulmonary shunt index reflect the matching of ventilation and perfusion in the whole and / or regional lungs in the two-dimensional cross-section.

[0061] In one exemplary embodiment, the dynamic image of electrical impedance change is a three-dimensional image, in which the ventilation-perfusion matching index, dead space ventilation index, and intrapulmonary shunt index reflect the matching of ventilation and perfusion of the whole / regional lung in a three-dimensional volume.

[0062] This disclosure proposes a method for calculating the matching index of ventilation and perfusion. The method can selectively calculate the overall and / or local ventilation and perfusion matching index of the human lungs based on the pixel values ​​of ventilation-related impedance images and perfusion-related impedance images. The matching index can accurately and objectively reflect the matching between ventilation and perfusion in the human lungs.

[0063] In this embodiment, the matching metric includes a matching index, which is calculated as follows:

[0064] Where MI is the matching index; v(i) is the value of the i-th pixel in the ventilation-related impedance image; p(i) is the value of the i-th pixel in the blood perfusion-related impedance image; and N is the number of pixels.

[0065] This disclosure proposes a method for calculating a matching index for ventilation and perfusion, wherein the matching index includes a ventilation-perfusion matching index. Based on this matching index, the matching degree between human lung ventilation and perfusion can be accurately and objectively reflected. Compared to related technologies where the evaluation of the matching degree between human lung ventilation and perfusion is mainly based on subjective judgment and experience, this disclosure provides a simple, accurate, and reasonably objective quantitative index.

[0066] In this embodiment of the disclosure, the matching indices further include the dead space ventilation index and the intrapulmonary shunt index, wherein the dead space ventilation index is calculated as follows:

[0067] Where DI is the dead space ventilation index; v(i) is the value of the i-th pixel in the ventilation-related impedance image; p(i) is the value of the i-th pixel in the perfusion-related impedance image; K is the normalization factor; and N is the number of pixels.

[0068] In this embodiment, the formula for calculating the intrapulmonary shunt index is:

[0069] Where SI is the intrapulmonary shunt index; v(i) is the value of the i-th pixel in the ventilation-related electrical impedance image; p(i) is the value of the i-th pixel in the perfusion-related electrical impedance image; K is the normalization factor; and N is the number of pixels.

[0070] This disclosure also proposes a method for calculating the matching index of ventilation and perfusion, wherein the matching index includes a ventilation-perfusion matching index, a dead space ventilation index, and an intrapulmonary shunt index. These three indices—the ventilation-perfusion matching index, the dead space ventilation index, and the intrapulmonary shunt index—can more accurately reflect the matching degree of ventilation and perfusion.

[0071] In this embodiment of the disclosure, the normalization factor K is calculated as follows:

[0072] in, The maximum pixel value of the ventilation-related impedance image; The maximum pixel value of the blood perfusion-related electrical impedance image; The average pixel value of the ventilation-related impedance image; The average pixel value of the blood perfusion-related electrical impedance image.

[0073] In some embodiments, the relationship between the ventilation-perfusion matching index, the dead space ventilation index, and the intrapulmonary shunt index satisfies the expression: MI = (1-DI)*(1-SI).

[0074] In one example, 0 < DI < 1; 0 < SI < 1.

[0075] In some embodiments, the step of calculating the matching index of ventilation and blood perfusion based on ventilation-related impedance images and blood perfusion-related impedance images includes:

[0076] The ventilation-related impedance image is divided into R... vThe blood perfusion-related electrical impedance image is divided into sub-regions, R p Sub-regions. Among them, R v and R p The values ​​are equal;

[0077] When R v =R p When = 1, the entire ventilation-related impedance image and the entire blood perfusion-related impedance image are calculated to obtain the matching index of ventilation and blood perfusion of the entire lung.

[0078] When R v =R p When >1, the R values ​​in the ventilation-related impedance image are divided. v R is divided into sub-regions and blood perfusion-related electrical impedance images. p The pixel values ​​of each sub-region are calculated to obtain the matching index of ventilation and blood perfusion in the local lungs.

[0079] In one example, when R v =R p When = 1, the matching index of ventilation and perfusion is calculated from the pixel values ​​of the ventilation-related electrical impedance image and the perfusion-related electrical impedance image of the entire lung, reflecting the overall matching of ventilation and perfusion of the lung.

[0080] In another example, when R v =R p When the value is greater than 1, the matching index of ventilation and blood perfusion is calculated from the pixel values ​​of sub-regions of ventilation-related electrical impedance images and sub-regions of blood perfusion-related electrical impedance images, reflecting the matching of ventilation and blood perfusion in the lungs.

[0081] Example 2

[0082] Based on the foregoing description, and in conjunction with specific scenarios, this disclosure will now be further described:

[0083] Figure 2 is a schematic diagram of the calculation result of the matching index of ventilation and blood perfusion images provided by an embodiment of the present disclosure, and Figure 3 is a schematic diagram of the matching state of lung ventilation and blood perfusion provided by an embodiment of the present disclosure. As shown in Figures 2 and 3, the method for calculating the matching index of ventilation and blood perfusion provided by an embodiment of the present disclosure includes:

[0084] S1. Acquire electrical impedance signals from the human thoracic cavity using electrical impedance imaging equipment;

[0085] In one example, data is acquired using an electrical impedance imaging device to obtain the electrical impedance signal of the human chest cavity.

[0086] In an exemplary embodiment, the human chest cavity is used as the test area, and an electrode array or electrode strip is used to excite the test area and the resulting excitation response is collected to obtain an impedance signal.

[0087] Among them, electrical impedance imaging equipment uses an electrode array or electrode strip that is fixed around the area to be measured. The electrode array or electrode strip can be set in a two-dimensional plane or in three-dimensional space.

[0088] For example, in one instance, the chest cavity is used as the test area. An electrode strip is fixed around the chest cavity of the test subject using a circumferential fixing method. Sixteen electrodes are placed on this electrode strip, and current is applied to the electrodes in turn to excite them. The response voltage data is measured on the other electrodes sequentially. One frame of measurement data contains 104 data points, thus obtaining the impedance signal.

[0089] For example, in another instance, two electrode strips are used to wrap around and secure the subject's chest. Each strip has 16 electrodes, and current is applied alternately to the electrodes to excite them, while response voltage data is measured sequentially on the other electrodes. One frame of measurement data contains 416 data points, thus yielding the impedance signal.

[0090] S2. Extract ventilation-related signals and blood perfusion-related signals from the impedance signals, and plot ventilation-related impedance images and blood perfusion-related impedance images based on the ventilation-related signals and blood perfusion-related signals, respectively;

[0091] Among them, the pixel values ​​of the ventilation-related impedance image are the ventilation-related impedance change values, and the pixel values ​​of the blood perfusion-related impedance image are the blood perfusion-related impedance change values.

[0092] In one exemplary embodiment, ventilation-related signals and blood perfusion-related signals are extracted from electrical impedance signals based on signal extraction algorithms. These signal extraction algorithms include hypertonic saline contrast imaging, frequency domain filtering, principal component analysis, and neural network-based methods.

[0093] For example, in one instance, frequency domain filtering is used to extract ventilation-related signals and perfusion-related signals from the impedance signal. In an exemplary embodiment, the frequency domain filtering uses a low-pass filter to extract the ventilation-related signal from the impedance signal and a band-pass filter to extract the perfusion-related signal from the impedance signal.

[0094] Both filters have dynamic parameters that are adjusted based on the physiological indicators of the tested human body.

[0095] In one exemplary embodiment, the physiological indicator of the human subject used in this example is heart rate. The cutoff frequency of the low-pass filter is slightly lower 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 3 times the heart rate.

[0096] S3. Calculate the matching index of ventilation and blood perfusion based on the pixel values ​​of the ventilation-related impedance image and the blood perfusion-related impedance image.

[0097] It should be noted that, in one example, the ventilation-related impedance image can be a dynamic image of ventilation-related impedance changes, and the blood perfusion-related impedance image can be a dynamic image of blood perfusion-related impedance changes.

[0098] In an exemplary embodiment, based on an image reconstruction algorithm, dynamic images of ventilation-related impedance changes and dynamic images of blood perfusion-related impedance changes are drawn according to ventilation-related signals and blood perfusion-related signals, respectively. The matching index of ventilation and blood perfusion is calculated based on the pixel values ​​of the dynamic images of ventilation-related impedance changes and blood perfusion-related impedance changes.

[0099] In another example, the ventilation-related impedance image can be a static ventilation image, and the blood perfusion-related impedance image can be a static blood perfusion image.

[0100] In one exemplary embodiment, based on an image reconstruction algorithm, dynamic images of ventilation-related impedance changes and dynamic images of blood perfusion-related impedance changes are drawn according to ventilation-related signals and blood perfusion-related signals, respectively. Static ventilation images and static blood perfusion images are generated based on the dynamic images of ventilation-related impedance changes and dynamic images of blood perfusion-related impedance changes. The matching index of ventilation and blood perfusion is calculated based on the pixel values ​​of the static ventilation images and static blood perfusion images.

[0101] Among them, the dynamic image of ventilation-related impedance changes reflects the dynamic changes in impedance within the human thoracic cavity caused by ventilation, and the dynamic image of blood perfusion-related impedance changes reflects the dynamic changes in impedance within the human thoracic cavity caused by blood perfusion.

[0102] In one exemplary embodiment, the image reconstruction algorithm includes linear or nonlinear, iterative or non-iterative, stochastic or deterministic reconstruction algorithms.

[0103] For example, in one example, the linear differential imaging algorithm (EIT) is used as the image reconstruction algorithm. The differential imaging principle of the linear differential imaging algorithm is to select a reference time and draw dynamic images of ventilation-related impedance changes and blood perfusion-related impedance changes respectively based on the dynamic changes of ventilation-related signals and blood perfusion-related impedance changes at each time relative to the reference time.

[0104] The time-domain form of the impedance signal is denoted as μ(t), where t is the time variable. EIT differential imaging can then be expressed as the following least-squares problem:

[0105] Where J is the Jacobian matrix; δμ=μ(t)-μ(t) ref () represents the impedance signal at time t relative to the reference time t. ref change value; δσ=σ(t)-σ(t ref The electrical conductivity distribution of the lungs at time t relative to the reference time t ref The change value 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 ·δμ

[0106] Wherein, δσ is the dynamic image of electrical impedance change. When δμ is the change value of ventilation-related signal in the electrical impedance signal, the obtained δσ is the dynamic image of ventilation-related electrical impedance change; when δμ is the change value of blood perfusion-related signal in the electrical impedance signal, the obtained δσ is the dynamic image of blood perfusion-related electrical impedance change.

[0107] In an exemplary embodiment, the method for generating static ventilation images and static blood perfusion images based on the obtained dynamic images of ventilation-related impedance changes and blood perfusion-related impedance changes can be the linear reference point method, the standard deviation method, etc.

[0108] Among them, static ventilation images and static blood perfusion images reflect the intensity and spatial distribution of lung ventilation and blood perfusion over a short period of time.

[0109] Of course, in some embodiments, the dynamic image of impedance change obtained can be a two-dimensional cross-sectional dynamic image of the lungs or a three-dimensional dynamic image of the lungs, depending on the distribution of the electrode array on the human chest cavity.

[0110] In one exemplary embodiment, the dynamic image of impedance change is a two-dimensional image. For example, in one example, ventilation-related signals and blood perfusion-related signals with a duration of 30 seconds are taken, and a two-dimensional linear differential imaging algorithm is used to obtain dynamic images of ventilation-related impedance changes and blood perfusion-related impedance changes in a two-dimensional cross-section of the target lung region. In this case, the ventilation-perfusion matching index, dead space ventilation index, and intrapulmonary shunt index reflect the matching of ventilation and blood perfusion in the whole and / or regional lungs in the two-dimensional cross-section.

[0111] In one exemplary embodiment, the dynamic image of electrical impedance change is a three-dimensional image, in which the ventilation-perfusion matching index, dead space ventilation index, and intrapulmonary shunt index reflect the matching of ventilation and perfusion of the whole / regional lung in a three-dimensional volume.

[0112] In this embodiment of the disclosure, the matching index includes a matching index, which is calculated as follows:

[0113] Where MI is the matching index; v(i) is the value of the i-th pixel in the ventilation-related impedance image; p(i) is the value of the i-th pixel in the blood perfusion-related impedance image; and N is the number of pixels.

[0114] This disclosure proposes a method for calculating a matching index for ventilation and perfusion, wherein the matching index includes a ventilation-perfusion matching index. Based on this matching index, the matching degree between human lung ventilation and perfusion can be accurately and objectively reflected. Compared to related technologies where the evaluation of the matching degree between human lung ventilation and perfusion is mainly based on subjective judgment and experience, this disclosure provides a simple, accurate, and reasonably objective quantitative index.

[0115] In this embodiment of the disclosure, the matching indices further include the dead space ventilation index and the intrapulmonary shunt index, wherein the dead space ventilation index is calculated as follows:

[0116] Where DI is the dead space ventilation index; v(i) is the value of the i-th pixel in the ventilation-related impedance image; p(i) is the value of the i-th pixel in the perfusion-related impedance image; K is the normalization factor; and N is the number of pixels.

[0117] In this embodiment of the disclosure, the formula for calculating the intrapulmonary shunt index is:

[0118] Where SI is the intrapulmonary shunt index; v(i) is the value of the i-th pixel in the ventilation-related electrical impedance image; p(i) is the value of the i-th pixel in the perfusion-related electrical impedance image; K is the normalization factor; and N is the number of pixels.

[0119] This disclosure also proposes a method for calculating the matching index of ventilation and perfusion, wherein the matching index includes a ventilation-perfusion matching index, a dead space ventilation index, and an intrapulmonary shunt index. These three indices—the ventilation-perfusion matching index, the dead space ventilation index, and the intrapulmonary shunt index—can more accurately reflect the matching degree of ventilation and perfusion.

[0120] In the embodiments of this disclosure, the normalization factor K is calculated as follows:

[0121] in, The maximum pixel value of the ventilation-related impedance image; The maximum pixel value of the blood perfusion-related electrical impedance image; The average pixel value of the ventilation-related impedance image; The average pixel value of the blood perfusion-related electrical impedance image.

[0122] In some embodiments, the relationship between the ventilation-perfusion matching index, the dead space ventilation index, and the intrapulmonary shunt index satisfies the expression: MI = (1-DI)*(1-SI).

[0123] In one example, 0 < DI < 1; 0 < SI < 1.

[0124] In some embodiments, the step of calculating the matching index of ventilation and blood perfusion based on ventilation-related impedance images and blood perfusion-related impedance images includes:

[0125] The ventilation-related impedance image is divided into R... v The blood perfusion-related electrical impedance image is divided into sub-regions, R p Sub-regions. Among them, R v and R p The values ​​are equal;

[0126] When R v =R p When = 1, the entire ventilation-related impedance image and the entire blood perfusion-related impedance image are calculated to obtain the matching index of ventilation and blood perfusion of the entire lung.

[0127] When R v =R p When >1, the R values ​​in the ventilation-related impedance image are divided. v R is divided into sub-regions and blood perfusion-related electrical impedance images. p The pixel values ​​of each sub-region are calculated to obtain the matching index of ventilation and blood perfusion in the local lungs.

[0128] In one example, when R v =R p When = 1, the matching index of ventilation and perfusion is calculated from the pixel values ​​of the ventilation-related electrical impedance image and the perfusion-related electrical impedance image of the entire lung, reflecting the overall matching of ventilation and perfusion of the lung.

[0129] In another example, when R v =R p When the value is greater than 1, the matching index of ventilation and blood perfusion is calculated from the pixel values ​​of sub-regions of ventilation-related electrical impedance images and sub-regions of blood perfusion-related electrical impedance images, reflecting the matching of ventilation and blood perfusion in the lungs.

[0130] For example, Figure 2 is a schematic diagram of the calculation result of the matching index of ventilation and blood perfusion images provided by an embodiment of the present disclosure, and Figure 3 is a schematic diagram of the matching state of lung ventilation and blood perfusion provided by an embodiment of the present disclosure. As shown in Figures 2 and 3, in one example, when R v =R p When the value is 4, the entire lung is divided into two sub-regions: the left lung and the right lung. Each of the left and right lungs is further divided into ventral and dorsal sub-regions to calculate the ventilation-perfusion matching of local lung areas. Specifically, the local lung areas are the ventral left lung, the dorsal left lung, the ventral right lung, and the dorsal right lung.

[0131] As shown in Figure 2, in one example, the image labeled LPB in the middle is a ventilation-related electrical impedance image, and the image labeled HPB on the right is a blood perfusion-related electrical impedance image. Both images are two-dimensional cross-sectional images of the lungs, and are divided into four local regions: the ventral side of the left lung, the dorsal side of the left lung, the ventral side of the right lung, and the dorsal side of the right lung.

[0132] The image labeled LHM on the left is a superposition of ventilation-related electrical impedance images and blood perfusion-related electrical impedance images. The lung contours in the ventilation-related electrical impedance images and blood perfusion-related electrical impedance images marked by lines reflect the spatial distribution of lung ventilation and blood perfusion.

[0133] Specifically, as shown in Figure 3, the pixel values ​​of the ventilation-related electrical impedance image and the blood perfusion-related electrical impedance image are normalized to between 0 and 1 (0-100%). Based on the threshold, the lung contours in the ventilation-related electrical impedance image and the blood perfusion-related electrical impedance image are marked with lines of different colors, and different colors are used to represent the strength relationship between lung ventilation and blood perfusion, so as to simultaneously reflect the matching of lung ventilation and blood perfusion in spatial and intensity distribution.

[0134] As shown in the LHM image in Figure 2, the data listed on the right side of the image are the calculated ventilation-perfusion matching index, dead space ventilation index, and intrapulmonary shunt index for the whole lung or sub-regions. Specifically, the first column (MI%) is the ventilation-perfusion matching index, the second column (DI%) is the dead space ventilation index, and the third column (SI%) is the intrapulmonary shunt index. The method for calculating the matching index of ventilation-perfusion images provided in this disclosure can quantify the ventilation-perfusion matching of the whole lung and / or local areas, or quantify the matching rate of ventilation-perfusion of the whole lung and / or local areas, as well as the dead space rate and shunt rate.

[0135] This disclosure proposes a method for calculating the matching index of ventilation and perfusion. This method can selectively calculate the overall and / or local ventilation-perfusion matching index of the human lungs based on pixel values ​​from ventilation-related electrical impedance images and perfusion-related electrical impedance images. The matching index includes a ventilation-perfusion matching index, which accurately and objectively reflects the matching degree of ventilation and perfusion in the human lungs. Alternatively, the matching index includes a ventilation-perfusion matching index, a dead space ventilation index, and an intrapulmonary shunt index. Among these three indices, the ventilation-perfusion matching index, dead space ventilation index, and intrapulmonary shunt index can more accurately reflect the matching degree of ventilation and perfusion. Compared to related technologies where the evaluation of the matching degree of ventilation and perfusion in the human lungs is mainly based on subjective judgment and experience, this disclosure provides a simple, accurate, and reasonably objective quantitative index.

[0136] Example 3

[0137] Figure 4 is a structural block diagram of a ventilation-perfusion matching index calculation device provided in an embodiment of this disclosure. As shown in Figure 4, the embodiment of this disclosure provides a ventilation-perfusion matching index calculation device, which includes an impedance signal acquisition module 10, an impedance signal imaging module 20, and an index calculation module 30. The impedance signal acquisition module 10 is configured to acquire impedance signals from the human thoracic cavity using an impedance imaging device.

[0138] The electrical impedance signal imaging module 20 is configured to extract ventilation-related signals and blood perfusion-related signals from the electrical impedance signals, and to draw ventilation-related electrical impedance images and blood perfusion-related electrical impedance images based on the ventilation-related signals and blood perfusion-related signals, respectively.

[0139] Among them, the pixel values ​​of the ventilation-related electrical impedance image are the ventilation-related electrical impedance changes, and the pixel values ​​of the blood perfusion-related electrical impedance image are the blood perfusion-related electrical impedance changes.

[0140] The index calculation module 30 is configured to calculate the matching index of ventilation and blood perfusion based on the pixel values ​​of the ventilation-related impedance image and the blood perfusion-related impedance image.

[0141] In one embodiment, the electrical impedance signal imaging module 20 includes a ventilation-related signal extraction unit and a blood perfusion-related signal extraction unit. The ventilation-related signal extraction unit is configured to extract ventilation-related components reflecting respiratory signals from the electrical impedance signal based on a signal extraction algorithm, thereby obtaining a ventilation-related signal. The blood perfusion-related signal extraction unit is configured to extract blood perfusion-related components from the electrical impedance signal based on a signal extraction algorithm, thereby obtaining a blood perfusion-related signal.

[0142] In one exemplary embodiment, the signal extraction algorithm includes hypertonic saline contrast imaging, frequency domain filtering, principal component analysis, and neural network-based methods.

[0143] In one exemplary embodiment, the image reconstruction algorithm includes a linear differential imaging algorithm and a neural network-based image reconstruction algorithm.

[0144] In one embodiment, the index calculation module 30 is configured to calculate a matching index of ventilation and perfusion based on the pixel values ​​of the ventilation-related impedance image and the perfusion-related impedance image.

[0145] For example, in one example, the index calculation module 30 includes a first matching index calculation unit and a second matching index calculation unit, wherein the first matching index calculation unit is configured to calculate when R v =R p When = 1, the pixel values ​​of the entire ventilation-related impedance image and the entire blood perfusion-related impedance image are calculated to obtain the matching index of ventilation and blood perfusion of the entire lung.

[0146] The second matching index calculation unit is configured to, when R v =R p When >1, the R values ​​in the ventilation-related impedance image are divided. v R is divided into sub-regions and blood perfusion-related electrical impedance images. p The pixel values ​​of each sub-region are calculated to obtain the matching index of ventilation and blood perfusion in the local lungs.

[0147] This disclosure proposes a device for calculating ventilation-perfusion matching indices. This device can selectively calculate overall and / or local ventilation-perfusion matching indices for the human lungs based on pixel values ​​from ventilation-related electrical impedance images and perfusion-related electrical impedance images. These matching indices include a ventilation-perfusion matching index, which accurately and objectively reflects the matching degree between ventilation and perfusion. Alternatively, the matching indices may include a ventilation-perfusion matching index, a dead space ventilation index, and an intrapulmonary shunt index. Using these three indices provides a more precise reflection of the matching degree between ventilation and perfusion. Compared to related technologies where the evaluation of ventilation-perfusion matching in the human lungs is primarily based on subjective experience, the ventilation-perfusion matching index calculation device provided in this disclosure offers a more accurate and objective evaluation. This disclosure provides a simple, accurate, and reasonable quantitative indicator.

[0148] For specific limitations on the device for calculating the matching index of ventilation and perfusion, please refer to the limitations on the calculation method of the matching index of ventilation and perfusion mentioned above, which will not be repeated here.

[0149] The various units in the aforementioned ventilation-perfusion matching index calculation device can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each unit.

[0150] Example 4

[0151] Figure 4 is an internal structure diagram of an electronic device provided in an embodiment of the present disclosure. As shown in Figure 4, the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0152] The memory 101 can be used to store the computer program 103. The processor 102 implements the method for calculating the matching index of ventilation and blood perfusion in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0153] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0154] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0155] In one example, the memory 101 in the electronic device 100 stores multiple instructions to implement a method for calculating a matching index of ventilation and blood perfusion, and the processor 102 can execute multiple instructions to achieve the following:

[0156] S1. Acquire electrical impedance signals from the human thoracic cavity using electrical impedance imaging equipment;

[0157] In one example, data is acquired using an electrical impedance imaging device to obtain the electrical impedance signal of the human chest cavity.

[0158] In an exemplary embodiment, the human chest cavity is used as the test area, and an electrode array or electrode strip is used to excite the test area and the resulting excitation response is collected to obtain an impedance signal.

[0159] Among them, electrical impedance imaging equipment uses an electrode array or electrode strip that is fixed around the area to be measured. The electrode array or electrode strip can be set in a two-dimensional plane or in three-dimensional space.

[0160] For example, in one instance, the chest cavity is used as the test area. An electrode strip is fixed around the chest cavity of the test subject using a circumferential fixing method. Sixteen electrodes are placed on this electrode strip, and current is applied to the electrodes in turn to excite them. The response voltage data is measured on the other electrodes sequentially. One frame of measurement data contains 104 data points, thus obtaining the impedance signal.

[0161] For example, in another instance, two electrode strips are used to wrap around and secure the subject's chest. Each strip has 16 electrodes, and current is applied alternately to the electrodes to excite them, while response voltage data is measured sequentially on the other electrodes. One frame of measurement data contains 416 data points, thus yielding the impedance signal.

[0162] S2. Extract ventilation-related signals and blood perfusion-related signals from the impedance signals, and plot ventilation-related impedance images and blood perfusion-related impedance images based on the ventilation-related signals and blood perfusion-related signals, respectively;

[0163] Among them, the pixel values ​​of the ventilation-related impedance image are the ventilation-related impedance change values, and the pixel values ​​of the blood perfusion-related impedance image are the blood perfusion-related impedance change values.

[0164] In one exemplary embodiment, ventilation-related signals and blood perfusion-related signals are extracted from electrical impedance signals based on signal extraction algorithms. These signal extraction algorithms include hypertonic saline contrast imaging, frequency domain filtering, principal component analysis, and neural network-based methods.

[0165] For example, in one instance, frequency domain filtering is used to extract ventilation-related signals and perfusion-related signals from the impedance signal. In an exemplary embodiment, the frequency domain filtering uses a low-pass filter to extract the ventilation-related signal from the impedance signal and a band-pass filter to extract the perfusion-related signal from the impedance signal.

[0166] Both filters have dynamic parameters that are adjusted based on the physiological indicators of the tested human body.

[0167] In one exemplary embodiment, the physiological indicator of the human subject used in this example is heart rate. The cutoff frequency of the low-pass filter is slightly lower 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 3 times the heart rate.

[0168] S3. Calculate the matching index of ventilation and blood perfusion based on the pixel values ​​of the ventilation-related impedance image and the blood perfusion-related impedance image.

[0169] In this embodiment, the matching metric includes a matching index, which is calculated as follows:

[0170] Wherein, MI is the matching index of ventilation and blood perfusion; v(i) is the value of the i-th pixel in the ventilation-related impedance image; p(i) is the value of the i-th pixel in the blood perfusion-related impedance image; and N is the number of pixels.

[0171] In this embodiment of the disclosure, the matching indices further include the dead space ventilation index and the intrapulmonary shunt index, wherein the dead space ventilation index is calculated as follows:

[0172] Where DI is the dead space ventilation index; v(i) is the value of the i-th pixel in the ventilation-related impedance image; p(i) is the value of the i-th pixel in the perfusion-related impedance image; K is the normalization factor; and N is the number of pixels.

[0173] In this embodiment, the formula for calculating the intrapulmonary shunt index is:

[0174] Where SI is the intrapulmonary shunt index; v(i) is the value of the i-th pixel in the ventilation-related electrical impedance image; p(i) is the value of the i-th pixel in the perfusion-related electrical impedance image; K is the normalization factor; and N is the number of pixels.

[0175] In this embodiment of the disclosure, the normalization factor K is calculated as follows:

[0176] in, The maximum pixel value of the ventilation-related impedance image; The maximum pixel value of the blood perfusion-related electrical impedance image; The average pixel value of the ventilation-related impedance image; The average pixel value of the blood perfusion-related electrical impedance image.

[0177] In some embodiments, the relationship between the ventilation-perfusion matching index, the dead space ventilation index, and the intrapulmonary shunt index satisfies the expression: MI = (1-DI)*(1-SI).

[0178] In some embodiments, the step of calculating the matching index of ventilation and blood perfusion based on ventilation-related impedance images and blood perfusion-related impedance images includes:

[0179] The ventilation-related impedance image is divided into R... v The blood perfusion-related electrical impedance image is divided into sub-regions, R p Sub-regions. Among them, R v and R p The values ​​are equal;

[0180] When R v =R p When = 1, the entire ventilation-related impedance image and the entire blood perfusion-related impedance image are calculated to obtain the matching index of ventilation and blood perfusion of the entire lung.

[0181] When R v =R p When >1, the R values ​​in the ventilation-related impedance image are divided. v R is divided into sub-regions and blood perfusion-related electrical impedance images. p The pixel values ​​of each sub-region are calculated to obtain the matching index of ventilation and blood perfusion in the local lungs.

[0182] In one example, when R v =R p When = 1, the matching index of ventilation and perfusion is calculated from the pixel values ​​of the ventilation-related electrical impedance image and the perfusion-related electrical impedance image of the entire lung, reflecting the overall matching of ventilation and perfusion of the lung.

[0183] In another example, when R v =R p When the value is greater than 1, the matching index of ventilation and blood perfusion is calculated from the pixel values ​​of sub-regions of ventilation-related electrical impedance images and sub-regions of blood perfusion-related electrical impedance images, reflecting the matching of ventilation and blood perfusion in the lungs.

[0184] This disclosure proposes a method for calculating the matching index of ventilation and perfusion. This method can selectively calculate the overall and / or local ventilation-perfusion matching index of the human lungs based on pixel values ​​from ventilation-related electrical impedance images and perfusion-related electrical impedance images. The matching index includes a ventilation-perfusion matching index, which accurately and objectively reflects the matching degree of ventilation and perfusion in the human lungs. Alternatively, the matching index includes a ventilation-perfusion matching index, a dead space ventilation index, and an intrapulmonary shunt index. Among these three indices, the ventilation-perfusion matching index, dead space ventilation index, and intrapulmonary shunt index can more accurately reflect the matching degree of ventilation and perfusion. Compared to related technologies where the evaluation of the matching degree of ventilation and perfusion in the human lungs is mainly based on subjective judgment and experience, this disclosure provides a simple, accurate, and reasonably objective quantitative index.

[0185] Example 5

[0186] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0187] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0188] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0189] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0190] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0191] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0192] In the description of this disclosure, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this disclosure, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit them. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of this disclosure. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this disclosure should be covered within the protection scope of the claims of this disclosure.

Claims

1. A method for calculating a matching index of ventilation and perfusion, comprising the following steps: S1, acquiring an electrical impedance signal of a human thoracic cavity by using an electrical impedance imaging device; S2, extracting a ventilation-related signal and a perfusion-related signal from the electrical impedance signal, and drawing a ventilation-related electrical impedance image and a perfusion-related electrical impedance image based on the ventilation-related signal and the perfusion-related signal, respectively; S3, calculating a matching index of ventilation and perfusion according to pixel point values of the ventilation-related electrical impedance image and the perfusion-related electrical impedance image.

2. The computational method of claim 1, wherein, The matching index includes a matching index, and a calculation formula of the matching index is: Wherein, MI is the matching index; v(i) is the i th pixel point value of the ventilation-related electrical impedance image; p(i) is the i th pixel point value of the perfusion-related electrical impedance image; N is the number of pixel points.

3. The computational method of claim 2, wherein, The matching index further comprises a dead space ventilation index and a intrapulmonary shunt index, wherein the calculation formula of the dead space ventilation index is: Wherein, DI is the dead space ventilation index; K is a normalization factor.

4. The computational method of claim 3, wherein, The formula for calculating the intrapulmonary shunt index is: Wherein, SI is the intrapulmonary shunt index.

5. The computational method of claim 4, wherein, The calculation formula of the normalization factor K is: wherein for the maximum pixel point value of the ventilation-related electrical impedance image; a maximum pixel value of the blood perfusion related electrical impedance image; is an average pixel value of the ventilation-related electrical impedance image; is the average pixel point value of the perfusion-related electrical impedance image.

6. The computational method of claim 4, wherein, MI=(1-DI)×(1-SI).

7. The computational method of claim 1, wherein, The step of calculating a matching index of ventilation and perfusion according to pixel point values of the ventilation-related electrical impedance image and the perfusion-related electrical impedance image comprises: dividing the ventilation-related electrical impedance image into R v sub-regions, dividing the blood flow perfusion-related electrical impedance image into R p sub-regions; wherein the values of the R v and the R p are equal. When R v = R p = 1, the pixel point values of the whole ventilation-related electrical impedance image and the whole blood perfusion-related electrical impedance image are calculated to obtain the matching index of ventilation and blood perfusion of the whole lung. When R v = R p > 1, the pixel point values of R v sub-regions divided by the ventilation-related electrical impedance image and R p sub-regions divided by the blood perfusion-related electrical impedance image are respectively calculated to obtain the matching index of the ventilation and the blood perfusion of the local lung. 8.A device for calculating a matching index of ventilation and perfusion, comprising: an electrical impedance signal acquisition module configured to acquire an electrical impedance signal of a human thoracic cavity by using an electrical impedance imaging device; an electrical impedance signal imaging module configured to extract a ventilation-related signal and a perfusion-related signal from the electrical impedance signal, and draw a ventilation-related electrical impedance image and a perfusion-related electrical impedance image based on the ventilation-related signal and the perfusion-related signal, respectively; an index calculation module configured to calculate a matching index of ventilation and perfusion according to pixel point values of the ventilation-related electrical impedance image and the perfusion-related electrical impedance image. 9.An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method for calculating a matching index of ventilation and perfusion according to any one of claims 1 to 7 when executing the computer program. 10.A computer readable storage medium storing at least one instruction, wherein the at least one instruction implements the method for calculating a matching index of ventilation and perfusion according to any one of claims 1 to 7 when executed by a processor.

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