A regional lung perfusion delay time analysis method, electronic device and storage medium

Abstract

The present application relates to the field of electrical impedance imaging, and discloses a regional lung perfusion delay time analysis method, an electronic device and a storage medium. A dynamic image of conductivity change caused by blood perfusion in a human thoracic cavity is obtained. A blood perfusion image of a heart region and a blood perfusion image of a lung region are separated. A reference curve of conductivity change of the heart region is obtained according to the dynamic image of blood perfusion conductivity change and the blood perfusion image of the heart region. A conductivity change curve of each pixel point in the blood perfusion image of the lung region is obtained according to the dynamic image of blood perfusion conductivity change and the blood perfusion image of the lung region. The conductivity change curve is divided by a sliding time window, and a regional lung perfusion delay time is calculated in each time window. The time resolution advantage of electrical impedance imaging technology is fully utilized, and the time delay of the time when the local blood volume of the lung reaches the maximum relative to the end of the ventricular diastole is reflected by calculating the regional lung perfusion delay time.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of electrical impedance imaging, and particularly to a method for analyzing regional lung perfusion delay time, an electronic device, and a storage medium. Background Technology

[0002] In recent years, pulmonary blood flow perfusion imaging technology based on electrical impedance tomography (EIP) has attracted widespread attention. Based on different imaging principles, it can be divided into two categories: the hypertonic saline method and the pulsatile method. The pulsatile method relies primarily on the propagation of pulse wave signals in blood vessels. Pulse wave signals originate from the periodic contraction and relaxation of the heart, forming rhythmic intermittent ejection of blood, resulting in pulsatile changes in blood pressure and vessel walls within the aorta or pulmonary artery, gradually affecting the entire arterial system. Currently, image analysis methods for pulmonary blood flow perfusion based on EIP mainly focus on studying image intensity and its spatial distribution information, lacking image analysis schemes that fully utilize the temporal resolution advantage of EIP technology. Summary of the Invention

[0003] The purpose of this invention is to provide at least one method, electronic device, and storage medium for analyzing regional lung perfusion delay time, which can at least solve the problem of fully utilizing the temporal resolution advantage of electrical impedance imaging technology for image analysis, and at least achieve the effect of reflecting the time delay of the moment when the local blood volume of the lung reaches its maximum value relative to the end of ventricular diastole by using the calculated regional lung perfusion delay time.

[0004] To address the aforementioned technical problems, at least one embodiment of the present invention provides a method for analyzing regional lung perfusion delay time, comprising:

[0005] To obtain dynamic images of conductivity changes caused by blood perfusion in the human thoracic cavity;

[0006] Based on the dynamic image of conductivity change, the blood perfusion image of the cardiac region and the blood perfusion image of the lung region are separated.

[0007] Based on the dynamic image of conductivity change and the blood perfusion image of the cardiac region, a reference curve of conductivity change in the cardiac region is obtained;

[0008] Based on the dynamic image of conductivity change and the blood perfusion image of the lung region, the conductivity change curve of each pixel in the blood perfusion image of the lung region is obtained;

[0009] The conductivity change curve is divided into sliding time windows, and the regional lung perfusion delay time is calculated within each time window.

[0010] In some optional embodiments, acquiring dynamic images of conductivity changes caused by intrathoracic blood flow perfusion includes:

[0011] Acquire the electrical impedance signal of the human thoracic cavity;

[0012] Extract blood perfusion-related signals from the impedance signals;

[0013] A dynamic image of conductivity changes is generated based on the blood perfusion-related signals.

[0014] In some optional embodiments, a reference curve for the conductivity change in the cardiac region is obtained based on the dynamic image of conductivity change and the blood perfusion image of the cardiac region, including:

[0015] Extract the region of interest (ROI) from the cardiac perfusion image of the cardiac region;

[0016] The average curve of the conductivity change curves of all pixels in the region of interest of the heart is calculated in the dynamic image of conductivity change, and the average curve is determined as the reference curve of conductivity change of the heart region.

[0017] In some optional embodiments, the regional lung perfusion delay time is calculated within each time window using the following formula:

[0018]

[0019] In the formula, This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window in the lung region blood perfusion image. This represents a reference curve showing the change in conductivity within the k-th time window. The curve representing the conductivity change of the i-th pixel in the lung perfusion image within the k-th time window is denoted by n, where n represents the discrete time and τ represents the delay time.

[0020] In some optional embodiments, it also includes:

[0021] A mean map of regional lung perfusion delay time and a standard deviation map of regional lung perfusion delay time are generated based on the regional lung perfusion delay time.

[0022] In some optional embodiments, it also includes:

[0023] The mean lung perfusion delay time map of the region is divided into R. M For each first region of interest, the mean μ of the mean lung perfusion delay time for each pixel within that region of interest is calculated. M and standard deviation σ M The standard deviation map of lung perfusion delay time in the aforementioned region is divided into R... SFor each second region of interest, the mean μ of the regional lung perfusion delay time standard deviation for each pixel is calculated. S and standard deviation σ S ;

[0024] Among them, R M ≥1, R s ≥1.

[0025] In some optional embodiments, the regional lung perfusion delay time analysis method further includes:

[0026] Based on the mean μ within the first region of interest M The first ratio is calculated based on the ratio between the cardiac cycle duration and the cardiac cycle length.

[0027] Based on the standard deviation σ within the first region of interest M The second ratio is calculated based on the ratio between the cardiac cycle duration and the cardiac cycle length.

[0028] Based on the mean μ in the second region of interest S The third ratio is calculated based on the ratio between the cardiac cycle duration and the heart rate duration.

[0029] Based on the standard deviation σ in the second region of interest S The fourth ratio is calculated by comparing the ratio between the cardiac cycle duration and the heart rate duration.

[0030] In some alternative embodiments, in R M =R S When the value is 2, the mean map of regional lung perfusion delay time is divided into the first region of interest for the left lung region and the first region of interest for the right lung region, and the standard deviation map of regional lung perfusion delay time is divided into the second region of interest for the left lung region and the second region of interest for the right lung region.

[0031] The method further includes:

[0032] Based on the mean μ within the first region of interest M Calculate the mean μ between the left and right lung regions based on the duration of the cardiac cycle. M Difference ratio.

[0033] In some optional embodiments, the mean regional lung perfusion delay time of the i-th pixel in the mean regional lung perfusion delay time map is calculated using the following formula:

[0034]

[0035] In the formula, denoted by , represents the mean lung perfusion delay time at the i-th pixel in the lung perfusion image, where K represents the number of time windows. This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window in the lung region perfusion image.

[0036] In some optional embodiments, the regional lung perfusion delay time standard deviation of the i-th pixel in the regional lung perfusion delay time standard deviation map is calculated using the following formula:

[0037]

[0038] In the formula, The standard deviation of the regional lung perfusion delay time at the i-th pixel in the lung perfusion image is represented by K, where K represents the number of time windows. This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window in the lung region blood perfusion image. This represents the mean lung perfusion delay time of the i-th pixel in the lung perfusion image.

[0039] At least one embodiment of the present invention also provides a regional lung perfusion delay time analysis device, comprising:

[0040] The image acquisition module is used to acquire dynamic images of conductivity changes caused by blood perfusion in the human thoracic cavity;

[0041] The region separation module is used to separate the blood flow perfusion images of the cardiac region and the lung region based on the dynamic image of the conductivity change;

[0042] The first curve generation module is used to obtain a reference curve of conductivity change in the heart region based on the dynamic image of conductivity change and the blood perfusion image of the heart region.

[0043] The second curve generation module is used to obtain the conductivity change curve of each pixel in the lung region blood perfusion image based on the conductivity change dynamic image and the lung region blood perfusion image;

[0044] The delay time analysis module is used to divide the conductivity change curve into sliding time windows and calculate the lung perfusion delay time within each time window.

[0045] At least one embodiment of the present invention also provides an electronic device, comprising:

[0046] At least one processor; and,

[0047] A memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the above-described regional lung perfusion delay time analysis method.

[0049] At least one embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described regional lung perfusion delay time analysis method.

[0050] The regional lung perfusion delay time analysis method, electronic device and storage medium provided by the present invention can at least solve the problem of making full use of the temporal resolution advantage of electrical impedance imaging technology for image analysis, and can at least achieve the effect of reflecting the time delay of the moment when the local blood volume of the lung reaches its maximum value relative to the end of ventricular diastole by the calculated regional lung perfusion delay time. Attached Figure Description

[0051] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0052] Figure 1 This is a flowchart of a regional lung perfusion delay time analysis method provided in an embodiment of the present invention;

[0053] Figure 2 This is an embodiment of the present invention providing a mean plot of regional lung perfusion delay time and a standard deviation plot of regional lung perfusion delay time;

[0054] Figure 3 This is a schematic diagram of a regional lung perfusion delay time analysis device provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0056] 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 simultaneously measuring the response voltage on 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.

[0057] In recent years, pulmonary perfusion imaging based on electrical impedance tomography (EIP) has attracted widespread attention. Based on different imaging principles, it can be divided into two categories: hypertonic saline method and pulsatile method. Among them, the pulsatile method's imaging principle mainly relies on the propagation of pulse wave signals in blood vessels. Pulse wave signals originate from the periodic contraction and relaxation of the heart, forming rhythmic intermittent ejection of blood, which leads to pulsatile changes in blood pressure and vessel walls within the aorta, gradually affecting the entire arterial system. The propagation of pulse wave signals is not only affected by the heart itself but also by various physiological and pathological factors in the arteries and their branches, such as vascular compliance, peripheral resistance, vascular volume, arterial pressure, and blood viscosity. Therefore, pulse wave signal propagation is closely related to human hemodynamic parameters.

[0058] In pulsatile dynamic images of pulmonary blood flow perfusion, the changes in conductivity over time in different lung regions reflect the pulsatile changes in local blood volume caused by the expansion and contraction of the pulmonary artery walls during pulse wave propagation. Specifically, when local blood volume increases, the conductivity of the relevant lung region increases; conversely, when local blood volume decreases, the conductivity of the relevant lung region decreases. Therefore, in pulsatile dynamic images of pulmonary blood flow perfusion, regional conductivity and its changes reflect the propagation characteristics of the pulse wave signal, potentially containing a wealth of information about pulmonary vascular hemodynamic parameters that warrants further investigation.

[0059] To address the aforementioned technical problems, this invention proposes a regional lung perfusion delay time analysis method, electronic device, and storage medium. The implementation details of the embodiments of this invention are described below. The following content is only for ease of understanding and is not essential for implementing this solution.

[0060] Example 1

[0061] The regional lung perfusion delay time analysis method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 1 As shown, it includes:

[0062] Step 101: Obtain dynamic images of the changes in conductivity caused by blood perfusion in the human thoracic cavity.

[0063] In the specific implementation, dynamic images of conductivity changes caused by blood perfusion in the human thoracic cavity are acquired, including:

[0064] Step 101a: Obtain the electrical impedance signal of the human thoracic cavity.

[0065] In a specific implementation, an electrode array positioned in the region to be measured is used to excite the region, and the resulting response is acquired using an electrical impedance imaging device to obtain the electrical impedance signal. The electrode array can be fixed around the region to be measured, and the arrangement of the electrode array can be in a two-dimensional plane or in three-dimensional space; this embodiment is not limited to any particular arrangement.

[0066] In one example, the human chest cavity is used as the test area. Two electrode strips, each with 16 electrodes, are fixed around the upper and lower ends of the subject's chest. Current excitation is applied to the electrodes alternately, and response voltage data are measured sequentially on other electrodes besides the currently excited electrode. One frame of measurement data contains 416 data points. These response voltage data are then acquired using an electrical impedance imaging device to obtain the electrical impedance signal of the human chest cavity.

[0067] Step 101b: Extract blood perfusion-related signals from the impedance signal.

[0068] In the specific implementation, a signal extraction algorithm is used to extract blood perfusion-related signals that reflect blood perfusion from the impedance signal.

[0069] The signal extraction algorithm can be any of the following: frequency domain filtering, principal component analysis, or neural network-based methods.

[0070] In one example, frequency domain filtering is used to extract perfusion-related signals reflecting blood flow from the impedance signal. Frequency domain filtering uses a bandpass filter to extract perfusion-related signals from the impedance signal. The parameters of both filters are dynamically adjusted based on the physiological indicators of the subject, such as heart rate. The lower cutoff frequency of the bandpass filter is set slightly less than the heart rate, and the upper cutoff frequency is set slightly greater than n times the heart rate, where n ≥ 1.

[0071] Step 101c: Generate dynamic images of conductivity changes based on blood perfusion-related signals.

[0072] In the specific implementation, an image reconstruction algorithm is used to reconstruct a dynamic image of conductivity changes from blood perfusion-related signals. The dynamic image of conductivity changes can reflect the dynamic changes in conductivity within the test area caused by blood perfusion.

[0073] The image reconstruction algorithm can be linear or nonlinear, iterative or non-iterative, stochastic or deterministic; this embodiment does not impose any limitations. Based on the aforementioned electrode array arrangement, the reconstructed image can be a two-dimensional cross-sectional dynamic image of the lung, or a three-dimensional dynamic image of the lung.

[0074] In one example, a linear differential imaging algorithm is used as the image reconstruction algorithm. The principle of EIT (Electrical Impedance Tomography) differential imaging is: select a reference time, and generate a dynamic image of conductivity changes related to blood perfusion based on the dynamic changes of blood perfusion-related signals at each time relative to the reference time.

[0075] The time-domain form of the impedance signal is denoted as u(t), where t is the time variable. Then, EIT differential imaging can be expressed as the following least squares problem:

[0076]

[0077] In the formula, J represents the Jacobian matrix; δu=u(t)-u(t) ref The impedance signal u(t) represents the impedance signal u(t) at time t relative to the reference time t. ref The impedance signal u(t) ref ); δσ=σ(t)-σ(t ref () indicates the conductivity distribution of the measured region at time t relative to the reference time t. ref The variation of is defined in the discretized model; α represents the regularization parameter; R represents the regularization matrix.

[0078] Therefore, the solution to the above least squares problem is:

[0079] δσ=(J T ·J+αR T ·R) -1 ·J T ·δu

[0080] The above δσ is the dynamic image of conductivity change obtained by calculation. When δu is the change of blood perfusion-related signal in the impedance signal, the obtained δσ is the dynamic image of conductivity change caused by blood perfusion.

[0081] In one example, a 60-second blood perfusion-related signal was extracted, and a dynamic image of the blood perfusion conductivity change in a two-dimensional cross-section of the target lung region was obtained using a two-dimensional linear differential imaging algorithm.

[0082] Step 102: Based on the dynamic image of conductivity change, separate the blood perfusion image of the cardiac region and the blood perfusion image of the lung region.

[0083] In the specific implementation, a cardiopulmonary separation method based on blood perfusion images is used to generate blood perfusion images of the cardiac region and the lung region. The cardiopulmonary separation method based on blood perfusion images can be linear regression or K-Means clustering; this embodiment is not limited to either.

[0084] Step 103: Based on the dynamic image of conductivity change and the blood perfusion image of the heart region, obtain the reference curve of conductivity change in the heart region.

[0085] In the specific implementation, a reference curve for conductivity changes in the cardiac region is obtained based on dynamic images of conductivity changes and blood perfusion images of the cardiac region, including:

[0086] Step 103a: Extract the region of interest (ROI) from the cardiac perfusion image;

[0087] Step 103b: Calculate the average curve of the conductivity change curves of all pixels in the region of interest of the heart in the dynamic image of conductivity change, and determine the average curve as the reference curve of conductivity change of the heart region.

[0088] Specifically, regions with pixel values ​​greater than a specified threshold in the blood perfusion image of the heart region are defined as regions of interest in the heart, and regions with pixel values ​​greater than a specified threshold in the blood perfusion image of the lung region are defined as regions of interest in the lung, thus achieving effective extraction of regions of interest in the heart and lung.

[0089] In one example, the specified threshold for defining the region of interest in the heart is 0.95, and the specified threshold for defining the region of interest in the lungs is 0.25.

[0090] In one example, the above average curve is calculated using the following formula:

[0091]

[0092] In the formula, x ref Reference curve representing the change in conductivity in the cardiac region, x i This represents the conductivity change curve at the i-th pixel in a dynamic image of conductivity changes caused by blood perfusion, Ω. H For the region of interest of the heart, N H denoted as the number of pixels belonging to the region of interest of the heart, and n represents the discrete time.

[0093] Step 104: Based on the dynamic image of conductivity change and the blood perfusion image of the lung region, obtain the conductivity change curve of each pixel in the blood perfusion image of the lung region.

[0094] In some cases, signal interpolation can be used to reconstruct conductivity variation curves. The signal interpolation method can be either sinc interpolation or cubic spline interpolation.

[0095] In one example, the frame rate of the electrical impedance imaging device is 20 frames per second. If a dynamic image of conductivity change is captured for 60 seconds, then the reference curve of conductivity change in the heart region and the conductivity change curve of each pixel in the lung region both have 1200 data points. Using the cubic spline interpolation method, the sampling frequency is increased by 5 times, that is, the reference curve of conductivity change in the heart region and the conductivity change curve of each pixel in the lung region both become 6000 data points.

[0096] Step 105: Divide the conductivity change curve into sliding time windows and calculate the regional lung perfusion delay time within each time window.

[0097] In the specific implementation, the following calculation formula is used to calculate the regional lung perfusion delay time within each time window.

[0098]

[0099] In the formula, This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window in the lung region blood perfusion image. This represents a reference curve showing the change in conductivity within the k-th time window. The curve representing the conductivity change of the i-th pixel in the lung perfusion image within the k-th time window is denoted by n, where n represents the discrete time and τ represents the delay time.

[0100] Sliding time windows can be segmented in an overlapping manner.

[0101] In one example, the sliding time window is 10 seconds long, the overlap length is 8 seconds, and the sliding step is 2 seconds. For a conductivity change curve with a duration of 60 seconds, it can be divided into 26 time windows. For each pixel in the lung region, the regional lung perfusion delay time within each time window is calculated.

[0102] In some cases, the method of this embodiment further includes:

[0103] Step 106: Generate a mean map of regional lung perfusion delay time and a standard deviation map of regional lung perfusion delay time based on the regional lung perfusion delay time.

[0104] Based on the regional lung perfusion delay time, mean plots and standard deviation plots of regional lung perfusion delay time are calculated, providing a visualization method for assessing the temporal instability and spatial heterogeneity of pulmonary blood flow perfusion.

[0105] In the specific implementation, the mean lung perfusion delay time of the i-th pixel in the mean lung perfusion delay time map is calculated using the following formula:

[0106]

[0107] In the formula, denoted by , represents the mean lung perfusion delay time at the i-th pixel in the lung perfusion image, where K represents the number of time windows. This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window of the lung regional blood perfusion image. The mean regional lung perfusion delay time plot is used to visualize the mean regional lung perfusion delay time for each pixel.

[0108] The standard deviation of the regional lung perfusion delay time at the i-th pixel in the regional lung perfusion delay time standard deviation map can be calculated using the following formula:

[0109]

[0110] In the formula, The standard deviation of the regional lung perfusion delay time at the i-th pixel in the lung perfusion image is represented by K, where K represents the number of time windows. This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window in the lung region blood perfusion image. This represents the mean lung perfusion delay time of the i-th pixel in the lung perfusion image. The regional lung perfusion delay time standard deviation plot is used to visualize the standard deviation of the regional lung perfusion delay time.

[0111] In some cases, the mean map of regional lung perfusion delay time is divided into R... M For each first region of interest, the mean μ of the mean lung perfusion delay time for each pixel within that region of interest is calculated. M and standard deviation σ M The regional lung perfusion delay time standard deviation map is divided into R... S For each second region of interest, the mean μ of the regional lung perfusion delay time standard deviation for each pixel is calculated. S and standard deviation σ S ;

[0112] Among them, R M ≥1, R s ≥1.

[0113] In one example, let R be... M =R S =1 and R M =R S=2, simultaneously assessing the temporal instability and spatial heterogeneity of pulmonary blood flow perfusion in the whole / local lung regions; in R M =R S When = 1, the mean value μ of the pixel values ​​in the calculated mean map of regional lung perfusion delay time. M and standard deviation σ M and the mean μ of the pixel values ​​in the regional lung perfusion delay time standard deviation map. S and standard deviation σ S It reflects the overall condition of the lungs; in T M =R S When = 2, the mean value μ of the pixel value of the calculated mean map of regional lung perfusion delay time. M and standard deviation σ M and the mean μ of the pixel values ​​in the regional lung perfusion delay time standard deviation map. S and standard deviation σ S It reflects the condition of a local area of ​​the lungs.

[0114] Furthermore, in order to eliminate the impact of heart rate differences among individuals, this embodiment also calculates a series of quantitative indicators, including a first ratio, a second ratio, a third ratio, and a fourth ratio, to facilitate comparative analysis among different individuals.

[0115] In some specific implementations, the aforementioned regional lung perfusion delay time analysis method also includes:

[0116] Based on the mean μ within the first region of interest M The first ratio is calculated based on the ratio between the cardiac cycle duration and the cardiac cycle length.

[0117] Based on the standard deviation σ within the first region of interest M The second ratio is calculated based on the ratio between the cardiac cycle duration and the cardiac cycle length.

[0118] Based on the mean μ in the second region of interest S The third ratio is calculated based on the ratio between the cardiac cycle duration and the heart rate duration.

[0119] Based on the standard deviation σ in the second region of interest S The fourth ratio is calculated by comparing the ratio between the cardiac cycle duration and the heart rate duration.

[0120] The formula for calculating the first ratio is as follows:

[0121]

[0122] In the formula, The first ratio represents the cardiac cycle duration; the first ratio reflects the spatial average of the regional lung perfusion delay time.

[0123] The second ratio is calculated as follows:

[0124]

[0125] In the formula, The second ratio represents the spatial heterogeneity of the regional lung perfusion delay time.

[0126] The third ratio is calculated as follows:

[0127]

[0128] In the formula, The third ratio represents the temporal instability of the regional lung perfusion delay time.

[0129] The formula for calculating the fourth ratio is as follows:

[0130]

[0131] In the formula, The fourth ratio reflects the temporal instability and spatial heterogeneity of the regional lung perfusion delay time.

[0132] In one example, R M =R s =2, dividing the lung region into left and right lungs to reflect the temporal instability and spatial heterogeneity of pulmonary blood perfusion on both sides of the lungs, respectively. The mean map of regional pulmonary perfusion delay time is divided into a first region of interest (ROI) for the left lung and a first region of interest (ROI) for the right lung, and the standard deviation map of regional pulmonary perfusion delay time is divided into a second region of interest (ROI) for the left lung and a second region of interest (ROI) for the right lung. Furthermore, the above method also includes:

[0133] Based on the mean μ within the first region of interest M Calculate the mean μ between left lung region a and right lung region b, based on the cardiac cycle duration. M Difference ratio:

[0134]

[0135] In the formula, μ represents the region within region a and region b. M The difference ratio is shown in region a (left lung) and region b (right lung). This reflects the overall difference in the mean time of regional lung perfusion delay between the left and right lung regions.

[0136] The method in this embodiment solves the problem of fully utilizing the temporal resolution advantage of electrical impedance imaging (EIA) for image analysis. It can at least reflect the time delay between the calculated regional lung perfusion delay time and the end of ventricular diastole, reflecting the moment when the local blood volume in the lungs reaches its maximum value. Furthermore, based on the calculated and visualized regional lung perfusion delay time mean and standard deviation plots, along with quantitative indicators, it has advantages such as ease of calculation, high sensitivity, and high quantification. This method reflects the temporal instability and spatial heterogeneity of pulmonary blood flow perfusion, assessing abnormalities in pulmonary vascular hemodynamic parameters and aiding in the diagnosis, classification, and treatment of pulmonary vascular diseases.

[0137] Example 2

[0138] Another embodiment of the present invention provides an application example.

[0139] This embodiment provides a mean regional lung perfusion delay time map M for a tested individual. RPD Plot S of the standard deviation of lung perfusion delay time in the region RPD ,like Figure 2 As shown, the region of interest is divided into two parts: the right lung and the left lung. The results were calculated from a 60-second dynamic image of blood perfusion. Figure 2 The table below shows the μ values ​​of the overall and local regions of interest in the lungs. M Ratio, σ M Ratio, μ S ratio and σ S The result of the ratio calculation.

[0140] Example 3

[0141] Another embodiment of the present invention relates to a regional lung perfusion delay time analysis device. The implementation details of this embodiment's regional lung perfusion delay time analysis device are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of this embodiment's regional lung perfusion delay time analysis device can be seen as follows: Figure 3 As shown, it includes an image acquisition module 201, a region separation module 202, a first curve generation module 203, a second curve generation module 204, and a delay time analysis module 205.

[0142] At least one embodiment of the present invention also provides a regional lung perfusion delay time analysis device, comprising:

[0143] Image acquisition module 201 is used to acquire dynamic images of conductivity changes caused by blood perfusion in the human thoracic cavity;

[0144] The region separation module 202 is used to separate the blood flow perfusion images of the cardiac region and the lung region based on the dynamic image of conductivity change;

[0145] The first curve generation module 203 is used to obtain a reference curve of conductivity change in the heart region based on the dynamic image of conductivity change and the blood perfusion image of the heart region.

[0146] The second curve generation module 204 is used to obtain the conductivity change curve of each pixel in the lung region blood perfusion image based on the dynamic image of conductivity change and the lung region blood perfusion image.

[0147] The delay time analysis module 205 is used to divide the conductivity change curve into sliding time windows and calculate the regional lung perfusion delay time within each time window.

[0148] In the specific implementation, acquiring dynamic images of blood perfusion in the human thoracic cavity includes: acquiring the electrical impedance signal of the human thoracic cavity; extracting blood perfusion-related signals from the electrical impedance signal; and generating dynamic images of conductivity changes caused by blood perfusion based on the blood perfusion-related signals.

[0149] In a specific implementation, an electrode array positioned in the region to be measured is used to excite the region, and the resulting response is acquired using an electrical impedance imaging device to obtain the electrical impedance signal. The electrode array can be fixed around the region to be measured, and the arrangement of the electrode array can be in a two-dimensional plane or in three-dimensional space; this embodiment is not limited to any particular arrangement.

[0150] In the specific implementation, a signal extraction algorithm is used to extract blood perfusion-related signals that reflect blood perfusion from the impedance signal.

[0151] The signal extraction algorithm can be any of the following: frequency domain filtering, principal component analysis, or neural network-based methods.

[0152] In one example, frequency domain filtering is used to extract perfusion-related signals reflecting blood flow from the impedance signal. Frequency domain filtering uses a bandpass filter to extract perfusion-related signals from the impedance signal. The parameters of both filters are dynamically adjusted based on the physiological indicators of the subject, such as heart rate. The lower cutoff frequency of the bandpass filter is set slightly less than the heart rate, and the upper cutoff frequency is set slightly greater than n times the heart rate, where n ≥ 1.

[0153] In the specific implementation, an image reconstruction algorithm is used to reconstruct a dynamic image of conductivity changes from blood perfusion-related signals. The dynamic image of conductivity changes can reflect the dynamic changes in conductivity within the test area caused by blood perfusion.

[0154] The image reconstruction algorithm can be linear or nonlinear, iterative or non-iterative, stochastic or deterministic; this embodiment does not impose any limitations. Based on the aforementioned electrode array arrangement, the reconstructed image can be a two-dimensional cross-sectional dynamic image of the lung, or a three-dimensional dynamic image of the lung.

[0155] In one example, a linear differential imaging algorithm is used as the image reconstruction algorithm. The principle of EIT (Electrical Impedance Tomography) differential imaging is: select a reference time, and generate a dynamic image of conductivity changes related to blood perfusion based on the dynamic changes of blood perfusion-related signals at each time relative to the reference time.

[0156] The time-domain form of the impedance signal is denoted as u(t), where t is the time variable. Then, EIT differential imaging can be expressed as the following least squares problem:

[0157]

[0158] In the formula, J represents the Jacobian matrix; δu=u(t)-u(t) ref The impedance signal u(t) represents the impedance signal u(t) at time t relative to the reference time t. ref The impedance signal u(t) ref ); δσ=σ(t)-σ(t ref () indicates the conductivity distribution of the measured region at time t relative to the reference time t. ref The variation of is defined in the discretized model; α represents the regularization parameter; R represents the regularization matrix.

[0159] Therefore, the solution to the above least squares problem is:

[0160] δσ=(J T ·J+αR T ·R) -1 ·J T ·δu

[0161] The above δσ is the dynamic image of conductivity change obtained by calculation. When δu is the change of blood perfusion-related signal in the impedance signal, the obtained δσ is the dynamic image of conductivity change caused by blood perfusion.

[0162] In one example, a 60-second blood perfusion-related signal was extracted, and a dynamic image of the conductivity change in a two-dimensional cross-section of the target lung region was obtained using a two-dimensional linear differential imaging algorithm.

[0163] In the specific implementation, a cardiopulmonary separation method based on blood perfusion images is used to generate blood perfusion images of the cardiac region and the lung region. The cardiopulmonary separation method based on blood perfusion images can be linear regression or K-Means clustering; this embodiment is not limited to either.

[0164] In the specific implementation, a reference curve for conductivity change in the heart region is obtained based on the dynamic image of conductivity change and the blood perfusion image of the heart region. This includes: extracting the region of interest in the heart region from the blood perfusion image of the heart region; calculating the average curve of conductivity change curves of all pixels in the region of interest in the dynamic image of conductivity change, and determining the average curve as the reference curve for conductivity change in the heart region.

[0165] Specifically, regions with pixel values ​​greater than a specified threshold in the blood perfusion image of the heart region are defined as regions of interest in the heart, and regions with pixel values ​​greater than a specified threshold in the blood perfusion image of the lung region are defined as regions of interest in the lung, thus achieving effective extraction of regions of interest in the heart and lung.

[0166] In one example, the specified threshold for defining the region of interest in the heart is 0.95, and the specified threshold for defining the region of interest in the lungs is 0.25.

[0167] In one example, the above average curve is calculated using the following formula:

[0168]

[0169] In the formula, x ref Reference curve representing the change in conductivity in the cardiac region, x i This represents the conductivity change curve at the i-th pixel in a dynamic image of conductivity changes caused by blood perfusion, Ω. H For the region of interest of the heart, N H denoted as the number of pixels belonging to the region of interest of the heart, and n represents the discrete time.

[0170] In some cases, signal interpolation can be used to reconstruct conductivity variation curves. The signal interpolation method can be either sinc interpolation or cubic spline interpolation.

[0171] In one example, the frame rate of the electrical impedance imaging device is 20 frames per second. If a dynamic image of conductivity change is captured for 60 seconds, then the reference curve of conductivity change in the heart region and the conductivity change curve of each pixel in the lung region both have 1200 data points. Using the cubic spline interpolation method, the sampling frequency is increased by 5 times, that is, the reference curve of conductivity change in the heart region and the conductivity change curve of each pixel in the lung region both become 6000 data points.

[0172] In the specific implementation, the following calculation formula is used to calculate the regional lung perfusion delay time within each time window.

[0173]

[0174] In the formula, This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window in the lung region blood perfusion image. This represents a reference curve showing the change in conductivity within the k-th time window. The curve representing the conductivity change of the i-th pixel in the lung perfusion image within the k-th time window is denoted by n, where n represents the discrete time and τ represents the delay time.

[0175] Sliding time windows can be segmented in an overlapping manner.

[0176] In one example, the sliding time window is 10 seconds long, the overlap length is 8 seconds, and the sliding step is 2 seconds. For a conductivity change curve with a duration of 60 seconds, it can be divided into 26 time windows. For each pixel in the lung region, the regional lung perfusion delay time within each time window is calculated.

[0177] In some cases, the delay time analysis module 205 is also used to generate a regional lung perfusion delay time mean map and a regional lung perfusion delay time standard deviation map based on the regional lung perfusion delay time.

[0178] Based on the regional lung perfusion delay time, mean plots and standard deviation plots of regional lung perfusion delay time are calculated, providing a visualization method for assessing the temporal instability and spatial heterogeneity of pulmonary blood flow perfusion.

[0179] In the specific implementation, the mean lung perfusion delay time of the i-th pixel in the mean lung perfusion delay time map is calculated using the following formula:

[0180]

[0181] In the formula, denoted by , represents the mean lung perfusion delay time at the i-th pixel in the lung perfusion image, where K represents the number of time windows. This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window of the lung regional blood perfusion image. The mean regional lung perfusion delay time plot is used to visualize the mean regional lung perfusion delay time for each pixel.

[0182] The standard deviation of the regional lung perfusion delay time at the i-th pixel in the regional lung perfusion delay time standard deviation map can be calculated using the following formula:

[0183]

[0184] In the formula, The standard deviation of the regional lung perfusion delay time at the i-th pixel in the lung perfusion image is represented by K, where K represents the number of time windows. This represents the regional lung perfusion delay time of the i-th pixel in the k-th time window in the lung region blood perfusion image. This represents the mean lung perfusion delay time of the i-th pixel in the lung perfusion image. The regional lung perfusion delay time standard deviation plot is used to visualize the standard deviation of the regional lung perfusion delay time.

[0185] In some cases, the mean map of regional lung perfusion delay time is divided into R... M For each first region of interest, the mean μ of the mean lung perfusion delay time for each pixel within that region of interest is calculated. M and standard deviation σ M The regional lung perfusion delay time standard deviation map is divided into R... S For each second region of interest, the mean μ of the regional lung perfusion delay time standard deviation for each pixel is calculated. S and standard deviation σ S ;

[0186] Among them, R M ≥1, R s ≥1.

[0187] In one example, let R be... M =R S =1 and R M =R S =2, simultaneously assessing the temporal instability and spatial heterogeneity of pulmonary blood flow perfusion in the whole / local lung regions; in R M =R S When = 1, the mean value μ of the pixel values ​​in the calculated mean map of regional lung perfusion delay time. M and standard deviation σ M and the mean μ of the pixel values ​​in the regional lung perfusion delay time standard deviation map. S and standard deviation σ S It reflects the overall condition of the lungs; in R M =R S When = 2, the mean value μ of the pixel value of the calculated mean map of regional lung perfusion delay time. M and standard deviation σ M and the mean μ of the pixel values ​​in the regional lung perfusion delay time standard deviation map. S and standard deviation σ S It reflects the condition of a local area of ​​the lungs.

[0188] Furthermore, in order to eliminate the impact of heart rate differences among individuals, this embodiment also calculates a series of quantitative indicators, including a first ratio, a second ratio, a third ratio, and a fourth ratio, to facilitate comparative analysis among different individuals.

[0189] In some specific implementations, the aforementioned delay analysis module 205 is also used for:

[0190] Based on the mean μ within the first region of interest M The first ratio is calculated based on the ratio between the cardiac cycle duration and the cardiac cycle length.

[0191] Based on the standard deviation σ within the first region of interest M The second ratio is calculated based on the ratio between the cardiac cycle duration and the cardiac cycle length.

[0192] Based on the mean μ in the second region of interest S The third ratio is calculated by comparing the ratio between the cardiac cycle duration and the heart rate duration; and

[0193] Based on the standard deviation σ in the second region of interest S The fourth ratio is calculated by comparing the ratio between the cardiac cycle duration and the heart rate duration.

[0194] The formula for calculating the first ratio is as follows:

[0195]

[0196] In the formula, The first ratio represents the cardiac cycle duration; the first ratio reflects the spatial average of the regional lung perfusion delay time.

[0197] The second ratio is calculated as follows:

[0198]

[0199] In the formula, The second ratio represents the spatial heterogeneity of the regional lung perfusion delay time.

[0200] The third ratio is calculated as follows:

[0201]

[0202] In the formula, The third ratio represents the temporal instability of the regional lung perfusion delay time.

[0203] The formula for calculating the fourth ratio is as follows:

[0204]

[0205] In the formula, The fourth ratio reflects the temporal instability and spatial heterogeneity of the regional lung perfusion delay time.

[0206] In one example, R M =R s =2, dividing the lung region into left and right lungs to reflect the temporal instability and spatial heterogeneity of pulmonary blood perfusion on both sides of the lungs, respectively. The mean map of regional pulmonary perfusion delay time is divided into a first region of interest (ROI) for the left lung and a first region of interest (ROI) for the right lung, and the standard deviation map of regional pulmonary perfusion delay time is divided into a second region of interest (ROI) for the left lung and a second region of interest (ROI) for the right lung. Furthermore, the above method also includes:

[0207] Based on the mean μ within the first region of interest M Calculate the mean μ between left lung region a and right lung region b, based on the cardiac cycle duration. M Difference ratio:

[0208]

[0209] In the formula, μ represents the region within region a and region b. M The difference ratio is shown in region a (left lung) and region b (right lung). This reflects the overall difference in the mean time of regional lung perfusion delay between the left and right lung regions.

[0210] The device in this embodiment solves the problem of fully utilizing the temporal resolution advantage of electrical impedance imaging technology for image analysis. It can at least reflect the time delay between the calculated regional lung perfusion delay time and the end of ventricular diastole, reflecting the moment when the local blood volume in the lungs reaches its maximum value. Furthermore, based on the calculated and visualized regional lung perfusion delay time mean and standard deviation plots, along with quantitative indicators, it has advantages such as ease of calculation, high sensitivity, and high degree of quantification. It is used to reflect the temporal instability and spatial heterogeneity of pulmonary blood flow perfusion, to assess abnormalities in pulmonary vascular hemodynamic parameters, and is helpful for the diagnosis, classification, and treatment of pulmonary vascular diseases.

[0211] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0212] Example 4

[0213] Another embodiment of the present invention relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the regional lung perfusion delay time analysis method of the above embodiments.

[0214] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0215] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0216] Example 5

[0217] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the above-described embodiment of the regional lung perfusion delay time analysis method.

[0218] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0219] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A method for analyzing regional lung perfusion delay time, characterized in that, include: To obtain dynamic images of conductivity changes caused by blood perfusion in the human thoracic cavity; Based on the dynamic image of conductivity change, the blood perfusion image of the cardiac region and the blood perfusion image of the lung region are separated. Based on the dynamic image of conductivity change and the blood perfusion image of the cardiac region, a reference curve of conductivity change in the cardiac region is obtained; Based on the dynamic image of conductivity changes and the image of blood perfusion in the lung region, a conductivity change curve for each pixel in the image of blood perfusion in the lung region is obtained. The conductivity change curve is then divided into sliding time windows, and the regional lung perfusion delay time is calculated within each time window using the following formula. In the formula, The image representing the blood perfusion in the lung region. The pixel at the th point Regional lung perfusion delay time within a time window Indicates the first Reference curves of conductivity changes within a time window The image representing the blood perfusion in the lung region. The pixel at the th point Conductivity variation curves within a time window n Represents discrete time; Indicates the delay time.

2. The method for analyzing regional lung perfusion delay time according to claim 1, characterized in that, The acquisition of dynamic images of conductivity changes caused by intrathoracic blood perfusion includes: Acquire the electrical impedance signal of the human thoracic cavity; Extract blood perfusion-related signals from the impedance signals; A dynamic image of conductivity changes is generated based on the blood perfusion-related signals.

3. The method for analyzing regional lung perfusion delay time according to claim 1, characterized in that, Based on the dynamic image of conductivity change and the blood perfusion image of the cardiac region, a reference curve of conductivity change in the cardiac region is obtained, including: Extract the region of interest (ROI) from the cardiac perfusion image of the cardiac region; The average curve of the conductivity change curves of all pixels in the region of interest of the heart is calculated in the dynamic image of conductivity change, and the average curve is determined as the reference curve of conductivity change of the heart region.

4. The method for analyzing regional lung perfusion delay time according to claim 1, characterized in that, Also includes: A mean map of regional lung perfusion delay time and a standard deviation map of regional lung perfusion delay time are generated based on the regional lung perfusion delay time.

5. The method for analyzing regional lung perfusion delay time according to claim 4, characterized in that, Also includes: The mean lung perfusion delay time map of the region is divided into... Within a first region of interest, the mean of the mean lung perfusion delay time for each pixel is calculated. and standard deviation ; The standard deviation map of lung perfusion delay time in the aforementioned region is divided into... For each second region of interest, the mean of the standard deviation of the regional lung perfusion delay time for each pixel is calculated. and standard deviation ; in, , .

6. The method for analyzing regional lung perfusion delay time according to claim 5, characterized in that, Also includes: Based on the mean value within the first region of interest The first ratio is calculated based on the ratio between the cardiac cycle duration and the cardiac cycle length. Based on the standard deviation within the first region of interest The second ratio is calculated based on the ratio between the cardiac cycle duration and the cardiac cycle length. Based on the mean value within the second region of interest The third ratio is calculated based on the ratio between the cardiac cycle duration and the heart rate duration. Based on the standard deviation within the second region of interest The fourth ratio is calculated by comparing the ratio between the cardiac cycle duration and the heart rate duration.

7. The method for analyzing regional lung perfusion delay time according to claim 5, characterized in that, exist In this case, the mean map of regional lung perfusion delay time is divided into the first region of interest in the left lung region and the first region of interest in the right lung region, and the standard deviation map of regional lung perfusion delay time is divided into the second region of interest in the left lung region and the second region of interest in the right lung region. The method further includes: Based on the mean value within the first region of interest Calculate the mean between the left and right lung regions based on the duration of the cardiac cycle. Difference ratio.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the regional lung perfusion delay time analysis method as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the regional lung perfusion delay time analysis method according to any one of claims 1 to 7.

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