Focused cardiac electrical impedance tomography method

Abstract

The application provides a focused cardiac electrical impedance imaging method, comprising the following steps: S1, obtaining a high-resolution medical image of a chest; S2, extracting a chest cavity and a heart region; S3, calculating a focused sensor; S4, processing and installing the sensor; S5, collecting EIT measurement voltage data; S6, according to the measurement voltage data, realizing chest conductivity image reconstruction according to an image reconstruction algorithm; and S7, generating a high-precision heart-lung conductivity image. The focused cardiac electrical impedance imaging method is based on a conformal transformation method, and realizes focused EIT sensor design under the condition that a subject's chest contour and heart region are available, and then, in combination with a data acquisition system and an image reconstruction algorithm, high-precision cardiac electrical impedance imaging is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of heart imaging, and particularly relates to a focused heart electrical impedance imaging method. BACKGROUND

[0002] Heart disease has high mortality and a wide range of people, which seriously affects people's health. As an organ composed of highly specialized muscles, evaluating the systolic function of the heart and the resulting pump function on the blood circulation system is an effective way to evaluate heart health. For example, local myocardial functional degradation leading to heart failure and other symptoms can be diagnosed through cardiac ejection fraction, cardiac output and other cardiac function parameters. Therefore, continuous and long-term monitoring of heart function is of great significance for reducing the incidence of acute cardiovascular events in patients with heart disease, improving the level of precise diagnosis and treatment of patients and improving the prognosis.

[0003] Electrical impedance tomography (EIT) is a modern non-invasive imaging method that applies current or voltage excitation signals to the observation field through electrodes installed at the boundary of the observation field, collects voltage or current response signals on the electrodes, and then obtains the conductivity distribution image in the observation field through image reconstruction method. Compared with other imaging technologies such as CT and MRI, EIT has the advantages of low cost, portable equipment, high time resolution, and no radiation, and has received widespread attention in the field of biomedical imaging. The volume change caused by cardiac contraction can be reflected by its conductivity distribution, so EIT can be used for monitoring and imaging. In 2000, Vonk-Noordegraaf et al. calculated the stroke volume (SV) by monitoring the conductivity change of the heart region in the EIT image during the cardiac cycle. The experimental results proved that EIT is an effective and repeatable method for evaluating SV. In 2003, Fu Feng et al. used EIT to image an isolated animal heart immersed in culture solution, and verified the feasibility of EIT in monitoring heart function by simulating different filling states of the heart. It is pointed out that the EIT image is sensitive to the electrical impedance change of the heart chamber, which proves the potential application value of EIT in cardiovascular system imaging. In 2015, Proenca et al. based on single-sample dynamic simulation research pointed out that the electrical impedance change of the ventricle is mainly caused by the deformation of the left ventricle. In 2019, Braun et al. proved that by analyzing the synchronous impedance change of the lung and heart, the change of SV in critically ill patients during fluid resuscitation can be monitored non-invasively.

[0004] In the reported cardiac imaging applications, the electrodes of EIT always have the same size and are uniformly placed on the boundary of a certain cross-section of the chest (in this document we call this electrode configuration method as uniform electrode method, and the electrical impedance imaging based on the uniform electrode method is traditional electrical impedance imaging). Due to the "soft field characteristics" of the electric field, traditional electrical impedance imaging is not sensitive to the conductivity changes far away from the electrodes, such as the conductivity changes of the heart, and cannot achieve high-precision imaging of the heart. SUMMARY

[0005] Therefore, the present application aims to provide a focused cardiac electrical impedance imaging method to improve the imaging accuracy of EIT for the conductivity changes in the heart region.

[0006] To achieve the above-mentioned purpose, the technical scheme of the present application is as follows:

[0007] The focused cardiac electrical impedance imaging method comprises the following steps:

[0008] S1, obtaining a high-resolution medical image of the chest;

[0009] S2, extracting the chest and heart region;

[0010] S3, focused sensor calculation;

[0011] S4, sensor processing and installation;

[0012] S5, collecting EIT measurement voltage data;

[0013] S6, according to the measurement voltage data, according to the image reconstruction algorithm, realizing the chest conductivity image reconstruction;

[0014] S7, generating a high-precision heart and lung conductivity image.

[0015] Further, in step S1, the medical image of the chest of the subject is obtained by CT or MRI.

[0016] Further, in step S2, the medical image of the chest of the subject obtained in step 1 is analyzed to determine the cross-sectional position of the chest for EIT electrode installation;

[0017] An image segmentation method is used to segment the medical image of the cross-section of the chest of the subject for EIT electrode design, extract the contour of the chest of the subject in the image, and calculate the center position of the heart region.

[0018] Further, the image segmentation method includes threshold-based method, edge-based method, clustering-based method, graph theory-based method, and machine learning-based method.

[0019] Further, in step S3, the following steps are included:

[0020] A1, define the plane where the chest contour and the heart region are located as the (x, y) plane;

[0021] A2, in the (u, v) plane, design the electrodes on the unit circle according to the uniform electrode distribution principle;

[0022] A3, according to the mapping relationship g calculated in step A1, map the electrodes in the (u, v) plane to the (x, y) plane, to realize the focused electrode design.

[0023] Further, in step A1, the chest contour in the (x, y) plane is mapped to the unit circle in the (u, v) plane by using the conformal transformation, and the center of the heart region in the (x, y) plane is mapped to the center of the unit circle in the (u, v) plane, and then the mapping relationship from the (x, y) plane to the (u, v) plane is recorded as f, and the mapping relationship from the (u, v) plane to the (x, y) plane is recorded as g.

[0024] Further, in step S5, the focused sensor in step S4 is worn on the chest of the subject according to the sensor design result in step S4, and the EIT measurement voltage data is collected based on the EIT data acquisition system.

[0025] Further, in step S7, the heart conductivity change image in the chest conductivity image is analyzed, and the heart function detection is realized.

[0026] An electronic device comprises a processor and a memory connected with the processor and used for storing executable instructions of the processor, and the processor is used for executing a focused cardiac electrical impedance imaging method.

[0027] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize a focused cardiac electrical impedance imaging method.

[0028] Compared with the prior art, the focused cardiac electrical impedance imaging method has the following beneficial effects:

[0029] The focused cardiac electrical impedance imaging method realizes the focused EIT sensor design based on the conformal transformation method under the condition that the chest contour and the heart region of the subject are available, and then realizes the high-precision cardiac conductivity imaging by combining the data acquisition system and the image reconstruction algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0030] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments illustrated in the drawings are provided to explain the present application and should not be considered limiting of the present application. In the drawings:

[0031] Figure 1 The schematic diagram of the processing unit included in the present application;

[0032] Figure 2 The pre-processing of the CT image (a) binarization image (b) thoracic cavity and heart boundary curve;

[0033] Figure 3 The calculation process for the focused sensor (a) conformal mapping f from the subject's thoracic coordinate system (x, y) to the coordinate system of the unit circle (u, v), (b) mapping g from the coordinate system of the unit circle (u, v) to the subject's thoracic coordinate system (x, y);

[0034] Figure 4 The comparison of the results of the uniform and focused cardiac electrical impedance imaging;

[0035] Figure 5 The comparison of the normalized pixel value and the true area trend of the cardiac region of the results of the uniform and focused cardiac electrical impedance imaging;

[0036] Figure 6 The schematic diagram of the performance comparison of the uniform and focused cardiac electrical impedance imaging according to the embodiment of the present application. DETAILED DESCRIPTION

[0037] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0038] The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] The present application, under the condition that the subject's thoracic profile and heart region are available, realizes the design of the focused EIT sensor based on the conformal transformation method, and further realizes the high-precision cardiac conductance imaging by combining the data acquisition system and the image reconstruction algorithm. Figure 1 The flowchart for realizing the high-precision cardiac imaging by optimizing the sensor in the present application mainly includes the following steps:

[0040] Step one: obtain the CT image of the subject's chest through the CT imaging device.

[0041] Step two: select the CT cross-sectional image at the fifth rib closest to the nipple through the medical image analysis software, and take the image as the thoracic cross-sectional image for EIT electrode design.

[0042] Step three: adopt the automatic threshold segmentation method to perform image segmentation, and perform binarization on the segmented image to extract the boundary of the thoracic cavity and heart region from the subject's thoracic cross-sectional medical image. Figure 2 The binarized CT image is given in (a),Figure 2 (b) shows the boundary curves of the thoracic cavity and heart extracted from the binary image. The center of the heart region is determined based on the obtained heart boundary.

[0043] Step 4: Define the plane containing the thoracic cavity contour and the heart region as the (x, y) plane. Using the Schwarz-Christoffel transformation, map the thoracic cavity contour in the (x, y) plane onto the unit circle in the (u, v) plane, ensuring that the center of the heart region in the (x, y) plane is mapped to the center of the unit circle in the (u, v) plane. This mapping relationship is denoted as f. The specific effect is shown below. Figure 3 As shown in (a). Calculate the inverse mapping of mapping f, denoted as g, with the specific effect as follows. Figure 3 As shown in (b). Based on the actual application scenario, a uniformly distributed electrode array is designed on the unit circle. Figure 3 (b) presents the design results for 16 electrodes, where the ratio of electrode width to inter-electrode gap width is 55:45. Based on the mapping function g, the uniformly distributed electrodes in the (u, v) plane are mapped to the (x, y) plane to obtain the focusing electrodes for the cardiac region in the (x, y) plane.

[0044] The Schwarz-Christoffel mapping formula is:

[0045]

[0046] Among them, z j =(x j ,y j ) is the j-th boundary point of the original domain, α j Let be the interior angle of the j-th boundary point after mapping, and δ be a discrete point on the boundary of the z = (x, y) domain. In the formula, z0 and c are complex constants. For j = 1, 2, ..., n, the following formula always holds:

[0047] w j =f(z) j )

[0048] Among them, w j =(u j ,v j ) is the j-th boundary point of the mapping domain.

[0049] Step 5: Process the sensor according to the design scheme. The sensor is processed by combining the base and the electrode ring. The thoracic base is made of resin material by 3D printing. The electrode ring is composed of a resin ring and copper electrodes that are quickly manufactured by cutting. Different electrode rings can be made to select different electrode arrays. The rapid manufacturing of the focusing sensor is achieved through flexible assembly.

[0050] Step six: The 16-channel high-speed parallel data acquisition system is used in this embodiment, and the voltage measurement data is collected through the electrode array according to the strategy of adjacent current excitation and adjacent voltage measurement.

[0051] Step seven: The total variation regularization method is used in this embodiment for the conductivity image reconstruction. The total variation regularization is a classical EIT image reconstruction algorithm, and the minimization objective function is as follows:

[0052]

[0053] wherein, V is the calculated voltage value, sigma is the conductivity, y is the measured voltage value change, x is the conductivity change, and lambda and theta are super parameters selected by experience.

[0054] Figure 5 The imaging results of the traditional uniform sensor and the focused sensor are given in the prior art. It can be seen that the beating process of the heart of the uniform electrode model is more reflected in the position change, and due to the imaging artifact near the electrode, the reconstructed result of the heart at the end of the contraction is larger, which is contrary to the actual situation. However, the method proposed in the present application can more intuitively reflect the contraction process of the heart through the change of the pixel value after optimization.

[0055] In order to further evaluate the performance difference between the sensor of the present application and the traditional sensor, the image correlation coefficient (CC) and the relative error (RE) are used to evaluate the imaging effect. The specific definitions of CC and RE are as follows:

[0056]

[0057]

[0058] The formula is changed to x, which is consistent with the previous formula. The meanings of the variables in the formula are explained. Among them, alpha k is a binary vector of the reconstructed image conductivity distribution, and the maximum element value in the vector is taken as a threshold value, alpha i is the i-th element in alpha, alpha ki is the i-th element in alpha k . l α represents the length of the vector alpha, alpha kx represents the x coordinate of the reconstructed image centroid, alpha ky represents the y coordinate of the reconstructed image centroid. Similarly, alpha x is the x coordinate of the original image centroid, and alpha yis its y coordinate. The quantitative indicators RE and CC are used in Table 1 to evaluate the reconstruction results obtained from the experiment. From the results, the optimized model has a higher CC value and a lower RE. It shows that the present application can achieve higher accuracy of cardiac region reconstruction and effectively improve the image reconstruction quality.

[0059] In order to more intuitively describe the dynamic process of the heartbeat cycle, an EIT-based cardiac volume evaluation index ACI (average cardiac index) is proposed, which represents the average conductivity value of the cardiac region of the reconstructed image. The average value is calculated by extracting the reconstructed pixels of the cardiac region. ACI is normalized by the following formula:

[0060]

[0061] where ACI t represents the ACI value at time t, A t is the cardiac volume value at time t, A max and A min are the maximum and minimum cardiac volume values in the observation time, respectively. Figure 5 In Fig. 6, the dotted line represents the true ACI change of the cardiac region, the triangle represents the ACI change obtained by the uniform sensor model, and the round point represents the ACI change obtained by the optimized model. The results show that the optimized model can more accurately reflect the contraction change of the heart, and the obtained ACI change curve trend is more consistent with the change of the original cross-sectional area.

[0062] Those skilled in the art can realize that the units and method steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been described in the above description in general terms. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0063] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other manners. For example, the division of the above-mentioned units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the above-mentioned units can or can not be physical units, and can or can not be distributed on a network. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0064] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.

[0065] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A focused cardiac electrical impedance imaging method, characterized by, The method comprises the following steps: S1, obtaining a high-resolution medical image of the chest, obtaining a CT image of the chest of the subject; S2, extracting the chest and heart region, selecting a CT cross-sectional image at the 5th rib closest to the nipple, and taking the image as the chest cross-sectional image for EIT electrode design; S3, focused sensor calculation; S4, sensor processing and installation; S5, collecting EIT measurement voltage data; S6, according to the measurement voltage data, according to the image reconstruction algorithm, realizing the chest conductivity image reconstruction; S7, generating a high-precision heart and lung conductivity image; In step S3, specifically comprising: Define the plane where the chest contour and the heart region are located as the (x, y) plane, use Schwarz-Christoffel transformation to map the chest contour in the (x, y) plane to the unit circle in the (u, v) plane, and make the center of the heart region in the (x, y) plane be mapped to the center of the unit circle in the (u, v) plane, the mapping relationship is denoted as f, calculate the inverse mapping of the mapping f, denoted as g, design a uniformly distributed electrode array on the unit circle, and map the uniformly distributed electrodes in the (u, v) plane to the (x, y) plane according to the mapping function g, to obtain the focused electrodes in the (x, y) plane; The Schwarz-Christoffel mapping formula is: where z j = (x j ,y j ) is the jth boundary point of the original domain, a j is the interior angle of the jth boundary point after mapping, and d is a discrete point on the boundary of the z = (x, y) domain. In the formula, z0 and c are complex constants, and the following formula always holds for j = 1, 2, … n: w j = f(z j ) where w j = (u j , v j ) is the jth boundary point of the mapping domain.

2. The focused cardiac electrical impedance imaging method of claim 1, wherein: In step S1, the subject's chest medical image is obtained by CT or MRI.

3. The focused cardiac electrical impedance imaging method of claim 1, wherein: In step S2, the chest medical image of the subject obtained in step 1 is analyzed to determine the chest cross-sectional position for EIT electrode installation; An image segmentation method is used to segment the subject's chest cross-sectional medical image for EIT electrode design, extract the subject's chest contour in the image, and calculate the center position of the heart region.

4. The focused cardiac electrical impedance imaging method of claim 3, wherein, The image segmentation method includes threshold-based method, edge-based method, clustering-based method, graph theory-based method, and machine learning-based method.

5. The focused cardiac electrical impedance imaging method of claim 1, wherein: In step S5, the focused sensor in step S4 is worn on the subject's chest according to the sensor design result in step S4, and EIT measurement voltage data is collected based on the EIT data acquisition system.

6. The focused cardiac electrical impedance imaging method of claim 1, wherein: In step S7, the heart conductivity change image over time in the chest conductivity image is analyzed to realize heart function detection.

7. An electronic device, comprising a processor and a memory connected to the processor in communication, and configured to store executable instructions of the processor, characterized in that: The processor is used to execute the focused heart electrical impedance imaging method of any one of claims 1-6.

8. A computer readable storage medium storing a computer program, characterized in that: The computer program is executed by the processor to realize the focused heart electrical impedance imaging method of any one of claims 1-6.

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