Electrocardiogram positive problem solving method and device based on cross-scale heart model

Abstract

The invention discloses an electrocardio positive problem solving method and device based on a cross-scale heart model, and relates to the technical field of heart modeling and electrocardio simulation. The method comprises the steps of obtaining anatomical data based on a medical image, segmenting the medical image, performing three-dimensional reconstruction based on a segmentation result and the anatomical data, and generating a cross-scale heart-trunk model; deriving a heart geometric structure model based on the cross-scale heart-trunk model; performing geometric optimization and mesh generation on the heart geometric structure model to generate a voxel matrix, and constructing an ion channel kinetic equation based on the voxel matrix to realize subcellular level simulation; and mapping the subcellular level simulation result into a cross-scale heart-trunk model, and calculating the propagation process of the cardiac electrical signal in the trunk to obtain the body surface potential distribution. According to the method, forward calculation of the body surface electrocardiosignals can be achieved from the source in a one-stop mode, and methodological reference is provided for cross-scale electrocardio positive problem solving simulation calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of heart modeling and electrocardiogram simulation, and in particular to a method and device for solving electrocardiogram (ECG) direct problems based on a cross-scale heart model. Background Art

[0002] Heart simulation technology integrates medical imaging and real-time physiological data to create a virtual model that can accurately replicate the characteristics of a patient's heart, especially in ECG analysis. Slight deviations in ECG signals can reflect potential cardiac abnormalities, such as myocardial ischemia and arrhythmias, and these changes usually appear earlier than clinical symptoms. Traditional ECGs may not be able to capture these subtle changes, but heart simulation models can accurately simulate and identify these deviations through comprehensive patient data, helping clinicians make early diagnoses and develop personalized treatment plans.

[0003] ECG analysis includes direct and inverse problems. The direct problem is to predict ECG signals based on known cardiac structure and function, while the inverse problem is to infer cardiac status from observed ECG signals. Solving the direct problem is crucial to accurately derive the inverse problem. Solving the inverse problem can support non-invasive diagnosis of cardiac abnormalities, which is especially important for high-risk patients.

[0004] However, the simulation calculation of ECG problems faces many challenges, especially in terms of multi-scale coupling and adaptation. Cross-scale simulation requires effective coordination between the microscopic (cellular level) and macroscopic (tissue level) levels to ensure the accurate transmission of electrical activity and the precise simulation of ECG signals. In addition, individualized modeling also increases the complexity of simulation. The cardiac anatomical structure and physiological characteristics of each patient are different. How to perform individualized modeling while maintaining high accuracy is the bottleneck of current technology. These challenges need to be overcome through more sophisticated modeling methods, interdisciplinary collaboration, and high-performance computing platforms to achieve more accurate and personalized ECG simulation analysis. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides the following technical solutions.

[0006] The first aspect of the present application provides a method for solving an electrocardiogram (ECG) positive problem based on a cross-scale heart model, comprising:

[0007] Acquire anatomical data based on medical images, segment the medical images, perform three-dimensional reconstruction based on the segmentation results and the anatomical data, and generate a cross-scale heart-torso model;

[0008] deriving a heart geometry model based on the cross-scale heart-torso model;

[0009] Perform geometric optimization and meshing on the cardiac geometry model to generate a voxel matrix, and construct ion channel kinetic equations based on the voxel matrix to achieve subcellular-level simulation;

[0010] Map the subcellular-level simulation results to the cross-scale heart-trunk model, calculate the propagation process of cardiac electrical signals in the trunk, and obtain the body surface potential distribution.

[0011] In an alternative embodiment, a segmentation algorithm is used to segment medical images.

[0012] In an alternative embodiment, the medical image is an MRI image or a CT image.

[0013] In an alternative embodiment, the subcellular-level simulation includes simulating the action potential, ion channel kinetics, and electrical signal conduction between cardiomyocytes of cardiomyocytes through an electrophysiological model.

[0014] In an alternative embodiment, the subcellular-level simulation further includes recording the simulation results in the form of a time series, and generating potential data for each myocardial unit based on the recorded time series.

[0015] In an alternative embodiment, the electrophysiological model is the Hodgkin-Huxley model or the Luo-Rudy model.

[0016] In an alternative embodiment, the calculation of the propagation process of cardiac electrical signals in the trunk includes: calculating the propagation process of cardiac electrical signals in the trunk using the finite element method or the boundary element method.

[0017] The second aspect of the present application also provides an electrocardiogram forward problem solving device based on a cross-scale heart model, including:

[0018] A cross-scale heart-trunk model generation module, configured to obtain anatomical data based on medical images, segment the medical images, and perform three-dimensional reconstruction based on the segmentation results and the anatomical data to generate a cross-scale heart-trunk model;

[0019] A cardiac geometry model export module, configured to export a cardiac geometry model based on the cross-scale heart-trunk model;

[0020] A subcellular-level simulation module, configured to perform geometric optimization and meshing on the cardiac geometry model to generate a voxel matrix, and construct ion channel kinetic equations based on the voxel matrix to achieve subcellular-level simulation;

[0021] A body surface potential distribution acquisition module, configured to map the subcellular-level simulation results to the cross-scale heart-trunk model, calculate the propagation process of cardiac electrical signals in the trunk, and obtain the body surface potential distribution.

[0022] The third aspect of the present application also provides a memory storing multiple instructions for implementing the method for solving the forward electrocardiogram problem based on the cross-scale heart model described in the first aspect of the present application.

[0023] The fourth aspect of the present application also provides an electronic device including a processor and a memory connected to the processor. The memory stores multiple instructions that can be loaded and executed by the processor so that the processor can execute the method for solving the forward electrocardiogram problem based on the cross-scale heart model described in the first aspect.

[0024] The beneficial effects of the present invention are as follows: The method and device for solving the forward electrocardiogram problem based on the cross-scale heart model provided by the present invention couple multi-scale heart models from two levels of structure and electrophysiological principles, and can solve the forward calculation of body surface electrocardiogram signals from the source in one stop, providing a methodological reference for cross-scale simulation calculation of solving the forward electrocardiogram problem. Description of the Drawings

[0025] Figure 1 is a schematic flowchart of the method for solving the forward electrocardiogram problem based on the cross-scale heart model described in the present invention;

[0026] Figure 2 is a schematic diagram of the cross-scale heart-trunk model established by using a one-dimensional heart model and the simulated 12-lead electrocardiogram obtained in the present invention;

[0027] Figure 3 is a schematic diagram of the functional structure of the device for solving the forward electrocardiogram problem based on the cross-scale heart model described in the present invention. Detailed Embodiments

[0028] To better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0029] The method provided by the present invention can be implemented in the following terminal environment. The terminal may include one or more of the following components: a processor, a memory, and a display screen. Among them, at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor to implement the method described in the following embodiments.

[0030] The processor may include one or more processing cores. The processor connects various parts inside the terminal through various interfaces and lines, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by calling data stored in the memory.

[0031] The memory may include a Random Access Memory (RAM), and may also include a Read-Only Memory (ROM). The memory can be used to store instructions, programs, codes, code sets or instructions.

[0032] The display screen is used to display the user interfaces of various applications.

[0033] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, etc., which will not be elaborated here.

[0034] Embodiment 1

[0035] As Figure 1 shown, an electrocardiogram forward problem solving method based on a cross-scale heart model provided by an embodiment of the present invention includes:

[0036] S101, obtaining anatomical data based on medical images, segmenting the medical images, and performing three-dimensional reconstruction based on the segmentation results and the anatomical data to generate a cross-scale heart-trunk model;

[0037] Specifically, first, obtain anatomical data based on medical images such as magnetic resonance imaging (MRI) or computed tomography (CT), and use a segmentation algorithm to segment the medical images into different anatomical structure regions, such as the heart, lungs, ribs, and spine, etc.; then, perform three-dimensional reconstruction based on the segmentation results and the anatomical data to generate a cross-scale heart-trunk model that includes the complete trunk and heart. The cross-scale heart-trunk model includes anatomical structure models at the trunk level, tissue and organ level, and subcellular level, and at least includes the three-dimensional stereoscopic structures of the heart and the trunk. Other tissue and organs such as the lungs, abdominal cavity, spine, ribs, liver, and kidneys can also be reconstructed and added to the model; the subcellular-level anatomical structure model can be a simple single-ventricle model, or a double-ventricle model, an atrium model, or a whole-heart model that includes both ventricles and atria; the format of the subcellular-level anatomical structure model can be a voxelized structure or a faceted mesh structure. Among them, when performing three-dimensional reconstruction based on the segmentation results and the anatomical data, it can be based on manual marker segmentation, or can be combined with artificial intelligence algorithms and implemented with the help of artificial neural networks; during the process of three-dimensional reconstruction to generate the cross-scale heart-trunk model, geometric optimization and smoothing processing are performed on the image data to ensure the mesh quality and accuracy of the cross-scale heart-trunk model and lay a foundation for subsequent calculations.

[0038] S102, deriving a heart geometric structure model based on the cross-scale heart-trunk model;

[0039] Specifically, based on the cross-scale heart-trunk model, a heart geometric structure model is derived for further independent modeling and refinement of the heart. The heart structure in the heart geometric structure model requires higher-resolution segmentation to ensure the inclusion of key anatomical details, such as ventricles, atria, and myocardial layered structures, including endocardial cells, middle-layer cells, and epicardial cells. The thickness ratio of these three types of cells can be 1:2:1 or a more accurate anatomical structure ratio obtained from future research. Similarly, according to requirements, the heart geometric structure model can also be simplified to a structure composed of cardiomyocytes.

[0040] S103. Geometrically optimize and mesh the heart geometric structure model to generate a voxel matrix, and construct ion channel kinetic equations based on the voxel matrix to achieve subcellular-level simulation.

[0041] Specifically, after geometric optimization and meshing of the heart geometric structure model, it is discretized into a voxel matrix, providing a data basis for subcellular-level simulation. The subcellular-level simulation includes: simulating the action potential, ion channel kinetics, and electrical signal conduction between cardiomyocytes through electrophysiological models such as the classical Hodgkin-Huxley model or the Luo-Rudy model (a mammalian ventricular myocyte model based on the Hodgkin-Huxley equation for computer simulation research). Then, record the simulation results in the form of a time series, and generate the potential data of each myocardial unit based on the recorded time series.

[0042] It should be noted that electrophysiological models are generally based on the human ventricular electrophysiological model, which has a relatively recognized mathematical expression formula. This model can also consider future scientific research for further correction and improvement of the existing model. Electrophysiological models can originate from cardiomyocytes or more complex situations, such as originating from more underlying structures such as Purkinje fibers and sinoatrial nodes.

[0043] S104. Map the subcellular-level simulation results into the cross-scale heart-trunk model, calculate the propagation process of heart electrical signals in the trunk, and obtain the body surface potential distribution.

[0044] Specifically, map the subcellular-level simulation results into the cross-scale heart-trunk model, and use the finite element method or boundary element method to calculate the propagation process of cardiac electrical signals in the trunk to obtain the body surface potential distribution; wherein, the body surface potential distribution includes the potential distribution map on the body surface or the dynamic body surface potential change map, and can further calculate and generate a 12-lead electrocardiogram, clearly reflecting the spatio-temporal change characteristics of cardiac electrical activity and the differences between leads. In the specific calculation process, based on the finite element method, by defining the electrical parameters of tissue components and meshing the cross-scale heart-trunk model, the discrete grid points form a dense potential field on the body surface, and by solving the partial differential equation of electrical activity (such as Laplace equation or reaction-diffusion equation), based on algorithms such as boundary element method and finite volume method, calculate the transmission results of all myocardial cell electrical signals to the body surface to obtain the body surface potential distribution.

[0045] It should be noted that in an embodiment of the present application, as Figure 2 shown, use a one-dimensional heart model to establish the cross-scale heart-trunk model, wherein the trunk model in the cross-scale heart-trunk model is a three-dimensional volume model derived from the real human anatomical structure, perform smoothing processing and mesh optimization on the segmented trunk structure, remove the reverse meshes therein, for successfully importing into the finite element analysis software; the size of the trunk model is reduced by 0.85 times, and then perform Boolean operation to generate the complement set; subsequently, the obtained volume is defined as the fat layer, and the reduced trunk model represents skeletal muscle. A similar process is also used to construct the lung model. The heart model in the cross-scale heart-trunk model contains one hundred myocardial units, including twenty-five endocardial myocardial units, thirty-five middle myocardial units and forty epicardial myocardial units, and the distance between every two units is 0.15 cm. The endocardial myocardial units are along the transverse plane and perpendicular to the body surface; the potential value of each endocardial myocardial unit at each time sampling point is obtained from the subcellular simulation results. Use the free tetrahedron method to mesh the cross-scale heart-trunk model, and the result is 241347 domain units, 26148 boundary units and 781 edge units. The mesh size used is a trade-off between calculation accuracy and complexity; 16841 nodes are meshed on the trunk surface, and the simulation results of these nodes are used for further processing. Based on the quasi-static approximation, use the Maxwell equation to determine the potential distribution in the biological model. In the finite element simulation, the myocardial units are approximated as point sources. Based on the potential changes observed at nine body surface electrode positions, a 12-lead electrocardiogram (12-lead ECG) is obtained, and the simulated 12-lead electrocardiogram clearly depicts the morphology of the QRS complex and T wave (as Figure 2 shown on the right), highlighting the polarity differences between different lead positions. The R-R interval obtained from the 12-lead electrocardiogram is consistent with the setting (800 milliseconds) in the single cell model.

[0046] The method provided by this application presents a complete framework for multi-scale modeling and feedback simulation from macro to micro. It integrates the individual medical image data of patients, combines cross-scale electrophysiological modeling and simulation technologies, and can accurately simulate the manifestation of cardiac electrical activity on the body surface. Based on millimeter-scale torso models, sub-millimeter-scale heart models, and micrometer-scale cardiomyocyte models, through multi-level modeling and simulation, it realizes accurate cross-scale coupling. Through the method provided by this application, the complex mechanism of cardiac electrical activity can be effectively revealed, providing advanced computational tools and methods for cardiac electrophysiology research.

[0047] Example Two

[0048] As Figure 3 shown, another aspect of the present invention further includes a functional module architecture that completely corresponds to the foregoing method flow. That is, the embodiment of the present invention also provides a device for solving the forward electrocardiogram problem based on a cross-scale heart model, including:

[0049] A cross-scale heart-torso model generation module 201, configured to obtain anatomical data based on medical images, segment the medical images, and perform three-dimensional reconstruction based on the segmentation results and the anatomical data to generate a cross-scale heart-torso model;

[0050] A heart geometric structure model export module 202, configured to export a heart geometric structure model based on the cross-scale heart-torso model;

[0051] A subcellular-level simulation module 203, configured to perform geometric optimization and mesh division on the heart geometric structure model to generate a voxel matrix, and construct an ion channel kinetic equation based on the voxel matrix to achieve subcellular-level simulation;

[0052] A body surface potential distribution acquisition module 204, configured to map the subcellular-level simulation results into the cross-scale heart-torso model, calculate the propagation process of cardiac electrical signals in the torso, and obtain the body surface potential distribution.

[0053] This device can be implemented by the method for solving the forward electrocardiogram problem based on a cross-scale heart model provided in the foregoing Example One. For the specific implementation method, reference can be made to the description in Example One, which will not be elaborated here.

[0054] The present invention also provides a memory storing multiple instructions for implementing the method for solving the forward electrocardiogram problem based on a cross-scale heart model as described in Example One.

[0055] The present invention also provides an electronic device, including a processor and a memory connected to the processor, where the memory stores a plurality of instructions that can be loaded and executed by the processor, so that the processor can execute the electrocardiogram forward problem solving method based on the cross-scale heart model as described in Embodiment 1.

[0056] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A method for solving ECG positive problems based on a cross-scale heart model, characterized in that ,include: Acquire anatomical data based on medical images, segment the medical images, perform three-dimensional reconstruction based on the segmentation results and the anatomical data, and generate a cross-scale heart-torso model; deriving a heart geometry model based on the cross-scale heart-torso model; Performing geometric optimization and meshing on the heart geometric structure model to generate a voxel matrix, and constructing an ion channel kinetic equation based on the voxel matrix to realize subcellular level simulation; The subcellular level simulation results are mapped to the cross-scale heart-trunk model, the propagation process of cardiac electrical signals in the trunk is calculated, and the surface potential distribution is obtained.

2. The method for solving ECG positive problem based on cross-scale heart model according to claim 1, characterized in that: Segmentation algorithms are used to segment medical images.

3. The method for solving ECG positive problem based on cross-scale heart model according to claim 1, characterized in that: The medical image is an MRI image or a CT image.

4. The method for solving ECG positive problem based on cross-scale heart model according to claim 1, characterized in that: The subcellular level simulation includes simulating the action potential of cardiomyocytes, ion channel dynamics and electrical signal conduction between cardiomyocytes through an electrophysiological model.

5. The method for solving ECG positive problem based on cross-scale heart model according to claim 4, characterized in that: The subcellular level simulation also includes recording the simulation results in the form of a time series, and generating the electric potential data of each myocardial unit based on the recorded time series.

6. The method for solving ECG positive problem based on cross-scale heart model according to claim 4, characterized in that: The electrophysiological model is the Hodgkin-Huxley model or the Luo-Rudy model.

7. The method for solving ECG positive problem based on cross-scale heart model according to claim 1, characterized in that: The calculating the propagation process of the cardiac electrical signal in the trunk includes: using a finite element method or a boundary element method to calculate the propagation process of the cardiac electrical signal in the trunk.

8. A device for solving ECG positive problems based on a cross-scale heart model, characterized in that ,include: A cross-scale heart-torso model generation module is used to obtain anatomical data based on medical images, segment the medical images, perform three-dimensional reconstruction based on the segmentation results and the anatomical data, and generate a cross-scale heart-torso model; A heart geometry model export module, used for exporting a heart geometry model based on the cross-scale heart-torso model; A subcellular simulation module, used for performing geometric optimization and meshing on the cardiac geometric structure model, generating a voxel matrix, and constructing an ion channel kinetic equation based on the voxel matrix to realize subcellular simulation; The body surface potential distribution acquisition module is used to map the subcellular level simulation results to the cross-scale heart-trunk model, calculate the propagation process of cardiac electrical signals in the trunk, and obtain the body surface potential distribution.

9. A memory, characterized in that: A plurality of instructions are stored, and the instructions are used to implement the method for solving the ECG positive problem based on a cross-scale heart model as described in any one of claims 1-7.

10. An electronic device, characterized in that: It includes a processor and a memory connected to the processor, the memory stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the method for solving ECG positive problems based on a cross-scale heart model as described in any one of claims 1-7.

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