Three-dimensional electrocardiogram imaging method and device based on dynamic atrial model and medium

Abstract

The invention discloses a three-dimensional electrocardiogram imaging method and device based on a dynamic atrium model and a medium, and relates to the technical field of electrocardiogram imaging, and the method comprises the steps that atrium multi-temporal image data and physiological state related data are collected, and the physiological state related data at least comprise respiratory signals and body position change data; based on the atrium multi-temporal image data, constructing an initial dynamic atrium model in combination with an adaptive grid optimization technology; performing form and position optimization on the initial dynamic atrium model through a self-adjusting geometric optimization algorithm by using the physiological state related data; generating an electrical activity transfer matrix based on the optimized dynamic atrial model, wherein the electrical activity transfer matrix is used for describing transfer characteristics of atrial electrical activity; based on the electrical activity transfer matrix and the body surface potential signal, three-dimensional electrocardiogram imaging is achieved through electrocardio imaging inverse problem solving. According to the invention, the solution precision of the ECGI inverse problem can be improved.

Description

Technical Field

[0001] This application relates to the field of electrocardiogram imaging technology, and in particular to a three-dimensional electrocardiogram imaging method, device and medium based on a dynamic atrial model. Background Technology

[0002] Electrocardiographic imaging (ECGI), as a non-invasive and precise method for detecting cardiac electrical activity, plays a vital role in the clinical diagnosis and treatment of cardiovascular diseases such as arrhythmias and atrial fibrillation. Its core principle is to reconstruct the electrical activity state of the heart surface by acquiring the body's surface potential signals and solving an inverse problem, providing clinicians with a direct assessment of cardiac electrophysiological function. In the ECGI inverse problem solving process, the accuracy of the atrial model's geometry and the rationality of its transfer matrix directly determine the accuracy of the electrical activity reconstruction. Therefore, constructing a model that accurately reflects the physiological state of the atria is a key step in enhancing the clinical application value of ECGI technology. With the development of medical imaging and computational simulation technologies, ECGI has gradually become a research hotspot in the field of cardiovascular disease diagnosis, and the demand for high-precision atrial models is becoming increasingly urgent.

[0003] In existing technologies, the construction of atrial models mainly relies on static imaging data such as cardiac computed tomography (CT) scans or magnetic resonance imaging (MRI). Image reconstruction algorithms are used to obtain the geometric morphology of the atria at specific time phases (such as end-systole or end-diastole), and then an electrical activity transfer matrix is ​​constructed based on this static morphology. To optimize model accuracy, some solutions employ traditional optimization methods such as geometric correction to fine-tune the spatial position of the static model, reducing deviations between the model and actual human anatomical structures. However, these technical solutions all focus on static geometry and do not fully consider the dynamic changes in human physiological processes.

[0004] The core technical problem with existing technologies lies in the fact that the atria, as a crucial component of the heart, undergo regular contraction and relaxation during the cardiac cycle, and their position is also affected by physiological factors such as respiratory rhythm and changes in body position. Existing static atrial models cannot dynamically simulate these geometric and positional changes during these physiological processes, resulting in a transfer matrix that fails to accurately describe the transmission characteristics of electrical activity under different physiological states. Even if traditional geometric correction methods can optimize the positional deviation of the static model, they cannot fundamentally solve the mismatch between the model and the actual atrial morphology caused by dynamic changes. Ultimately, this leads to insufficient accuracy in solving the ECGI inverse problem, and the reconstructed cardiac surface electrical activity deviates from the actual physiological state, failing to meet the stringent accuracy requirements of clinical diagnosis. Summary of the Invention

[0005] In view of this, this application provides a three-dimensional electrocardiogram imaging method, device and medium based on a dynamic atrial model, which can improve the solution accuracy of the ECGI inverse problem.

[0006] According to a first aspect of this application, a three-dimensional electrocardiogram imaging method based on a dynamic atrial model is provided, comprising: Collect multi-temporal imaging data of the atrium and physiological state-related data, wherein the physiological state-related data includes at least respiratory signals and body position change data; Based on the aforementioned multi-temporal atrial imaging data, an initial dynamic atrial model was constructed using adaptive grid optimization technology. Using the physiological state-related data, the initial dynamic atrial model is optimized in terms of morphology and position through a self-regulating geometric optimization algorithm; An electrical activity transfer matrix is ​​generated based on the optimized dynamic atrial model, which is used to describe the transfer characteristics of atrial electrical activity. Based on the electrical activity transfer matrix and the body surface potential signal, three-dimensional electrocardiogram imaging is achieved by solving the inverse problem of electrocardiogram imaging.

[0007] According to a second aspect of this application, a three-dimensional electrocardiogram imaging device based on a dynamic atrial model is provided, comprising: The acquisition module is used to acquire multi-temporal imaging data of the atrium and physiological state-related data, wherein the physiological state-related data includes at least respiratory signals and body position change data; The construction module is used to construct an initial dynamic atrial model based on the multi-temporal atrial imaging data and combined with adaptive grid optimization technology; The optimization module is used to optimize the morphology and position of the initial dynamic atrial model using the physiological state-related data and a self-adjusting geometric optimization algorithm. A generation module is used to generate an electrical activity transfer matrix based on an optimized dynamic atrial model, wherein the electrical activity transfer matrix is ​​used to describe the transfer characteristics of atrial electrical activity. The imaging module is used to achieve three-dimensional electrocardiogram imaging by solving the inverse problem of electrocardiogram imaging based on the electrical activity transfer matrix and the body surface potential signal.

[0008] According to a third aspect of this application, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described three-dimensional electrocardiogram imaging method based on a dynamic atrial model.

[0009] According to a fourth aspect of this application, an electronic device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described three-dimensional electrocardiogram imaging method based on a dynamic atrial model.

[0010] By employing the above technical solutions, this application provides a three-dimensional electrocardiogram (ECG) imaging method, device, and medium based on a dynamic atrial model. This method collects multi-temporal atrial imaging data and physiological state-related data such as respiratory signals and body position changes. It then constructs an initial dynamic atrial model reflecting morphological changes within the cardiac cycle using adaptive mesh optimization technology. A self-adjusting geometric optimization algorithm is then used to precisely optimize the model's morphology and position to adapt to physiological states, thereby generating an electrical activity transfer matrix that accurately describes the characteristics of atrial electrical activity transmission. Finally, based on this matrix and body surface potential signals, three-dimensional ECG imaging is achieved by solving the inverse problem of ECG imaging. This effectively addresses the shortcomings of traditional static atrial models, which cannot adapt to dynamic atrial changes and ignore the influence of physiological factors. It significantly improves the accuracy of electrical activity reconstruction, the model's adaptability to physiological changes, and the precision and reliability of three-dimensional ECG imaging. Furthermore, the assessment of the continuity of the electrical activity envelope ensures the physiological consistency of the reconstruction results, providing more precise technical support for the clinical diagnosis of cardiovascular diseases.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A schematic flowchart of a three-dimensional electrocardiogram imaging method based on a dynamic atrial model provided in an embodiment of this application is shown. Figure 2 A flowchart illustrating a three-dimensional electrocardiogram imaging method based on a dynamic atrial model, according to another embodiment of this application, is shown. Figure 3 This illustration shows a schematic diagram of an atrial model at different scaling scales provided in an embodiment of this application. Figure 4 This illustration shows a schematic diagram of the relative positions of the atria after rotation around different coordinate axes, according to an embodiment of this application. Figure 5 This illustration shows a schematic diagram of the relative position changes of an atrial model under different displacement scales, as provided in an embodiment of this application. Figure 6 This illustration shows a schematic diagram of a three-dimensional electrocardiogram imaging device based on a dynamic atrial model, provided in an embodiment of this application. Detailed Implementation

[0013] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0014] The core technical problem with existing technologies lies in the fact that the atria, as a crucial component of the heart, undergo regular contraction and relaxation during the cardiac cycle, and their position is also affected by physiological factors such as respiratory rhythm and changes in body position. Existing static atrial models cannot dynamically simulate these geometric and positional changes during these physiological processes, resulting in a transfer matrix that fails to accurately describe the transmission characteristics of electrical activity under different physiological states. Even if traditional geometric correction methods can optimize the positional deviation of the static model, they cannot fundamentally solve the mismatch between the model and the actual atrial morphology caused by dynamic changes. Ultimately, this leads to insufficient accuracy in solving the ECGI inverse problem, and the reconstructed cardiac surface electrical activity deviates from the actual physiological state, failing to meet the stringent accuracy requirements of clinical diagnosis.

[0015] To address the aforementioned technical problems, embodiments of the present invention provide a three-dimensional electrocardiogram imaging method based on a dynamic atrial model, such as... Figure 1 As shown, the method includes: Step 110: Collect multi-temporal imaging data of the atrium and physiological state-related data. Physiological state-related data shall include at least respiratory signals and body position change data.

[0016] Among them, atrial multiphase imaging data refers to the collection of image data obtained by CT or MRI scans during the atrial cardiac cycle by selecting multiple key phases (such as end-systole, end-diastole, etc.) and reflecting the geometric morphology of the atria at different motion stages; physiological state-related data refers to signal data that are related to human physiological activities and affect the morphology and position of the atria, and is mainly used to adapt to the dynamic physiological changes of the atria; respiratory signals are physiological signals that reflect the periodic changes of human respiration, collected by respiratory monitoring devices (such as chest straps, airflow sensors); and body position change data are physiological signals that record changes in human body position, collected by inertial measurement units or accelerometers.

[0017] During the data acquisition phase, several key phases within the atrial cardiac cycle, such as end-systole and end-diastole, can be identified. CT or MRI scans are then performed on these phases to obtain multi-phase atrial imaging data. Simultaneously, respiratory signals and physiological state-related data, such as changes in body position, are collected using surface sensors such as respiratory monitoring devices and inertial measurement units. Finally, all acquired imaging data and physiological signals undergo unified preprocessing to remove noise interference and ensure spatial consistency and signal purity, providing comprehensive and high-quality basic data support for the subsequent construction of the initial dynamic atrial model.

[0018] By simultaneously acquiring multi-temporal atrial imaging data and physiological state-related data such as respiratory signals and body position changes, it is possible to dynamically capture the geometric morphological changes of the atria during the cardiac cycle and comprehensively cover key physiological factors affecting atrial position. This breaks through the limitations of traditional single static data acquisition, and the acquired data can fully reflect the dynamic physiological characteristics of the atria. This lays a solid foundation for the subsequent construction of a dynamic atrial model that fits the actual physiological state, effectively improving the accuracy of subsequent model construction and optimization, and thus providing reliable data support for solving the inverse problem of electrocardiogram imaging.

[0019] Step 120: Based on multi-temporal atrial imaging data, construct an initial dynamic atrial model using adaptive grid optimization technology.

[0020] Among them, the adaptive mesh optimization technology is a technique that dynamically adjusts the mesh distribution according to the geometric characteristics of the atrium. The core is to generate high-density meshes in key areas such as the atrial boundary and areas with large curvature changes, and generate low-density meshes in flat areas. The mesh resolution is further optimized through geometric shape and current density feedback to balance model accuracy and computational efficiency. The initial dynamic atrial model is a basic model that can simulate the morphological changes of the atrium during the cardiac cycle, such as contraction and relaxation, based on multi-temporal image data, after registration, mesh optimization and multi-stage three-dimensional reconstruction.

[0021] In this embodiment of the present disclosure, the acquired multi-temporal atrial image data can first be preprocessed and multimodal registered to ensure spatial consistency of images from different temporal phases. Then, based on the registered images, the geometric characteristics such as the boundary shape and curvature of the atrium in each key temporal phase are analyzed. Subsequently, an adaptive mesh generation algorithm is used to generate high-density and low-density meshes in the key and flat regions of the atrium, respectively, in combination with the geometric characteristics. The mesh resolution is adjusted by feedback from the geometric shape and current density distribution. Then, the three-dimensional reconstruction of the atrium in a single temporal phase is completed based on the optimized mesh distribution and the image data of each temporal phase. Finally, the reconstruction results of all key temporal phases are integrated to form an initial dynamic atrial model that can dynamically change with the cardiac cycle.

[0022] By combining multi-temporal atrial imaging data with adaptive mesh optimization technology, we can accurately capture the dynamic geometric changes of the atria during the cardiac cycle, breaking the limitations of traditional static imaging data. Furthermore, through adaptive adjustment and optimization of mesh density, we can ensure the fidelity of the detailed reproduction of the complex morphology of the atria while taking into account computational efficiency. The constructed initial dynamic atrial model can realistically reflect the dynamic motion characteristics of the atria, providing a high-precision foundation for subsequent morphological and positional optimization of the model and the construction of the electrical activity transfer matrix. This will improve the accuracy and reliability of solving the inverse problem of electrocardiogram imaging.

[0023] Step 130: Using physiological state-related data, optimize the morphology and position of the initial dynamic atrial model through a self-regulating geometric optimization algorithm.

[0024] Among them, the self-adjusting geometric optimization algorithm is an optimization algorithm that can adaptively adjust the shape and position of the atrial model. Its core features include performing scaling, rotation and displacement adjustments in a preset order, introducing kinematic constraints and smooth transition processing, and iteratively optimizing by combining electrical activity feedback information until the model adapts to the physiological state. Shape and position optimization refers to adjusting the geometric shape (such as size and angle) and spatial position of the initial dynamic atrial model to adapt it to breathing, body position changes and atrial movement, and ensuring that the model is consistent with the actual physiological state of the human body.

[0025] In this embodiment of the present disclosure, a dynamic atrial position change model can be established based on the collected respiratory signals and body position change data. This model is used as an adjustment reference, and the geometry and spatial position of the initial dynamic atrial model are gradually adjusted according to a preset order of scaling, rotation, and displacement. During the adjustment process, kinematic constraints are introduced to avoid model changes exceeding the physiological range. A smooth transition algorithm is used to ensure the continuity of position updates. At the same time, the correlation coefficient, relative error, and main frequency difference between the reconstructed body surface potential and the actual measured value and the actual measured value are calculated to obtain electrical activity feedback information. The adjustment effect is judged based on the feedback. If the current strategy cannot improve the continuity of the electrical activity envelope, the next strategy is switched to iteratively optimize the model parameters until the correlation coefficient reaches a maximum value, the relative error reaches a minimum value, and the electrical activity envelope meets the continuity requirements, thus completing the precise optimization of the model shape and position.

[0026] The dynamic equations corresponding to the kinematic constraints are as follows:

[0027] This dynamic equation is used to quantify dynamic parameters such as atrial displacement and mechanical stress. The calculation results directly serve to adjust the geometry and spatial position of the initial dynamic atrial model. In the equation, u represents the atrial displacement. Let f(u) be the atrial tissue density, f(u) be the mechanical stress, and b(u) be the external load.

[0028] By combining physiological state-related data with a self-regulating geometric optimization algorithm, the morphology and position of the initial dynamic atrial model can be adaptively optimized. This effectively adapts to physiological factors such as respiration, body position changes, and atrial movement, overcoming the limitation of traditional models that ignore dynamic physiological changes. Furthermore, through kinematic constraints, smooth transition processing, and iterative feedback of electrical activity, the physiological rationality and accuracy of the model adjustment are ensured. The optimized model can truly reflect the dynamic physiological state of the atrium, providing reliable model support for the subsequent construction of the electrical activity transfer matrix and the high-precision solution of the inverse problem of electrocardiogram imaging. This significantly improves the practicality and imaging accuracy of the overall technical solution.

[0029] Step 140: Generate an electrical activity transfer matrix based on the optimized dynamic atrial model. The electrical activity transfer matrix is ​​used to describe the transfer characteristics of atrial electrical activity.

[0030] Among them, the electrical activity transfer matrix is ​​the core matrix describing the transmission law of atrial electrical activity from the epicardium to the body surface. It is constructed based on the geometric shape and electrophysiological parameters of the dynamic atrial model and can quantify the mapping relationship between epicardial potential and body surface potential. The transmission characteristics of atrial electrical activity refer to the spatiotemporal distribution law, signal attenuation characteristics and correlation law with tissue electrophysiological properties during the conduction of atrial electrical activity (such as epicardial potential) in atrial tissue and then through trunk tissue to the body surface.

[0031] In this embodiment of the disclosure, an optimized dynamic atrial model can be used as a basis. First, the model is refined into an electrophysiological model. A cardiac cell model is used to describe the electrical activity of atrial cells and derive the current density at each grid point. Key electrophysiological parameters such as tissue conductivity are obtained by mapping the tissue electrophysiological characteristics. Then, considering the temporal dynamic changes and spatial distribution characteristics of electrical activity, a time-space coupled optimization model is established. The electrical activity transmission process in different time phases is simulated by coordinating the time-space step size. Subsequently, based on this coupled model, the geometric data of the model and the mapped electrophysiological parameters are integrated to initially construct an electrical activity transmission matrix. Finally, a multi-objective optimization objective is set, including electrical activity transmission error, geometric consistency, and temporal stability. The initial matrix is ​​iteratively optimized by a global optimization algorithm, and the spatial, temporal, and geometric errors are comprehensively reduced by combining a global error evaluation model to generate an electrical activity transmission matrix that can accurately describe the electrical activity transmission characteristics of the atria.

[0032] By generating an electrical activity transfer matrix based on an optimized dynamic atrial model, we can fully utilize the dynamic model's geometric shape and location features that conform to physiological reality, and incorporate refined electrophysiological parameters and spatiotemporal coupling optimization logic. This allows the transfer matrix to accurately capture the dynamic transfer patterns of atrial electrical activity, breaking the limitation of traditional static model-constructed transfer matrices that cannot adapt to physiological changes. This significantly improves the accuracy and physiological relevance of the transfer matrix, providing core support for subsequent solutions to the inverse problem of electrocardiogram imaging based on body surface potential signals and accurate reconstruction of epicardial electrical activity, thereby ensuring the accuracy and reliability of three-dimensional electrocardiogram imaging.

[0033] Step 150: Based on the electrical activity transfer matrix and the body surface potential signal, three-dimensional electrocardiogram imaging is achieved by solving the inverse problem of electrocardiogram imaging.

[0034] Among them, body surface potential signal refers to the potential signal collected through human body surface electrodes. After filtering and other preprocessing to remove noise and interference, it can provide high-quality input data for solving the inverse problem of electrocardiogram imaging. The core of solving the inverse problem of electrocardiogram imaging is the process of inferring the electrical activity of the heart surface (epidermis) from the preprocessed body surface potential signal. By introducing regularization constraints and global optimization algorithms, the problem of non-unique solution caused by underdetermined transfer matrix is ​​solved, and the accurate reconstruction of epicardial potential is achieved. Three-dimensional electrocardiogram imaging refers to the combination of the spatiotemporal characteristics of reconstructed epicardial electrical activity with the geometric morphology of dynamic atrial model. The imaging results generated by three-dimensional visualization technology can intuitively present the origin, conduction path and abnormal areas of atrial electrical activity.

[0035] In this embodiment of the present disclosure, the human body surface potential signal can be collected first and filtered preprocessed to remove redundant interference. Then, the electrical activity transfer matrix constructed based on the optimized dynamic atrial model is called, and the preprocessed body surface potential signal is input into the matrix. By establishing an optimization objective function with regularization constraints, the inverse problem of electrocardiogram imaging is solved using a global optimization algorithm to reconstruct the epicardial potential. Then, the electrical activity envelope of the reconstructed epicardial potential is extracted and its continuity and smoothness are evaluated. If the envelope characteristics meet the preset criteria, the three-dimensional electrocardiogram imaging data is generated by combining the geometry of the dynamic atrial model with three-dimensional visualization technology. If not, the data is fed back to the model optimization stage to adjust the parameters and solve the problem again.

[0036] By combining a precise electrical activity transfer matrix with high-quality body surface potential signals, and using the inverse problem of electrocardiogram imaging to achieve three-dimensional electrocardiogram imaging, this method can fully utilize the transfer matrix to accurately depict the spatiotemporal transmission law of electrical activity, and ensure the accuracy of epicardial potential reconstruction through regularization constraints and optimization algorithms. This effectively solves the problem of insufficient imaging accuracy under traditional static models. The generated three-dimensional electrocardiogram can intuitively and realistically reflect the dynamic characteristics of atrial electrical activity, providing more reliable technical support for the clinical diagnosis of cardiovascular diseases, while improving the physiological consistency and clinical applicability of imaging results.

[0037] In summary, the three-dimensional electrocardiogram (ECG) imaging method based on a dynamic atrial model provided by this invention collects multi-temporal atrial image data and physiological state-related data such as respiratory signals and body position changes. It then constructs an initial dynamic atrial model that reflects morphological changes during the cardiac cycle using adaptive mesh optimization technology. A self-adjusting geometric optimization algorithm is then used to precisely optimize the model's morphology and position to adapt to physiological states, thereby generating an electrical activity transfer matrix that accurately describes the characteristics of atrial electrical activity transmission. Finally, based on this matrix and body surface potential signals, three-dimensional ECG imaging is achieved by solving the inverse problem of ECG imaging. This method effectively addresses the shortcomings of traditional static atrial models, which cannot adapt to dynamic atrial changes and ignore the influence of physiological factors. It significantly improves the accuracy of electrical activity reconstruction, the model's adaptability to physiological changes, and the precision and reliability of three-dimensional ECG imaging. Furthermore, the assessment of the continuity of the electrical activity envelope ensures the physiological consistency of the reconstruction results, providing more precise technical support for the clinical diagnosis of cardiovascular diseases.

[0038] Furthermore, as a refinement and extension of the specific implementation methods of the above embodiments, and to fully illustrate the implementation methods of this embodiment, this embodiment also provides another three-dimensional electrocardiogram imaging method based on a dynamic atrial model, such as... Figure 2 As shown, the method includes: Step 210: Collect multi-temporal imaging data of the atrium and physiological state-related data, including at least respiratory signals and body position change data.

[0039] For the specific implementation process of the embodiments disclosed herein, please refer to the relevant description in step 110 of the embodiments, which will not be repeated here.

[0040] Accordingly, the steps of the embodiment may include: determining multiple key phases within the atrial cardiac cycle, the key phases covering at least end-systole and end-diastole; performing CT or MRI scans on the multiple key phases respectively to obtain corresponding multi-phase atrial imaging data; and collecting physiological state-related data through surface sensors.

[0041] Step 220: Based on multi-temporal atrial imaging data, construct an initial dynamic atrial model using adaptive grid optimization technology.

[0042] For embodiments of this disclosure, step 220 may include the following steps: Step 220-1: Register the preprocessed multi-temporal atrial image data to ensure spatial consistency of images at different temporal phases.

[0043] Among them, preprocessed atrial multi-temporal image data refers to the original image data obtained by CT or MRI scans of multiple key temporal phases (such as end-systole and end-diastole) within the atrial cardiac cycle, which, after preprocessing operations such as noise removal, can reflect the geometric morphology of different atrial motion stages; image registration is a technique that adjusts the spatial position of images of different temporal phases through specific algorithms. Its core is to align images of multiple temporal phases in the spatial coordinate system and eliminate spatial deviations caused by shooting sequence, equipment errors, etc.

[0044] In this embodiment of the disclosure, a multimodal registration algorithm based on maximizing mutual information can be used for atrial multi-temporal image data that has undergone noise removal. The algorithm takes the mutual information measurement of image grayscale distribution as the core basis, calculates the grayscale correlation between images of different temporal phases, and dynamically adjusts the spatial position, scale, and angle parameters of each temporal phase image to eliminate spatial misalignment caused by atrial motion, shooting differences, etc. This ensures that atrial images of all key temporal phases are accurately aligned in the same spatial coordinate system, and ultimately ensures that the atrial anatomical structure, boundary contours, etc. in images of different temporal phases maintain spatial consistency, providing a unified benchmark for subsequent geometric characteristic analysis and three-dimensional reconstruction.

[0045] Specifically, when registering preprocessed multi-temporal atrial image data, a multimodal registration algorithm based on maximizing mutual information can be used, through the formula... Calculate the mutual information metric S(A,B) between image A and image B, where, , (These are the grayscale distributions of the two images, respectively). Based on the principle of maximizing mutual information, the images from different time phases are precisely aligned to ensure the spatial consistency of the model and provide a unified benchmark for subsequent 3D reconstruction.

[0046] By registering the preprocessed multi-temporal atrial images, the spatial deviation caused by the shooting sequence, equipment errors, and atrial motion in different temporal images can be effectively solved. This ensures the accurate correspondence of atrial anatomical structures in each temporal image and provides a high-quality unified spatial benchmark for subsequent atrial geometric characteristic analysis, adaptive mesh generation, and multi-key temporal 3D reconstruction. It avoids the impact of spatial misalignment on the model's morphological fidelity and significantly improves the accuracy and reliability of the initial dynamic atrial model construction.

[0047] Step 220-2: Based on the registered multi-temporal atrial image data, analyze the atrial geometric characteristics of each key temporal phase.

[0048] Among them, the key phase refers to the representative motion phase in the atrial cardiac cycle, which can reflect the core changes in atrial morphology, including at least end-systole and end-diastole; atrial geometric characteristics refer to the spatial structural features of the atrium in a specific phase, including quantifiable geometric indicators such as boundary shape, curvature distribution, anatomical structure outline (such as the morphology of the pulmonary vein connection), and size parameters (such as superior-inferior diameter and left-right diameter).

[0049] In this embodiment of the disclosure, based on the registered and spatially consistent multi-temporal atrial image data, medical image analysis tools are used to refine the images of each key temporal phase. By extracting the atrial boundary contour, calculating the curvature values ​​of different regions, and measuring core size parameters, the geometric structural features of the atrium in the end-systolic and end-diastolic stages are systematically analyzed. At the same time, the geometric change patterns of each temporal phase are correlated to form an analysis result that comprehensively reflects the dynamic geometric attributes of the atrium, providing data support for subsequent mesh generation and optimization.

[0050] By analyzing the key temporal geometric characteristics of registered multi-temporal atrial images, we can accurately capture the morphological changes of the atrium during the cardiac cycle and obtain core geometric parameters such as boundaries, curvature, and size. This provides a precise basis for subsequent adaptive mesh generation (such as high-density mesh allocation in key areas), avoiding unreasonable mesh distribution due to missing geometric information. At the same time, it ensures that the subsequently constructed dynamic atrial model can realistically reproduce the anatomical structure and dynamic motion characteristics of the atrium, laying a solid foundation for improving the accuracy of solving the inverse problem of electrocardiogram imaging.

[0051] Step 220-3: Based on the geometric characteristics of the atrium, an adaptive mesh generation algorithm is used to generate high-density meshes in key areas of the atrium and low-density meshes in flat areas.

[0052] Among them, the adaptive mesh generation algorithm is an algorithm that can dynamically adjust the mesh density according to the geometric characteristics of the target object. Its core is to increase the mesh resolution in complex areas and decrease the mesh resolution in simple areas to balance model accuracy and computational efficiency. The critical atrial region refers to the region with complex atrial anatomy and dramatic geometric changes, including the atrial boundary, the part with large curvature changes, and the connection between the pulmonary vein and the atrium, which are areas that need to accurately reproduce details. The high-density mesh is a mesh structure with dense mesh point distribution and high spatial resolution, which can accurately capture the complex shape and subtle changes of the target region. The low-density mesh is a mesh structure with sparse mesh point distribution and low spatial resolution, which is suitable for regions with gentle shape and simple changes, and can reduce redundant calculations.

[0053] In this embodiment of the present disclosure, based on the previously analyzed geometric characteristics such as the shape of the atrial boundary, curvature distribution, and anatomical complexity, an adaptive mesh generation algorithm that dynamically adapts to geometric features can be used to generate high-density meshes with dense mesh points and high resolution for key areas such as the atrial boundary, pulmonary vein junction, and abrupt curvature changes, accurately reproducing complex morphological details. Simultaneously, in areas with flat atrial surfaces and gradual geometric feature changes, low-density meshes with sparse mesh points and low resolution are generated, achieving accurate characterization of the atrial morphology and efficient utilization of computational resources. Furthermore, the mesh resolution can be adjusted and optimized through feedback from geometric shape and current density distribution to ensure that the mesh accuracy meets requirements in the calculation of electrical activity transmission. Mesh optimization can employ an adaptive mesh adjustment formula based on curvature and electrical activity transmission.

[0054] In the formula, h(x) is the grid size, C is a constant, and r(x) is the position vector of the grid point. r(x) is the geometric curvature.

[0055] By combining atrial geometry with an adaptive mesh generation algorithm, high-density meshes are used in critical areas to ensure the detailed reproduction of complex anatomical structures and dynamic changes, while low-density meshes are used in flat areas to effectively control the computational load. This not only avoids the loss of key details or waste of computational resources caused by uniform meshes, but also provides a high-precision and efficient mesh foundation for subsequent electrophysiological parameter mapping, construction of electrical activity transfer matrices, and solving inverse problems of electrocardiogram imaging, significantly improving the computational accuracy, stability, and practicality of the overall technical solution.

[0056] Step 220-4: Based on the grid distribution and multi-temporal image data of the atrium, perform three-dimensional reconstruction of the atrium for each key temporal phase.

[0057] Among them, grid distribution refers to the non-uniform grid structure obtained by an adaptive grid generation algorithm based on the geometric characteristics of the atrium. High-density grids are used in key areas such as the atrial boundary and pulmonary vein junction, while low-density grids are used in flat areas. Three-dimensional reconstruction refers to the process of using medical image processing technology, combined with the spatial division of grid distribution and the geometric information of multi-temporal images, to construct a three-dimensional digital model that can accurately restore the anatomical structure of the atrium.

[0058] In this embodiment of the disclosure, the adaptively optimized grid distribution can be used as a spatial framework. Combined with the registered multi-temporal atrial image data, the atrial boundaries and anatomical structure contours in each key temporal image are accurately matched with the grid nodes through a three-dimensional reconstruction algorithm. By utilizing the high-density area of ​​the grid to capture complex morphologies in detail and the efficient computational advantage of the low-density area, the three-dimensional geometric morphology of the atrium in each temporal phase is restored. This ensures that the reconstruction results not only conform to the anatomical authenticity of the image data, but also provide a stable spatial carrier for subsequent electrophysiological parameter mapping through the grid structure.

[0059] By combining optimized grid distribution with multi-temporal atrial imaging data for 3D reconstruction, high-density grids can ensure the detailed reproduction of complex atrial anatomical structures (such as the pulmonary vein junction), while low-density grids can balance computational efficiency. At the same time, based on multi-temporal images, the morphology of the atria at different motion stages can be accurately depicted. The generated 3D models of each key temporal phase can provide a high-quality foundation for the subsequent integration of dynamic atrial models, ensuring that the dynamic model can truly reflect the morphological changes of the atria during the cardiac cycle. This lays a core foundation for improving the accuracy of constructing the electrical activity transfer matrix and solving the inverse problem of electrocardiogram imaging.

[0060] Step 220-5: Integrate the three-dimensional reconstruction results of each key time phase to form an initial dynamic atrial model that can change with the cardiac cycle.

[0061] In this embodiment of the disclosure, completed three-dimensional reconstruction models of the atria at each key time phase (such as end-systole and end-diastole) can be collected. These models are then systematically associated based on the temporal logic of the cardiac cycle. An interpolation algorithm is used to achieve a smooth transition of the model morphology at different time phases, eliminating morphological abrupt changes between time phases. At the same time, it is ensured that the models maintain a consistent anatomical reference in the spatial coordinate system. Finally, these models are integrated to form an initial dynamic atrial model that can completely reproduce the dynamic changes of the atria during contraction and relaxation within the cardiac cycle.

[0062] By integrating the 3D reconstruction results of key time phases to form an initial dynamic atrial model, we can overcome the limitations of traditional static models that can only reflect the atrial morphology at a single moment. This model accurately reproduces the dynamic motion characteristics of the atria during the cardiac cycle and provides a continuous and reliable basic framework for subsequent optimization of model morphology and position by combining physiological state data. At the same time, it ensures that the subsequent construction of the electrical activity transfer matrix can conform to the dynamic physiological laws of the atria, thereby improving the accuracy and physiological relevance of solving the inverse problem of electrocardiogram imaging from the source.

[0063] Step 230: Based on the collected respiratory signals and body position change data, establish a dynamic change model of atrial position.

[0064] In this embodiment of the present disclosure, human respiratory periodic signals and body position change data can be collected simultaneously by body surface sensors such as respiratory monitoring devices and inertial measurement units. The two types of data are preprocessed to remove noise interference. Then, based on the periodicity of the respiratory signals and the quantitative parameters of body position changes, a linear combination mathematical model that integrates the influence of the two is constructed. By setting weight coefficients, the strength of the effect of the two types of factors on the atrial position is quantified, and finally a dynamic change model that can map the correspondence between respiration, body position changes and atrial spatial position in real time is formed.

[0065] Specifically, a dynamic model of atrial position changes can be established based on respiratory and postural change data. This model takes into account the periodic changes in body position and respiration, simulating the impact of these changes on atrial position.

[0066] Assume that atrial displacement caused by changes in body position and respiration can be modeled using a linear combination:

[0067] Where P(t) represents the position of the atrium at time t. The initial position, and These are the influence vectors of body position changes and respiratory changes, respectively. and These are the weighting coefficients.

[0068] By collecting respiratory signals and body position change data and establishing a dynamic model of atrial position changes, the influence of two key physiological factors on atrial position can be accurately captured. This breaks through the limitation of traditional models that ignore dynamic physiological changes, enabling the model to adapt to fluctuations in human physiological state in real time. It can provide accurate reference for the subsequent optimization of the morphology and position of the initial dynamic atrial model, effectively improving the physiological fit of subsequent model optimization, and thus laying a high-precision foundation for the construction of the electrical activity transfer matrix and the solution of the inverse problem of electrocardiogram imaging.

[0069] Step 240: Using the dynamic change model as a reference, optimize the morphology and position of the initial dynamic atrial model in the order of scaling, rotation, and displacement, and obtain electrical activity feedback information. The optimization process includes kinematic constraints, smooth transition processing, and electrical activity feedback iteration.

[0070] Among these, scaling strategy refers to the optimization method of adjusting the initial dynamic atrial model volume according to the physiological characteristics of atrial contraction and relaxation during the cardiac cycle, with the scaling range closely matching the physiological dimensions of the atria under normal and pathological conditions; rotation strategy refers to the optimization method of adjusting the angle of the initial dynamic atrial model around the spatial coordinate axes (x, y, z axes or multi-axis combinations) to simulate the angular displacement caused by atrial mechanical movement; displacement strategy refers to the optimization method of adjusting the position of the initial dynamic atrial model in the three-dimensional space of x, y, and z to adapt to the spatial displacement of the atria caused by respiration and body position changes; kinematic constraints refer to the physiologically reasonable restrictions set during the model optimization process, by limiting the maximum displacement of the model. Parameters such as displacement amplitude and rotation angle are used to prevent model changes from exceeding the physiological range; smooth transition processing refers to using interpolation and other algorithms to ensure that the morphological and positional changes of the model are continuous and without abrupt changes during scaling, rotation, and displacement adjustments, thus ensuring the integrity and coherence of the anatomical structure; electrical activity feedback iteration refers to the cyclical process of obtaining feedback information by calculating the correlation coefficient and relative error between the reconstructed body surface potential and the actual measured body surface potential, as well as the difference in the dominant frequency between the reconstructed epicardial potential and the mapped potential, and driving the model to adjust repeatedly; electrical activity feedback information refers to the quantitative data used to evaluate the model optimization effect, the core of which includes indicators such as the correlation coefficient, relative error, and dominant frequency difference between the reconstructed potential and the actual potential.

[0071] In this embodiment of the present disclosure, a dynamic change model constructed based on respiratory signals and body position change data can be used as a physiological reference. First, the volume of the initial dynamic atrial model is adjusted by scaling strategy to adapt to the physiological characteristics of atrial contraction and relaxation. Then, the angle of the model around the spatial coordinate axis is adjusted by rotation strategy and the position of the model in three-dimensional space is adjusted by displacement strategy in sequence to simulate the mechanical movement of the atrium and the morphological and positional changes caused by physiological factors. Throughout the optimization process, kinematic constraints are used to limit the model changes to not exceed the physiologically reasonable range. Smooth transition processing is used to ensure the continuity of morphology and position between each adjustment link. At the same time, by calculating the correlation coefficient, relative error and main frequency difference between the reconstructed body surface potential and the actual measured value, and the reconstructed epicardial potential and the measured value, electrical activity feedback information is obtained to form an iterative cycle and continuously optimize the morphology and position of the model.

[0072] Accordingly, during the iterative optimization process, an optimization objective function can be constructed. (in and (where is the weighting coefficient, Error is the error between the reconstructed potential and the actual potential, and CC is the correlation coefficient between the two). Using this function as the optimization guide, the scaling, rotation, and displacement parameters of the atrial model are repeatedly adjusted through algorithms such as particle swarm optimization until the objective function value is optimal, thereby achieving a precise match between the model morphology and the law of electrical activity transmission.

[0073] Specifically, during the feedback iteration of electrical activity, a feedback mechanism allows the transfer matrix to adaptively adjust after each change in atrial position, ensuring the accuracy of electrical activity transmission. This is something traditional methods cannot achieve, enabling a more precise simulation of the real atrial state. Specifically, after each change in atrial position, a new electrical activity transfer matrix is ​​calculated using the ECGI forward problem. This matrix is ​​then compared with the actual BSP (body surface potential map) signal measured on the body surface to calculate the error. The error calculation formula is:

[0074] In the formula, This is the actual body surface potential signal. This is the reconstructed body surface potential signal.

[0075] Then, optimization algorithms (such as particle swarm optimization or genetic algorithms) can be used to adjust the atrium's geometry (scaling, rotation, and displacement) based on feedback from the electrical activity transfer matrix, and update the model's electrical activity transfer matrix. The formulaic feature description corresponding to the update rule can be as follows:

[0076] In the formula, The adjustment amount is calculated using an optimized algorithm.

[0077] Unlike traditional single-objective optimization, this application proposes a multi-dimensional optimization algorithm based on a physiological model, considering multiple physiological factors such as body position and respiration, and combining this with an optimization objective based on the electrical activity transfer matrix to optimize the atrial model from multiple dimensions. The defined multi-dimensional optimization objective may include electrical activity transfer error, adaptability to body position and respiration, and geometric rationality of the model. The specific objective function is as follows:

[0078] In the formula, is the weighting coefficient, Adaptation is the adaptation of body position and respiration, and Geometrical Consistency is the consistency of geometric shape.

[0079] When performing multi-dimensional objective optimization, optimization algorithms such as Particle Swarm Optimization (PSO) or Genetic Algorithm (GA) can be used to optimize electrical activity error, fitness, and geometric consistency separately. Each objective is evaluated through independent computation, and then combined with the global objective for comprehensive optimization.

[0080] The particle swarm update formula is:

[0081]

[0082] In the formula, For particle velocity, For the particle position, denoted as the individual's optimal position, g is the global optimal position, c1 and c2 are learning factors, and r1 and r2 are random numbers.

[0083] To avoid instability in the electrical activity transfer matrix caused by excessively drastic changes in atrial position, this application introduces kinematic constraints to ensure a smooth transition in atrial position and reduce the negative impact of over-adjustment on the optimization results. The smooth transition formula is as follows:

[0084] In the formula, λ is a smoothing factor used to control the smoothness of the transition.

[0085] By adding kinematic constraints, we ensure that the adjustment of the atrial position conforms to physiological norms and does not produce unreasonable morphological changes. The constraints are:

[0086] In the formula, The maximum allowable displacement range is used to ensure the smoothness of atrial movement.

[0087] In specific application scenarios, 10 different initial dynamic atrial models with atrial scaling ranges from 0.6 to 1.5 and a step length of 0.1 can be set. For example... Figure 3 As shown in the diagram, green represents the initial dynamic atrial model, and red represents atrial models at different scaling scales. 1.0 indicates no scaling of the atria, and the numbers below the models represent the corresponding scaling scales. For simulating the rotational motion of the atria within the trunk caused by their mechanical movement, rotations of the atrial model around the x-axis, y-axis, z-axis, xy-axis, xz-axis, yz-axis, xyz-axis, and yzx-axis can be designed. Rotation around the xy-axis represents rotation around the x-axis first, followed by rotation around the y-axis; rotation around the yzx-axis represents rotation around the y-axis first, then around the z-axis, and finally around the x-axis. The set rotation angles are ±10° and ±20°. Figure 4 It shows the relative positional relationship of the atria before and after rotation under different settings. Figure 4In the diagram, (a) and (b) represent rotations of 10° on the corresponding coordinate axis, (c) and (d) represent rotations of 20° on the corresponding coordinate axis, (e) and (f) represent rotations of -10° on the corresponding coordinate axis, and (g) and (h) represent rotations of -20° on the corresponding coordinate axis. For the problem of changes in the position of the atria in the trunk caused by atrial mechanical movement, respiration, and changes in body position, this application can conduct simulation experiments by setting displacements in the x, y, and z directions at 10 scales. Specifically, a step length of 0.1 times the span of the initial dynamic atrial model in the x, y, and z directions is set, moving the corresponding coordinate span in the x, y, and z directions by -0.5 to 0.5 times. Figure 5 As shown, the positional relationship between the atrial model and the initial dynamic atrial model under different displacements is illustrated. Green represents the initial dynamic atrial model, and red represents the atrial model under different displacements. Figure 5 In the figure, (a) and (b) represent displacements on the x-axis, (c) and (d) represent displacements on the y-axis, and (e) and (f) represent displacements on the z-axis. The positive and negative signs indicate the movement in the positive and negative directions on the corresponding coordinate axes.

[0088] By performing optimization in the order of scaling, rotation, and translation, and incorporating kinematic constraints, smooth transition processing, and iterative feedback of electrical activity, we can ensure that the adjustment of the initial dynamic atrial model conforms to the physiological laws such as atrial contraction and relaxation, respiratory and postural changes, and avoid non-physiological morphological abrupt changes or positional deviations. At the same time, we can accurately drive the iterative optimization of the model based on the feedback information of electrical activity, so that the model is highly adapted to the actual physiological state. This can significantly improve the physiological relevance and morphological accuracy of the model, and lay the core foundation for the subsequent construction of the electrical activity transfer matrix and the high-precision solution of the inverse problem of electrocardiogram imaging.

[0089] Step 250: Based on the electrical activity feedback information, determine the adjustment effect of the current strategy. If the current strategy cannot improve the continuity of the electrical activity envelope, switch to the next strategy and iteratively adjust the model parameters until the electrical activity feedback information meets the preset standard. Determine whether the initial dynamic atrial model is suitable for the current physiological state and obtain the optimized dynamic atrial model.

[0090] Among them, the preset standard refers to the pre-set conditions for model optimization, including the correlation coefficient in the electrical activity feedback information reaching a maximum value, the relative error reaching a minimum value, and the continuity of the electrical activity envelope meeting the smoothness threshold requirement; iterative adjustment of model parameters refers to the cyclical process of continuously correcting the model's adjustment parameters (such as scaling, rotation angle, displacement amplitude, etc.) based on the electrical activity feedback information after each strategy execution until the model adapts to the physiological state; the optimized dynamic atrial model refers to a dynamic model that, after strategy switching and iterative adjustment, has electrical activity feedback information that meets the preset standard, can accurately adapt to cardiac cycle, respiration, and body position changes, and whose morphology and position conform to the actual physiological state.

[0091] In the embodiments of this disclosure, during the optimization of the initial dynamic atrial model, the model parameters can be adjusted based on the currently executed scaling, rotation, or displacement strategy. Then, by calculating the correlation coefficient, relative error, and main frequency difference between the reconstructed surface potential and the actual measured value, as well as the reconstructed epicardial potential and the mapped EGM, electrical activity feedback information is obtained. At the same time, the continuity of the electrical activity envelope is evaluated. If the current strategy does not improve the continuity of the envelope (e.g., the mean absolute value of the second derivative does not decrease), the next strategy is switched in a preset order, the model parameters are adjusted, and the feedback evaluation process is repeated. This process is iterated until the electrical activity feedback information meets the preset standards of extremely high correlation coefficient, extremely low relative error, and satisfactory envelope continuity. It is then determined that the initial dynamic atrial model has adapted to the current physiological state, and the optimized dynamic atrial model is finally obtained.

[0092] By judging the effectiveness of the strategy based on the feedback information of electrical activity, dynamically switching and adjusting the strategy, and iteratively optimizing the model parameters, it is possible to ensure that the model adjustment always revolves around the actual physiological needs, avoiding the impact of ineffective strategies on optimization efficiency. Furthermore, through continuous feedback and iteration, the model's morphology and position can be accurately adapted to the dynamic physiological changes of the atrium, which can effectively improve the physiological relevance of the model and the accuracy of electrical activity reconstruction. This provides high-quality model support for the subsequent construction of the electrical activity transfer matrix and the solution of the inverse problem of electrocardiogram imaging, and can significantly reduce the deviation between the reconstructed electrical activity and the actual electrical activity.

[0093] Step 260: Generate the electrical activity transfer matrix based on the optimized dynamic atrial model.

[0094] For embodiments of this disclosure, step 260 may include the following steps: Step 260-1: Perform electrophysiological modeling on the optimized dynamic atrial model to obtain the electrophysiological parameters corresponding to each atrial grid point. The electrophysiological parameters include tissue conductivity, cell potential and current density.

[0095] Electrophysiological modeling refers to the process of constructing a mathematical model based on the physiological characteristics of the heart and the laws of cellular electrical activity to simulate the electrophysiological behavior of atrial tissue and quantify the generation, conduction, and distribution characteristics of electrical signals within the atrial tissue. Atrial grid points are discrete spatial nodes divided on the atrial model using an adaptive grid generation algorithm. They are distributed at high density in key areas (such as the pulmonary vein junction) and at low density in flat areas, serving as the basic units carrying electrophysiological parameters. Electrophysiological parameters are quantitative indicators related to atrial electrical activity obtained from electrophysiological modeling. Core parameters include tissue conductivity, cell potential, and current density, which are key parameters describing the generation and conduction characteristics of electrical signals. Tissue conductivity is a physical parameter characterizing the atrial tissue's ability to conduct electrical signals; it is related to tissue type and physiological state and is a core indicator describing the characteristics of electrical activity transmission. Cell potential refers to the potential difference generated by atrial cells during electrical activity, exhibiting periodic changes with the cardiac cycle and serving as the source signal for electrical activity. Current density refers to the conduction intensity of electrical signals per unit area, determined by changes in cell potential and tissue conductivity, reflecting the distribution of conduction intensity of electrical activity within the atrial tissue.

[0096] In this embodiment of the present disclosure, an optimized dynamic atrial model adapted to physiological states can be used as a basis. A cardiac cell model (such as the Luo-Rudy model) is used to simulate the electrical activity of atrial cells, and the temporal changes of cell potentials corresponding to each atrial grid point are calculated. Combining the physiological characteristics of atrial tissue, a mapping relationship is established between cell potentials, body surface potential signals, and tissue conductivity, and the tissue conductivity of each grid point is quantified. Based on the spatiotemporal variation characteristics of cell potentials and tissue conductivity parameters, the current density of each atrial grid point is derived through electromagnetic formulas. Finally, the electrophysiological modeling of the entire model is completed, and core electrophysiological parameters such as tissue conductivity, cell potentials, and current densities covering all grid points are obtained.

[0097] By performing refined electrophysiological modeling on the optimized dynamic atrial model, precise tissue conductivity, cell potential, and current density parameters are assigned to each atrial grid point. This approach fully leverages the advantages of the dynamic model's morphology and location in conforming to physiological reality, and achieves precise matching between electrophysiological characteristics and geometric morphology. It breaks through the limitations of generalization of electrophysiological parameters in traditional static modeling, significantly improving the accuracy and physiological relevance of the description of electrical activity transmission laws. This provides core data support for the subsequent construction of the electrical activity transmission matrix and the high-precision solution of the inverse problem of electrocardiogram imaging.

[0098] Step 260-2: Considering the temporal variation and spatial distribution characteristics of electrical activity, establish a time-space coupled optimization model.

[0099] Among them, the temporal variation of electrical activity refers to the dynamic evolution of atrial electrical activity over time, specifically manifested as the periodic fluctuations of electrophysiological parameters such as cell potential and current density during the cardiac cycle, covering the changes in electrical signals in different phases such as systole and diastole; the spatial distribution characteristics refer to the differences in the distribution of electrical activity within the spatial range of the atrial model, which are related to atrial anatomical structure, grid density, tissue conductivity, etc., and are reflected in the heterogeneous distribution of electrophysiological parameters in different regions (such as the pulmonary vein junction and the flat area of ​​the atrium); the time-space coupling optimization model is a mathematical model that integrates the temporal dynamic law of electrical activity and the spatial distribution characteristics. By considering the matching relationship between the time step and the spatial step in a coordinated manner, it achieves accurate simulation of the spatiotemporal coordinated changes of electrical activity and avoids modeling bias caused by the separation of spatiotemporal characteristics.

[0100] For the embodiments of this disclosure, the temporal variation law of atrial electrical activity in the optimized dynamic atrial model can be analyzed first to capture the fluctuation characteristics of electrophysiological parameters in different cardiac phases. At the same time, the spatial distribution differences of electrical activity can be clarified by combining atrial grid distribution, tissue conductivity heterogeneity, etc. On this basis, a mathematical model that can correlate spatiotemporal characteristics can be constructed. By setting an appropriate time step and spatial step, the dynamic evolution of electrical activity in the temporal domain can be combined with the distribution law of electrical signals in the space, and the spatiotemporal mapping relationship of electrophysiological parameters can be incorporated. Finally, a coupled optimization model that can synchronously adapt to the spatiotemporal changes of electrical activity can be established.

[0101] Specifically, optimization algorithms based on spatiotemporal separation (such as the spatiotemporal finite difference method) can be used to model the temporal dynamics of electrical activity:

[0102] In the formula, Let J be the electrical potential, J be the current density, I be the external input, and D be the diffusion coefficient. To improve the electrical conductivity of the tissue.

[0103] By establishing a time-space coupled optimization model, the limitations of separating spatiotemporal characteristics in traditional modeling can be effectively overcome. This model can accurately capture the dynamic fluctuations of electrical activity over time and fully adapt to its heterogeneous spatial distribution, enabling the model to more realistically reflect the spatiotemporal coordinated transmission law of atrial electrical activity and significantly improve the comprehensiveness and accuracy of electrophysiological modeling.

[0104] Step 260-3: Based on the coupled optimization model, and combining the model's geometric shape and electrophysiological parameters, a preliminary electroactivity transfer matrix is ​​constructed.

[0105] Among them, the model geometry refers to the spatial structural features of the optimized dynamic atrial model, including geometric attributes such as grid distribution (high density in key areas and low density in flat areas), anatomical contours (such as the morphology of the pulmonary vein junction), boundary shape, and curvature distribution; the electrical activity transfer matrix is ​​the core matrix that quantifies the mapping relationship between epicardial potential and body surface potential. It can describe the law of atrial electrical activity being transferred from the epicardium through trunk tissues to the body surface and is the basis for solving the inverse problem of electrocardiogram imaging.

[0106] In this embodiment of the disclosure, the time-space coupling optimization model of electrical activity can be used as the core framework. The geometric morphological features of the optimized dynamic atrial model (including grid distribution, anatomical contour and curvature changes, etc.) are fully combined. The electrophysiological parameters such as tissue conductivity, cell potential and current density corresponding to each atrial grid point are incorporated into the mapping relationship calculation. By quantifying the generation, conduction and transmission of electrical signals in the atrial tissue and through the trunk tissue to the body surface, a basic mapping matrix between epicardial potential and body surface potential is established, and the preliminary construction of the electrical activity transmission matrix is ​​completed.

[0107] In the dynamic atrial model, electrophysiological parameters (such as conductivity, time history curves, etc.) can be defined based on cell potential and tissue electrophysiological characteristics, and then mapped to the transfer matrix calculation.

[0108] The mapping process of electrophysiological parameters is as follows:

[0109] In the formula, Let be the conductivity of the i-th grid point. For cell potential, ECG i This is the ECG signal at this location.

[0110] By coupling the optimization model and the synergistic application of model geometry and electrophysiological parameters, the initially constructed electroactivity transfer matrix can not only fully adapt to the spatiotemporal synergistic characteristics of electroactivity, but also accurately combine the anatomical structure and electrophysiological essence of the model. This breaks through the limitations of traditional transfer matrices that ignore spatiotemporal coupling or electrophysiological details, enabling the initially constructed electroactivity transfer matrix to have high physiological fit and basic accuracy. It can provide a scientific and reliable basic framework for subsequent multi-objective global optimization, effectively ensuring the accuracy and practicality of the final transfer matrix.

[0111] Step 260-4: Based on the preset multiple optimization objectives, the initially constructed electrical activity transfer matrix is ​​iteratively optimized using a global optimization algorithm to obtain the optimized electrical activity transfer matrix.

[0112] Among them, multiple optimization objectives refer to multiple pre-set optimization criteria that must be satisfied simultaneously. The core can include minimizing the spatial and temporal errors of electrical activity transmission, as well as the geometric errors between the model geometry and the actual physiological state. The influence priority of each objective is balanced by weight coefficients. Global optimization algorithm refers to an algorithm that can search for the optimal solution in the entire solution space, not limited to local optima, such as particle swarm optimization (PSO) and genetic algorithm (GA), which are suitable for multi-objective collaborative optimization scenarios. Iterative optimization refers to the cyclic process of repeatedly adjusting the parameters of the transfer matrix through the algorithm, continuously calculating the fit with the multiple optimization objectives, until the error is reduced to a preset threshold or the optimal state is reached. The optimized electrical activity transfer matrix is ​​the core matrix that, after multi-objective global optimization, can accurately balance spatial, temporal, and geometric errors and truly reflect the spatiotemporal transmission law of atrial electrical activity. It is the key foundation for solving the inverse problem of electrocardiogram imaging.

[0113] In this embodiment of the present disclosure, multiple optimization objectives can be defined first, and the influence of spatial error, temporal error and geometric error can be quantified by weighting coefficients to construct a comprehensive optimization objective function. Then, a global optimization algorithm such as particle swarm optimization or genetic algorithm is selected, and the matrix parameters are iteratively adjusted in the solution space using the initially constructed electrical activity transfer matrix as the initial solution. After each iteration, the fit between the current matrix and the multiple optimization objectives is calculated, and it is determined whether the error meets the preset requirements. If it does not meet the requirements, the parameters are adjusted and the evaluation process is repeated until the comprehensive error is reduced to the minimum or reaches the optimal standard, and finally the optimized electrical activity transfer matrix is ​​obtained.

[0114] Specifically, optimization objectives may include: 1) minimizing spatial errors; 2) minimizing temporal errors; and 3) minimizing geometric errors (such as changes in atrial morphology). The formulaic characteristics defining the comprehensive optimization objective are described as follows:

[0115] in, These are weighting coefficients, which respectively control the effects of spatial error, temporal error, and geometric error.

[0116] When iteratively optimizing the initially constructed electrical activity transfer matrix using global optimization algorithms, multi-objective optimization problems are solved using global optimization algorithms such as Particle Swarm Optimization (PSO) or Genetic Algorithm (GA). The update rule for PSO is as follows:

[0117]

[0118] In the formula, For particle velocity, For the particle position, denoted as the individual's optimal position, g is the global optimal position, c1 and c2 are learning factors, and r1 and r2 are random numbers.

[0119] The accuracy of the transfer matrix is ​​comprehensively evaluated by calculating the overall error, including spatial error, temporal error, and geometric error. The global error evaluation formula is: In the formula, N is the number of measurement points. and It is a regulating factor.

[0120] By iteratively optimizing the initial transfer matrix using multiple preset optimization objectives and a global optimization algorithm, a synergistic balance between spatial, temporal, and geometric errors can be achieved, avoiding the accuracy limitations caused by single-objective optimization. Furthermore, the global optimization algorithm can overcome the limitations of local optima, significantly improving the accuracy and physiological relevance of the transfer matrix in describing the spatiotemporal transfer laws of electrical activity. This provides core support for the high-precision solution of subsequent inverse problems in electrocardiogram imaging and effectively reduces the deviation between reconstructed and actual electrical activity.

[0121] Step 270: Based on the electrical activity transfer matrix and the body surface potential signal, three-dimensional electrocardiogram imaging is achieved by solving the inverse problem of electrocardiogram imaging.

[0122] For embodiments of this disclosure, step 270 may include the following steps: Step 270-1: Collect the surface potential signal of the human body and perform filtering preprocessing on the surface potential signal.

[0123] Among them, the body surface potential signal refers to the potential data collected by electrodes placed on the human body surface. It can reflect the electrical signal characteristics of atrial electrical activity transmitted to the body surface through trunk tissues and is the core input data for solving the inverse problem of electrocardiogram imaging. Filtering preprocessing refers to the targeted processing of the collected raw body surface potential signal. The core is to remove redundant components such as power frequency interference and environmental noise in the signal through specific filtering techniques (such as low-pass filtering) and retain the effective signal related to atrial electrical activity.

[0124] In this embodiment of the present disclosure, raw surface potential signals reflecting the transmission characteristics of atrial electrical activity can be collected by reasonably arranging electrodes on the human body surface. Then, filtering techniques adapted to the characteristics of electrocardiogram signals, such as low-pass filtering, are used to process the raw signals, effectively removing irrelevant components such as power frequency interference and environmental noise, and finally obtaining a surface potential signal with high purity and strong effectiveness, providing qualified input for solving the inverse problem of electrocardiogram imaging.

[0125] By acquiring surface potential signals and performing filtering preprocessing, we can obtain basic data that reflects the transmission law of atrial electrical activity. At the same time, by removing interference components, we can significantly improve the signal-to-noise ratio and purity of the signal, avoid noise interference to subsequent applications of electrical activity transmission matrices and reconstruction of epicardial potentials, and ensure the accuracy and reliability of solving the inverse problem of electrocardiogram imaging from the source, providing high-quality data support for the entire technical process.

[0126] Step 270-2: Input the preprocessed body surface potential signal into the optimized electrical activity transfer matrix, solve the inverse problem of electrocardiogram imaging, and reconstruct the epicardial potential.

[0127] Among them, the inverse problem of electrocardiogram imaging refers to the mathematical problem of inferring the electrical activity of the heart surface (epidermal) from the body surface potential signal. The core is to solve the problem of non-unique solution caused by the underdetermined transfer matrix and realize the accurate restoration of the epicardial potential. The reconstruction of epicardial potential refers to the potential data that can reflect the actual electrical activity state of the heart surface by solving the inverse problem of electrocardiogram imaging and inferring it from the body surface potential signal. It is the core foundation for subsequent electrical activity assessment and three-dimensional imaging.

[0128] In this embodiment of the present disclosure, the body surface potential signal, after filtering and preprocessing to remove redundant interference, can be used as input data and imported into the electrical activity transfer matrix, which has been optimized globally through multi-objectives and can accurately describe the spatiotemporal transmission law of electrical activity. Based on the mapping relationship between epicardial potential and body surface potential depicted by the matrix, an optimization objective function is constructed by introducing regularization constraints. The inverse problem of electrocardiogram imaging is solved by using a global optimization algorithm, and finally the epicardial potential that can truly reflect the state of electrical activity on the surface of the heart is obtained by reverse deduction, thus completing the reconstruction of the epicardial potential.

[0129] By combining high-quality preprocessed body surface potential signals with optimized and accurate electrical activity transfer matrices to solve the inverse problem, we can fully utilize the advantage of high signal-to-noise ratio and accurately characterize the electrical activity transfer law with the help of the transfer matrix. This effectively solves the defects of non-unique solutions and large errors in traditional inverse problem solving, and can significantly improve the accuracy and physiological consistency of epicardial potential reconstruction. It provides reliable core data support for subsequent assessment of electrical activity envelope continuity and three-dimensional electrocardiogram imaging, further ensuring the accuracy and practicality of the entire electrocardiogram imaging process.

[0130] Step 270-3: Extract the electrical activity envelope of the reconstructed epicardial potential.

[0131] Among them, the electrical activity envelope refers to the instantaneous amplitude curve obtained after processing the reconstructed epicardial potential signal. It can intuitively reflect the overall trend of electrical activity signal changes, shield high-frequency detail interference, and highlight the continuity and smoothness of electrical activity.

[0132] In this embodiment of the present disclosure, a reconstructed epicardial potential time series signal obtained by solving the inverse problem of electrocardiogram imaging can be acquired. The potential signal is then processed using Hilbert transform. By calculating the instantaneous amplitude of the signal, an electrical activity envelope that can characterize the overall trend of electrical activity is extracted, providing an intuitive and quantifiable analytical object for subsequent evaluation of the continuity and smoothness of electrical activity.

[0133] When processing the potential signal using the Hilbert transform, and extracting the electrical activity envelope that characterizes the overall trend of electrical activity by calculating the instantaneous amplitude of the signal, the specific calculation formula can be as follows:

[0134] Wherein, EP(t) is the reconstructed epicardial potential signal. Let A(t) be the Hilbert transform of the signal, and let A(t) be the instantaneous amplitude (envelope).

[0135] By extracting the electrical activity envelope of the reconstructed epicardial potential, high-frequency redundant details in the potential signal can be effectively removed, focusing on the overall trend of electrical activity. This provides a precise analytical basis for subsequent assessment of continuity and smoothness based on the second derivative, avoiding interference from noise in the original signal on the assessment results. At the same time, it can clearly present the dynamic evolution of electrical activity, providing a key basis for judging whether the reconstructed electrical activity conforms to physiological characteristics, thereby ensuring the accuracy of subsequent model optimization and electrocardiogram imaging.

[0136] Step 270-4: Evaluate whether the envelope characteristics of the electrical activity envelope meet the preset optimization criteria.

[0137] For embodiments of this disclosure, the steps may include the following: performing multi-scale decomposition on the extracted electrical activity envelope to obtain envelope signals of different frequency components; calculating the second derivative of the envelope signals of different frequency components, and obtaining the second derivative evaluation result by taking the average absolute value of the second derivative; performing feature analysis on the envelope signals of different frequency components to identify potential abrupt change points; scoring the continuity and smoothness of different frequency components based on the second derivative evaluation result and the abrupt change point identification result; performing weighted summation on the scores of each frequency component to obtain the overall evaluation score of the electrical activity envelope; if the overall evaluation score is greater than or equal to a preset threshold, it is determined that the characteristics of the electrical activity envelope meet the preset optimization standard; if the overall evaluation score is less than the preset threshold, it is determined that the envelope characteristics of the electrical activity envelope do not meet the preset optimization standard.

[0138] Multi-scale decomposition refers to the process of breaking down the envelope of electrical activity into multiple components of different frequencies using techniques such as wavelet transform or high-frequency-low-frequency separation, which can capture signal characteristics of different frequency bands. Envelope signals of different frequency components refer to the electrical activity envelope segments corresponding to different frequency bands such as high-frequency and low-frequency components after multi-scale decomposition, each reflecting the variation law of electrical activity within a specific frequency range. Second-order derivative evaluation results refer to the quantitative data obtained by calculating the average of the absolute values ​​of the second-order derivatives of the envelope signals of each frequency component, used to intuitively measure the smoothness of the envelope. Abrupt change points refer to rapid fluctuations or discontinuities in the electrical activity envelope over time, deviating from the stable changes of normal electrical activity. The continuity and smoothness score refers to the quantitative score assigned to each frequency component envelope signal based on the second derivative evaluation results and the identification of abrupt change points. The higher the score, the better the continuity and smoothness. The overall evaluation score is the comprehensive score obtained by weighting and summing the continuity and smoothness scores of each frequency component according to preset weights, which reflects the overall characteristics of the electrical activity envelope. The preset optimization standard is a pre-set standard for judging whether the electrical activity envelope conforms to physiological laws. The core criterion is whether the overall evaluation score reaches the preset threshold. The preset threshold is a critical score determined based on experimental data and actual physiological conditions. It is the core reference value used to distinguish whether the characteristics of the electrical activity envelope meet the standard.

[0139] In specific application scenarios, the second derivative of the extracted electrical activity envelope is calculated to capture the curvature of the electrical activity envelope (i.e., the acceleration of its change). The better the continuity and smoothness of the electrical activity envelope, the smaller the absolute value of its second derivative.

[0140]

[0141] Where A(t) is the electrical activity envelope, It is the second derivative.

[0142] The mean of the absolute values ​​of the second derivatives of the electrical activity envelope can then be calculated as an evaluation criterion for smoothness and continuity. If the changes in the envelope are too drastic, the absolute value of the second derivative will be large, indicating that there are abrupt changes in the electrical activity; conversely, it indicates that the electrical activity is smoother and more continuous.

[0143]

[0144] Where T is the total duration of the signal. It is the second derivative.

[0145] When performing multi-scale decomposition on the extracted electrical activity envelope, wavelet transform or high-frequency-low-frequency separation is applied to the envelope to decompose the electrical activity signal into multiple frequency components. Then, the continuity and smoothness of the envelope of each frequency component are evaluated to obtain the evaluation results at each scale.

[0146] The wavelet transform formula is:

[0147] in, In scale and location The decomposition coefficients, It is a wavelet function.

[0148] When calculating the second derivative of each scale obtained from the decomposition and evaluating its smoothness, the calculation formula can be:

[0149] In the formula, Let be the envelope of electrical activity at scale a.

[0150] When the evaluation results at each scale are weighted and summed to obtain the overall continuity and smoothness score of the electrical activity envelope across multiple scales, the calculation formula can be:

[0151] in, The weights for each scale are given, where N is the total number of scales.

[0152] This assessment method comprehensively covers the electrical activity characteristics of different frequency components through multi-scale decomposition. It combines second-derivative assessment and mutation point identification to achieve accurate quantification of continuity and smoothness. The comprehensive assessment score is then obtained through weighted summation. This method avoids the limitations of single-dimensional assessment and ensures the objectivity of the judgment by comparing the quantitative score with the threshold. It can accurately identify whether the electrical activity envelope conforms to physiological laws, providing a reliable basis for subsequent model adjustment or inverse problem solving effect verification, and effectively ensuring the accuracy and physiological consistency of electrocardiogram imaging.

[0153] Step 270-5: If the envelope characteristics meet the preset optimization criteria, then generate three-dimensional electrocardiogram imaging data based on the reconstructed epicardial potential.

[0154] In this embodiment of the disclosure, after confirming that the continuity and smoothness of the electrical activity envelope meet the preset optimization criteria, the epicardial potential can be accurately reconstructed as the core, and key features such as potential distribution and conduction sequence in different cardiac phases can be extracted. These electrical activity features are spatiotemporally matched with the three-dimensional geometric morphology (including anatomical contour, grid distribution, etc.) of the optimized dynamic atrial model. The dynamic changes of electrical activity are integrated with the spatial structure of the atrium through three-dimensional visualization technology, and finally, three-dimensional electrocardiogram imaging data that can intuitively show the spatiotemporal distribution law of atrial electrical activity is generated.

[0155] By generating three-dimensional electrocardiogram (ECG) imaging data based on reconstructed epicardial potential after the electrical activity envelope characteristics meet the standards, it is possible to ensure that the core source of imaging data (reconstructed epicardial potential) has high accuracy and physiological consistency. Furthermore, it is possible to achieve deep integration of electrical activity and atrial spatial structure through three-dimensional visualization, breaking through the limitations of traditional two-dimensional ECG in presenting spatial distribution and dynamic conduction. This allows the generated three-dimensional ECG imaging data to intuitively and accurately reflect the true state of atrial electrical activity, providing more comprehensive and reliable visualization support for the clinical diagnosis of cardiovascular diseases and significantly enhancing the clinical application value of ECG imaging.

[0156] In summary, the technical solution in this application collects multi-temporal atrial imaging data and physiological state-related data such as respiratory signals and body position changes. It then constructs an initial dynamic atrial model reflecting morphological changes during the cardiac cycle using adaptive mesh optimization technology. A self-adjusting geometric optimization algorithm is then used to precisely optimize the model's morphology and position to adapt to physiological states, thereby generating an electrical activity transfer matrix that accurately describes the characteristics of atrial electrical activity transmission. Finally, based on this matrix and body surface potential signals, a three-dimensional electrocardiogram (ECG) is achieved by solving the inverse problem of ECG imaging. This effectively addresses the shortcomings of traditional static atrial models, which cannot adapt to dynamic atrial changes and ignore the influence of physiological factors. It significantly improves the accuracy of electrical activity reconstruction, the model's adaptability to physiological changes, and the precision and reliability of three-dimensional ECG imaging. Furthermore, the assessment of the continuity of the electrical activity envelope ensures the physiological consistency of the reconstruction results, providing more precise technical support for the clinical diagnosis of cardiovascular diseases.

[0157] Furthermore, as Figure 1 and Figure 2 The specific implementation of the method shown in this embodiment provides a three-dimensional electrocardiogram imaging device based on a dynamic atrial model, such as... Figure 6 As shown, the device may include: an acquisition module 61, a construction module 62, an optimization module 63, a generation module 64, and an imaging module 65.

[0158] The acquisition module 61 can be used to acquire multi-temporal imaging data of the atrium and physiological state-related data, including at least respiratory signals and body position change data. Module 62 can be used to construct an initial dynamic atrial model based on multi-temporal atrial imaging data and combined with adaptive grid optimization technology; Optimization module 63 can be used to optimize the morphology and position of the initial dynamic atrial model using physiological state-related data and a self-adjusting geometric optimization algorithm; The generation module 64 can be used to generate an electrical activity transfer matrix based on the optimized dynamic atrial model. The electrical activity transfer matrix is ​​used to describe the transfer characteristics of atrial electrical activity. The imaging module 65 can be used to achieve three-dimensional electrocardiogram imaging by solving the inverse problem of electrocardiogram imaging based on the electrical activity transfer matrix and body surface potential signal.

[0159] In some embodiments of this application, the acquisition module 61 can be specifically used to determine multiple key phases within the atrial cardiac cycle, the key phases of which at least cover end-systole and end-diastole; to perform CT or MRI scans on the multiple key phases respectively to obtain corresponding atrial multi-phase image data; and to acquire physiological state-related data through surface sensors.

[0160] In some embodiments of this application, the construction module 62 can be specifically used to register preprocessed multi-temporal atrial image data to ensure spatial consistency of images at different temporal phases; based on the registered multi-temporal atrial image data, analyze the atrial geometric characteristics of each key temporal phase; based on the atrial geometric characteristics, use an adaptive mesh generation algorithm to generate high-density meshes in key atrial regions and low-density meshes in flat regions; according to the mesh distribution and multi-temporal atrial image data, perform three-dimensional reconstruction of the atrium at each key temporal phase; integrate the three-dimensional reconstruction results of each key temporal phase to form an initial dynamic atrial model that can change with the cardiac cycle.

[0161] In some embodiments of this application, the optimization module 63 can be specifically used to establish a dynamic change model of atrial position based on the collected respiratory signals and body position change data; using the dynamic change model as a reference, the initial dynamic atrial model is optimized in terms of shape and position according to the order of scaling, rotation, and displacement, and electrical activity feedback information is obtained. The optimization process includes kinematic constraints, smooth transition processing, and electrical activity feedback iteration; the adjustment effect of the current strategy is judged based on the electrical activity feedback information. If the current strategy cannot improve the continuity of the electrical activity envelope, the next strategy is switched, and the model parameters are iteratively adjusted until the electrical activity feedback information meets the preset standard. It is then determined that the initial dynamic atrial model is adapted to the current physiological state, and the optimized dynamic atrial model is obtained.

[0162] In some embodiments of this application, the generation module 64 can be specifically used to perform electrophysiological modeling on the optimized dynamic atrial model, obtain the electrophysiological parameters corresponding to each atrial grid point, including tissue conductivity, cell potential, and current density; consider the temporal variation and spatial distribution characteristics of electrical activity, establish a time-space coupled optimization model; based on the coupled optimization model, combined with the model geometry and electrophysiological parameters, initially construct the electrical activity transfer matrix; based on preset multi-optimization objectives, iteratively optimize the initially constructed electrical activity transfer matrix through a global optimization algorithm to obtain the optimized electrical activity transfer matrix.

[0163] In some embodiments of this application, the imaging module 65 can be specifically used to acquire the body surface potential signal of the human body, perform filtering preprocessing on the body surface potential signal; input the preprocessed body surface potential signal into the optimized electrical activity transfer matrix, solve the inverse problem of electrocardiogram imaging, and reconstruct the epicardial potential; extract the electrical activity envelope of the reconstructed epicardial potential; evaluate whether the envelope characteristics of the electrical activity envelope meet the preset optimization criteria; if the envelope characteristics meet the preset optimization criteria, then generate three-dimensional electrocardiogram imaging data based on the reconstructed epicardial potential.

[0164] In some embodiments of this application, when evaluating whether the envelope characteristics of the electrical activity envelope meet the preset optimization criteria, the imaging module 65 can specifically be used to perform multi-scale decomposition on the extracted electrical activity envelope to obtain envelope signals of different frequency components; calculate the second derivative of the envelope signals of different frequency components, and obtain the second derivative evaluation result by taking the average absolute value of the second derivative; perform feature analysis on the envelope signals of different frequency components to identify potential abrupt change points; score the continuity and smoothness of different frequency components based on the second derivative evaluation result and the abrupt change point identification result; perform weighted summation on the scores of each frequency component to obtain the overall evaluation score of the electrical activity envelope; if the overall evaluation score is greater than or equal to a preset threshold, it is determined that the characteristics of the electrical activity envelope meet the preset optimization criteria; if the overall evaluation score is less than the preset threshold, it is determined that the envelope characteristics of the electrical activity envelope do not meet the preset optimization criteria.

[0165] It should be noted that other corresponding descriptions of the functional units involved in the three-dimensional electrocardiogram imaging device based on a dynamic atrial model provided in this embodiment can be found in [reference needed]. Figure 1 and Figure 2 The corresponding descriptions in [the document] will not be repeated here.

[0166] Based on the above, Figure 1 and Figure 2 Accordingly, this embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method. Figure 1 and Figure 2 The method for three-dimensional electrocardiogram imaging based on a dynamic atrial model is shown.

[0167] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause an electronic device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0168] Based on the above, Figure 1 and Figure 2 The method shown, and Figure 6To achieve the above objectives, the present application also provides an electronic device, specifically a personal computer, tablet computer, server, or other network device, as shown in the virtual device embodiment. This device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to achieve the above-described objectives. Figure 1 and Figure 2 The method for three-dimensional electrocardiogram imaging based on a dynamic atrial model is shown.

[0169] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0170] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0171] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0172] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platform, or it can be implemented by hardware.

[0173] This invention collects multi-temporal atrial imaging data and physiological state-related data such as respiratory signals and body position changes. It then constructs an initial dynamic atrial model reflecting morphological changes during the cardiac cycle using adaptive mesh optimization technology. A self-adjusting geometric optimization algorithm is then used to precisely optimize the model's morphology and position to suit physiological states, generating an electrical activity transfer matrix that accurately describes the characteristics of atrial electrical activity transmission. Finally, based on this matrix and body surface potential signals, a three-dimensional electrocardiogram (ECG) is achieved by solving the inverse problem of ECG imaging. This effectively addresses the shortcomings of traditional static atrial models, which cannot adapt to dynamic atrial changes and ignore the influence of physiological factors. It significantly improves the accuracy of electrical activity reconstruction, the model's adaptability to physiological changes, and the precision and reliability of three-dimensional ECG imaging. Furthermore, the assessment of the continuity of the electrical activity envelope ensures the physiological consistency of the reconstruction results, providing more precise technical support for the clinical diagnosis of cardiovascular diseases.

[0174] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0175] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A three-dimensional electrocardiogram imaging method based on a dynamic atrial model, characterized in that, include: Collect multi-temporal imaging data of the atrium and physiological state-related data, wherein the physiological state-related data includes at least respiratory signals and body position change data; Based on the aforementioned multi-temporal atrial imaging data, an initial dynamic atrial model was constructed using adaptive grid optimization technology. Using the physiological state-related data, the initial dynamic atrial model is optimized in terms of morphology and position through a self-regulating geometric optimization algorithm; An electrical activity transfer matrix is ​​generated based on the optimized dynamic atrial model, which is used to describe the transfer characteristics of atrial electrical activity. Based on the electrical activity transfer matrix and the body surface potential signal, three-dimensional electrocardiogram imaging is achieved by solving the inverse problem of electrocardiogram imaging.

2. The method according to claim 1, characterized in that, The collected multi-temporal atrial imaging data and physiological state-related data include: Identify multiple key phases within the atrial cardiac cycle, wherein the key phases at least cover end-systole and end-diastole; CT or MRI scans were performed on the multiple key time phases to obtain corresponding multi-temporal atrial imaging data. Physiological data are collected using surface sensors.

3. The method according to claim 1, characterized in that, Based on the aforementioned multi-temporal atrial imaging data, an initial dynamic atrial model is constructed using adaptive grid optimization technology, including: Register the preprocessed multi-temporal atrial image data to ensure spatial consistency of images from different temporal phases; Based on the registered multi-temporal atrial image data, the atrial geometric characteristics of each key temporal phase were analyzed. Based on the aforementioned atrial geometric characteristics, an adaptive mesh generation algorithm is used to generate high-density meshes in key atrial regions and low-density meshes in flat regions. Based on the grid distribution and the multi-temporal image data of the atrium, the atrium is reconstructed in three dimensions for each key temporal phase; By integrating the three-dimensional reconstruction results of each key time phase, an initial dynamic atrial model that can change with the cardiac cycle is formed.

4. The method according to claim 1, characterized in that, Using the aforementioned physiological state-related data, the initial dynamic atrial model is morphologically and positionally optimized using a self-regulating geometric optimization algorithm, including: A dynamic model of atrial position change was established based on the collected respiratory signals and body position change data. Using the dynamic change model as a reference, the initial dynamic atrial model is optimized in terms of shape and position according to the order of scaling, rotation, and displacement, and electrical activity feedback information is obtained. The optimization process includes kinematic constraints, smooth transition processing, and electrical activity feedback iteration. Based on the electrical activity feedback information, the adjustment effect of the current strategy is judged. If the current strategy cannot improve the continuity of the electrical activity envelope, the next strategy is switched, and the model parameters are iteratively adjusted until the electrical activity feedback information meets the preset standard. The initial dynamic atrial model is then judged to be suitable for the current physiological state, and the optimized dynamic atrial model is obtained.

5. The method according to claim 1, characterized in that, The generation of the electrical activity transfer matrix based on the optimized dynamic atrial model includes: Electrophysiological modeling was performed on the optimized dynamic atrial model to obtain the electrophysiological parameters corresponding to each atrial grid point. The electrophysiological parameters include tissue conductivity, cell potential and current density. Considering the temporal variation and spatial distribution characteristics of electrical activity, a coupled optimization model of time and space is established; Based on the aforementioned coupling optimization model, and combining the model geometry with the aforementioned electrophysiological parameters, an electroactivity transfer matrix is ​​initially constructed. Based on preset multiple optimization objectives, the initially constructed electrical activity transfer matrix is ​​iteratively optimized using a global optimization algorithm to obtain the optimized electrical activity transfer matrix.

6. The method according to claim 1, characterized in that, Based on the aforementioned electrical activity transfer matrix and body surface potential signals, three-dimensional electrocardiogram (ECG) imaging is achieved by solving the inverse problem of ECG imaging, including: Collect the surface potential signal of the human body and perform filtering preprocessing on the surface potential signal; The preprocessed body surface potential signal is input into the optimized electrical activity transfer matrix to solve the inverse problem of electrocardiogram imaging and reconstruct the epicardial potential. Extract the electrical activity envelope of the reconstructed epicardial potential; Evaluate whether the envelope characteristics of the electrical activity envelope meet the preset optimization criteria; If the envelope characteristics meet the preset optimization criteria, then three-dimensional electrocardiogram imaging data are generated based on the reconstructed epicardial potential.

7. The method according to claim 6, characterized in that, The evaluation of whether the envelope characteristics of the electrical activity envelope meet the preset optimization criteria includes: The extracted electrical activity envelope is decomposed into multiple scales to obtain envelope signals with different frequency components; The second derivative of the envelope signal with different frequency components is calculated, and the evaluation result of the second derivative is obtained by taking the average of the absolute values ​​of the second derivatives. Feature analysis is performed on envelope signals with different frequency components to identify potential abrupt change points; Based on the second derivative evaluation results and the mutation point identification results, the continuity and smoothness of different frequency components are scored. The scores of each frequency component are weighted and summed to obtain the overall evaluation score of the electrical activity envelope; If the overall evaluation score is greater than or equal to a preset threshold, then the characteristics of the electrical activity envelope are determined to meet the preset optimization criteria. If the overall evaluation score is less than the preset threshold, it is determined that the envelope characteristics of the electrical activity envelope do not meet the preset optimization criteria.

8. A three-dimensional electrocardiogram imaging device based on a dynamic atrial model, characterized in that, include: The acquisition module is used to acquire multi-temporal imaging data of the atrium and physiological state-related data, wherein the physiological state-related data includes at least respiratory signals and body position change data; The construction module is used to construct an initial dynamic atrial model based on the multi-temporal atrial imaging data and combined with adaptive grid optimization technology; The optimization module is used to optimize the morphology and position of the initial dynamic atrial model using the physiological state-related data and a self-adjusting geometric optimization algorithm. A generation module is used to generate an electrical activity transfer matrix based on an optimized dynamic atrial model, wherein the electrical activity transfer matrix is ​​used to describe the transfer characteristics of atrial electrical activity. The imaging module is used to achieve three-dimensional electrocardiogram imaging by solving the inverse problem of electrocardiogram imaging based on the electrical activity transfer matrix and the body surface potential signal.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

Images