Cardiac Electrophysiology Simulation System

Abstract

The present application provides a cardiac electrophysiological simulation system, comprising: an image acquisition module for acquiring multiple thoracic cavity images; a three-dimensional mesh reconstruction module for obtaining a three-dimensional ventricular mesh by means of the finite element method according to the multiple thoracic cavity images; a ventricular conduction network construction module for generating a ventricular conduction network according to the three-dimensional ventricular mesh, the ventricular conduction network including a plurality of first grid points, and the geometric positions of each of the first grid points sharing the point cloud of the three-dimensional ventricular mesh; an electrophysiological parameter simulation module for simulating the conduction process of cardiac action potentials in the ventricles according to the geometric positions and electrophysiological parameters of the first grid points, wherein the electrophysiological parameters include cell-level parameters and tissue-level parameters; and a dynamic simulation module for simulating cardiac electrical signals according to the conduction process of cardiac action potentials in the ventricles to obtain a dynamic simulation result.

Description

Technical Field

[0001] This application mainly relates to the field of biomedical engineering simulation, and particularly to a cardiac electrophysiology simulation system. Background Art

[0002] The simulation technology based on the heart can simulate the heart characteristics, diseases, etc., which can help doctors diagnose or evaluate diseases and is of great significance. Currently, in the research of heart simulation, usually a relatively large number of electrodes are used to collect multi-lead electrocardiogram signals on the surface of the torso, and then through the conduction matrix, the epicardial action potential phase map distribution is inversely deduced in the form of an electrocardiogram inverse problem, and then the lesion and treatment plan are evaluated. However, the related technologies of the electrocardiogram inverse problem face difficulties in principle and accuracy. In principle, the electrocardiogram inverse problem relies on the boundary element method to calculate the conduction matrix between the epicardium and the geometric grid on the torso surface to establish the relationship between the electric potentials on the corresponding grids. The calculation of the conduction matrix is extremely sensitive to the accuracy of the geometric grid and is interfered by the physiological parameters of other organs such as the patient's own chest cavity and lungs, resulting in limited accuracy of the obtained conduction matrix. In addition, the equation for solving the inverse problem is a severely underdetermined equation, which makes the numerical solution extremely unstable during calculation. The accuracy and number of electrodes on the torso surface, as input parameters, can never achieve the accuracy required to eliminate this instability for the underdetermined equation.

[0003] Therefore, the accuracy that the solution obtained from the inverse problem can meet is very limited. If used to predict lesion points, such as arrhythmia lesion points, its accuracy is unreliable.

[0004] The inventor of this application proposed a simulation system for cardiac ablation plan evaluation in the Chinese patent application with the publication number of CN119028528A, which can more accurately simulate the electrophysiological activities of actual myocardial fibers. In order to more accurately simulate the real pacing process of the heart and simulate the conduction of electrical signals in the His bundle and Purkinje fiber cell network, the inventor of this application proposed the cardiac electrophysiology simulation system of this application. Summary of the Invention

[0005] This application aims at the above technical problems and provides a cardiac electrophysiology simulation system, which can simulate the ventricular conduction network including the His bundle and Purkinje fiber network and realize the simulation of the overall cardiac electrophysiological activities.

[0006] To solve the above technical problems, the present application provides a cardiac electrophysiological simulation system, including: an image acquisition module for acquiring multiple thoracic cavity images, wherein the thoracic cavity images include ventricular images; a three-dimensional mesh reconstruction module for obtaining a ventricular three-dimensional mesh from the multiple thoracic cavity images by means of the finite element method; a ventricular conduction network construction module for generating a ventricular conduction network based on the ventricular three-dimensional mesh to simulate the conduction of cardiac action potentials in the ventricles, wherein the ventricular conduction network includes the His bundle and the Purkinje fiber network, the ventricular conduction network includes multiple first grid points, and the geometric positions of each first grid point share the point cloud of the ventricular three-dimensional mesh; an electrophysiological parameter simulation module for simulating the conduction process of cardiac action potentials in the ventricles according to the geometric positions and electrophysiological parameters of the first grid points, wherein the electrophysiological parameters include cell-level parameters and tissue-level parameters, the cell-level parameters include current parameters of ion channels, and the tissue-level parameters include conductivity, electrical signal propagation rate, and pacing stimulation parameters; and a dynamic simulation module for simulating cardiac electrical signals according to the conduction process of cardiac action potentials in the ventricles to obtain a dynamic simulation result.

[0007] In an embodiment of the present application, the ventricular conduction network construction module is configured to simulate and generate the ventricular conduction network by using the following method: forming a two-dimensional fractal network by using the fractal method; projecting the two-dimensional fractal network onto the endocardial surface to generate a three-dimensional fractal network, wherein the endocardial surface is a non-smooth surface; and using the three-dimensional fractal network as the ventricular conduction network.

[0008] In an embodiment of the present application, forming the two-dimensional fractal network by using the fractal method includes: creating an initial node and an initial branch of the two-dimensional fractal network by using the fractal method, wherein the initial branch has an initial growth direction; creating multiple new branches from the end of the initial branch, and iteratively creating the next-generation new branches from the new branches to generate the two-dimensional fractal network, wherein, during the iterative process, fractal motion parameters are set according to the geometric structure of the ventricle, and the fractal motion parameters include: branch length, branch angle, and repulsion parameter.

[0009] In an embodiment of the present application, each branch includes n line segments and n + 1 nodes, and the n + 1 nodes repel each other to generate a curve, wherein the direction vector of each node is determined by the direction vector of the previous node and the distance gradient, and n is a natural number.

[0010] In an embodiment of the present application, the projection of the two-dimensional fractal network onto the endocardial surface to generate a three-dimensional fractal network includes: obtaining the vertex normal of the current node, where the vertex normal is the weighted sum of all normals of the adjacent fractal meshes of the current node; projecting the current node onto the surface defined by the fractal mesh through the vertex normal; determining whether the projection result of the current node is within the fractal mesh; if the projection result of the current node is not within the fractal mesh, projecting the projection result onto the surface through the surface normal of the grid cell.

[0011] In an embodiment of the present application, the projecting the projection result onto the surface through the surface normal of the grid cell includes: calculating the direction vectors on all k - 1 nodes before the current kth node, where k is a positive integer; projecting the kth node onto the surface according to the direction vectors and the branch line segment lengths of all k - 1 nodes; if the projected kth node is within the fractal mesh, or the node line segment length is greater than or equal to a preset range, continue to project the next node, otherwise stop the iteration process.

[0012] In an embodiment of the present application, the following formula is used to calculate the distance gradient :

[0013] ,

[0014] where ϵ is a preset constant, e i represents the Cartesian basis vector, k represents the serial number of the current node on the ventricular conduction network, i represents a certain component x, y, z of the three-dimensional coordinates, g represents the distance gradient, p k represents the line segment length of the kth node on the ventricular three-dimensional grid, dist cp represents the function that returns the distance from node k to the nearest node in the network.

[0015] In an embodiment of the present application, the following formula is used to calculate the direction vector d of the kth node k :

[0016] ,

[0017] where w represents the repulsive parameter of the fractal structure, d k-1 represents the direction vector of the (k - 1)th node, represents the distance gradient of the (k - 1)th node;

[0018] The following formula is used to project the kth node onto the surface according to the direction vector d k and the branch line segment lengths of all k - 1 nodes:

[0019] 。

[0020] In one embodiment of the present application, the direction vector is initialized using the following formula:

[0021] ,

[0022] where d 0 represents the initial direction vector of the initial node, n 0 represents the initial normal vector of the initial node, and α represents the single-step change angle of the branch angle.

[0023] In one embodiment of the present application, the fractal method includes the k-d tree method.

[0024] In one embodiment of the present application, the pacing stimulation parameters correspond to the pacing stimulation, and the pacing stimulation includes a stimulation signal applied to the His bundle.

[0025] In one embodiment of the present application, the three-dimensional grid reconstruction module is further configured to label ablation points on the multiple thoracic cavity images, and obtain a cardiac three-dimensional grid by means of the finite element method. The ablation points include predicted ablation points to be ablated and / or actual ablation points that have been ablated; the cardiac electrophysiological simulation system further includes: a fiber rotation direction calculation module, configured to calculate the myocardial fiber rotation direction according to the cardiac three-dimensional grid to obtain the direction vector of the myocardial fiber rotation corresponding to each second grid point on the cardiac three-dimensional grid; the electrophysiological parameter simulation module is further configured to simulate the cardiac action potential excitation process according to the geometric position of the second grid point, the fiber rotation direction corresponding to the second grid point, and the electrophysiological parameters, where the electrophysiological parameters include cell-level parameters and tissue-level parameters, the cell-level parameters include the current parameters of ion channels, and the tissue-level parameters include the conductivity of different cardiac regions, pacing stimulation parameters, and the propagation rate of the electrical signal set according to the direction vector of the myocardial fiber rotation.

[0026] In one embodiment of the present application, the electrophysiological parameter simulation module obtains the current parameters of the ion channels corresponding to each first grid point on the ventricular conduction network based on the first ion channel model, and obtains the current parameters of the ion channels corresponding to each second grid point on the cardiac three-dimensional grid based on the second ion channel model, and the first ion channel model and the second ion channel model are different.

[0027] In one embodiment of the present application, the first grid point and the second grid point share the point cloud of the ventricular three-dimensional grid, the end of the ventricular conduction network is connected to the end of the cardiac three-dimensional grid, and the electrical signal can be conducted from the ventricular conduction network to the cardiac three-dimensional grid.

[0028] The cardiac electrophysiological simulation system of the present application obtains multiple thoracic cavity images through an image acquisition module, simulates the conduction of cardiac action potentials in the ventricles through a ventricular conduction network construction module, sets the physiological parameters corresponding to each first grid point in the ventricular conduction network through an electrophysiological parameter simulation module, and simulates cardiac electrical signals through a dynamic simulation module for the conduction process of cardiac action potentials in the ventricles. It can achieve personalized cardiac electrophysiological simulation for the subject, with accurate results and good timeliness, which is conducive to assisting users in applying the simulation results. Description of the Drawings

[0029] The accompanying drawings are provided to provide a further understanding of the present application. They are incorporated and constitute a part of the present application. The accompanying drawings illustrate embodiments of the present application and, together with this specification, serve to explain the principles of the present application. In the accompanying drawings:

[0030] Figure 1 is a block diagram of a cardiac electrophysiological simulation system according to an embodiment of the present application;

[0031] Figure 2 is an exemplary flowchart of generating a ventricular conduction network by a ventricular conduction network construction module in a cardiac electrophysiological simulation system according to an embodiment of the present application;

[0032] Figure 3 is an exemplary flowchart of forming a two-dimensional fractal network by a ventricular conduction network construction module in a cardiac electrophysiological simulation system according to an embodiment of the present application using a fractal method;

[0033] Figure 4 is an exemplary flowchart of a two-step projection method used by a ventricular conduction network construction module in a cardiac electrophysiological simulation system according to an embodiment of the present application;

[0034] Figure 5 is a schematic diagram of a single-step projection method;

[0035] Figure 6 is a schematic diagram of a two-step projection method according to an embodiment of the present application;

[0036] Figure 7 is an exemplary flowchart of projecting a current node onto a surface by a ventricular conduction network construction module in a cardiac electrophysiological simulation system according to an embodiment of the present application through the normal direction of a grid unit. Detailed Description of the Embodiment

[0037] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0038] As shown in the present application, unless the context clearly indicates an exceptional situation, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0039] Unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps described in these embodiments do not limit the scope of the present application. At the same time, it should be understood that for the sake of convenience of description, the sizes of the various parts shown in the accompanying drawings are not drawn in actual proportional relationships. Technologies, methods, and devices known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the said technologies, methods, and devices should be regarded as part of the specification. In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings represent similar items. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0040] In the description of the present application, it should be understood that the orientation or positional relationships indicated by orientation words such as "front, rear, upper, lower, left, right", "lateral, vertical, perpendicular, horizontal", and "top, bottom" are usually based on the orientation or positional relationships shown in the accompanying drawings. It is only for the convenience of describing the present application and simplifying the description. Without contrary explanations, these orientation words do not indicate and imply that the devices or elements referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the protection scope of the present application; the orientation words "inside, outside" refer to the inside and outside relative to the outline of each component itself.

[0041] For ease of description, spatial relative terms such as "above", "over", "on the upper surface", "upper" etc. can be used here to describe the spatial positional relationship of a device or feature shown in the figure with other devices or features. It should be understood that the spatial relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figure for the device. For example, if the device in the figure is inverted, a device described as "above" or "over" other devices or structures will then be positioned "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both the orientations of "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the corresponding interpretations are made for the spatial relative descriptions used here.

[0042] In addition, it should be noted that the use of terms such as "first", "second" etc. to limit components is only for the convenience of differentiating the corresponding components. Without further statement, the above terms have no special meaning, and thus should not be construed as a limitation on the protection scope of this application. In addition, although the terms used in this application are selected from well-known and commonly used terms, some of the terms mentioned in the specification of this application may be selected by the applicant according to his or her judgment, and their detailed meanings are described in the relevant parts of the description herein. In addition, it is required to understand this application not only through the actual terms used, but also through the meaning implied by each term.

[0043] Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the operations before or after may not necessarily be executed precisely in sequence. Instead, various steps can be executed in reverse order or simultaneously. At the same time, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0044] The cardiac electrophysiological simulation system of this application is proposed based on an improvement on a simulation system for cardiac ablation scheme evaluation proposed by the inventors of this application in a Chinese patent application with the publication number CN119028528A. The entire content of CN119028528A can be used to assist in explaining the relevant content in the simulation system of this application.

[0045] The cardiac electrophysiological simulation system of this application can simulate the electrophysiological activities of the heart, especially the conduction process of cardiac action potentials in the ventricles, so as to simulate the conduction process of real cardiac electrical excitation in the whole heart including the ventricles.

[0046] Figure 1It is a block diagram of a cardiac electrophysiological simulation system according to an embodiment of the present application. Refer to Figure 1 As shown, the cardiac electrophysiological simulation system 100 of this embodiment includes an image acquisition module 110, a three-dimensional mesh reconstruction module 120, a ventricular conduction network construction module 130, an electrophysiological parameter simulation module 140, and a dynamic simulation module 150. Among them, the image acquisition module 110 is used to acquire multiple thoracic cavity images, and the ventricular images are included in the thoracic cavity images; the three-dimensional mesh reconstruction module 120 is used to obtain a ventricular three-dimensional mesh by means of finite elements according to multiple thoracic cavity images; the ventricular conduction network construction module 130 is used to generate a ventricular conduction network according to the ventricular three-dimensional mesh to simulate the conduction of cardiac action potentials in the ventricles, wherein the ventricular conduction network includes the His bundle and the Purkinje fiber network, the ventricular conduction network includes a plurality of first grid points, and the geometric positions of each first grid point share the point cloud of the ventricular three-dimensional mesh; the electrophysiological parameter simulation module 140 is used to simulate the conduction process of cardiac action potentials in the ventricles according to the geometric positions and electrophysiological parameters of the first grid points, wherein the electrophysiological parameters include cell-level parameters and tissue-level parameters, the cell-level parameters include the current parameters of ion channels, and the tissue-level parameters include conductivity, electrical signal propagation rate, and pacing stimulation parameters; the dynamic simulation module 150 is used to simulate cardiac electrical signals according to the conduction process of cardiac action potentials in the ventricles to obtain a dynamic simulation result.

[0047] The above-mentioned image acquisition module 110, three-dimensional mesh reconstruction module 120, ventricular conduction network construction module 130, electrophysiological parameter simulation module 140, and dynamic simulation module 150 will be described in detail below.

[0048] This application does not limit how the image acquisition module 110 obtains these thoracic cavity images.

[0049] In some embodiments, the image acquisition module 110 is specifically implemented as an MRI device, and more specifically, it can be implemented as an LGE-MRI device for obtaining LGE-MRI enhanced nuclear magnetic resonance images.

[0050] In some embodiments, the image acquisition module 110 is specifically implemented as a medical image device such as a CT.

[0051] The three-dimensional mesh reconstruction module 120 obtains multiple required thoracic images from the image acquisition module 110. In some embodiments, the left ventricular endocardium, right ventricular endocardium, and epicardium are segmented from the multiple axial enhanced MRI slice images of the thorax, which is equivalent to marking the boundaries of the left ventricular endocardium, right ventricular endocardium, and epicardium on each thoracic image. Further, the three-dimensional mesh reconstruction module 120 obtains a ventricular three-dimensional mesh from the multiple thoracic images by means of the finite element method. The ventricular three-dimensional mesh corresponds to the ventricles, including the left ventricle and / or the right ventricle, and includes a plurality of mesh points. The present application places no restrictions on the mesh form, which may be a triangular mesh, a tetrahedral mesh, a hexahedral mesh, etc. The finite element method used is a common method in the art and will not be elaborated here.

[0052] In other embodiments, for example, for the atrial region, the same operation can also be used to mark the left atrium, right atrium, and the outer surface of the atrium. In this way, an atrial three-dimensional mesh can be generated. It can be understood that in some embodiments, a cardiac three-dimensional mesh including the atria and ventricles can be generated. At the same time, the abnormal regions on the MRI slice images can also be marked by means of finite element software. The present application places no restrictions on the number of thoracic images. The multiple thoracic images can be multiple thoracic slice images from a single subject. The more thoracic images, the more beneficial it is for the subsequent simulation and emulation processes.

[0053] It should be noted that the His bundle and the Purkinje fiber network are key components of the cardiac excitation system. The Purkinje fibers, also known as bundle cells, are a special type of myocardial fiber. The Purkinje fiber network is composed of special fast-conducting cells and is located subendocardially, that is, directly below the inner wall of the heart. Compared with myocardial cells, Purkinje cells are larger, have fewer myofibrils, and more mitochondria, so as to conduct excitation waves efficiently and rapidly. In the present application, the ventricular conduction network construction module 130 can simulate and generate a ventricular conduction network according to the ventricular three-dimensional mesh, simulate the conduction of cardiac action potentials in the ventricles, and can simulate the conduction process of cardiac electrical signals through the His bundle and the Purkinje fiber network, which is beneficial to discovering problems existing in the ventricular electrophysiological conduction of patients. The application of the simulation results is not limited to the diagnosis of heart-related diseases, and can also be used for other scientific research purposes such as drug research and development.

[0054] In some embodiments, the finite element method can be used to simulate and generate the ventricular conduction network.

[0055] Figure 2 This is an exemplary flowchart for the ventricular conduction network construction module in the cardiac electrophysiological simulation system of an embodiment of the present application to generate a ventricular conduction network. Refer to Figure 2As shown, in this embodiment, the ventricular conduction network construction module is configured to simulate and generate the ventricular conduction network by using the following method:

[0056] Step S210: Form a two-dimensional fractal network by using the fractal method;

[0057] Step S220: Project the two-dimensional fractal network onto the endocardial surface to generate a three-dimensional fractal network, where the endocardial surface is a non-smooth surface; and

[0058] Step S230: Use the three-dimensional fractal network as the ventricular conduction network.

[0059] The fractal method is a mathematical method for generating complex self-similar structures and is widely used in fields such as computer graphics, natural phenomenon simulation, and art design. Its basic implementation method is to generate structures with sub-similarity through recursion or iteration. The inventors of the present application found that the His bundle and Purkinje fiber network also exhibit certain self-similarity at different scales, so the fractal method is applied to the cardiac electrophysiological simulation system of the present application.

[0060] The fractal method is generally applied to two-dimensional space or two-dimensional graphics. In step S210, first, a two-dimensional fractal network is formed by using the fractal method. Figure 3 It is an exemplary flowchart of a ventricular conduction network construction module in a cardiac electrophysiological simulation system of an embodiment of the present application using the fractal method to form a two-dimensional fractal network. Refer to Figure 3 As shown, in this embodiment, step S210 specifically includes:

[0061] Step S211: Create the initial nodes and initial branches of the two-dimensional fractal network by using the fractal method, and the initial branches have initial growth directions;

[0062] Step S212: Create multiple new branches from the ends of the initial branches and iteratively create the next-generation new branches from the new branches to generate a two-dimensional fractal network. During the iteration process, the fractal motion parameters are set according to the geometric structure of the ventricle, and the fractal motion parameters include: branch length, branch angle α, and repulsion parameter w.

[0063] In some implementations, the fractal method includes the k-d tree method, or "k-d tree algorithm". A k-d tree is a data structure for organizing multi-dimensional space data and is mainly used for efficient spatial search operations, such as nearest neighbor search, range search, etc. The k-d tree method constructs a binary tree structure by recursively dividing the data space into two sub-spaces. At each level, the k-d tree selects a dimension as the division axis and divides the data points into two parts according to the median of this dimension, which are stored in the left subtree and right subtree of the tree respectively.

[0064] When using the k-d tree method, "creating multiple new branches from the end of the initial branch" in step S212 includes: creating 2 new branches from the end of the initial branch.

[0065] This application uses the k-d tree method in the fractal method to generate a two-dimensional fractal network for simulating the ventricular conduction network. Specifically, this application uses the construction strategy of the k-d tree to determine the collision between two nodes in the ventricular conduction network. To detect potential collisions, all nodes in the ventricular conduction network are considered, excluding the nodes in the parent branch and sibling branches. If the distance between the new node and any existing node is less than a certain tolerance, the branch cannot grow further and the new node is removed. In some cases, a closed loop can be created by adding the nearest node in the network as the last node in the branch.

[0066] In some embodiments, the above fractal method includes three random sources: First, calculate the branch length of each branch k from a normal distribution with a mean of β and a variance of 0.4β 2 ; second, randomly shuffle the order of the growing branches in each generation to randomly distribute the influence of curvature on the existing nodes; third, randomly shuffle the order of the growing sub-branches. The following gives a pseudo-code example of a k-d tree fractal growth algorithm:

[0067] for generation = 1 to total number of generations

[0068] Shuffle the current nodes

[0069] for node in current nodes

[0070] for current child node = 1 to total number of child nodes

[0071] Create a new child node

[0072] if the child node does not touch the existing figure and is within the boundary

[0073] Add the child node to the current nodes

[0074] According to the above pseudo-code example, the two-dimensional fractal network in step S210 can be formed.

[0075] In step S212, the repulsion parameter is used to adjust the curvature of the branches. The larger the repulsion parameter, the stronger the repulsion between the branches. The fractal motion parameters are all set according to the ventricular three-dimensional grid. This ventricular three-dimensional grid comes from the cardiac three-dimensional grid constructed by the three-dimensional grid reconstruction module 120. In this way, it is possible to realize the personalized construction of the ventricular conduction network for the ventricle of a certain subject.

[0076] In some embodiments, each branch includes n line segments and n + 1 nodes. Thus, each branch is represented as a broken line with n + 1 nodes. Among them, the n + 1 nodes mutually exclude to generate a curve, which can be achieved by determining the direction vector of each line segment in the branch. Specifically, the direction vector of each node is determined by the direction vector of the previous node and the distance gradient, where n is a natural number. The specific calculation method and application of the distance gradient will be described later.

[0077] After the ventricular conduction network construction module 130 obtains the two-dimensional fractal network in step S210, in step S220, the two-dimensional fractal network is projected onto the endocardial surface to generate a three-dimensional fractal network.

[0078] It should be noted that the endocardial surface is a non-smooth surface. In some embodiments, the projection step in step S220 includes: projecting the nodes onto the plane defined by the fractal grid through the normal of the fractal grid, and checking whether the projected nodes are inside the fractal grid. If so, it means the projection result is available; otherwise, it means the projection result is unavailable. It should be noted that the shape of the fractal grid can be a triangle, a polygon, etc. In each step of iteration of the fractal method, when a new branch is generated, the new nodes are projected, so that the two-dimensional fractal network is projected into a three-dimensional fractal network. This projection method is also called single-step projection. For regular and smooth surfaces, single-step projection can achieve good results. However, in some cases, for irregular and non-smooth surfaces, the single-step projection effect is poor and may fail. This is because when there are sharp angles on the surface, the single-step projection method has blind spots. Therefore, in some embodiments, the present application adopts a two-step projection method. Figure 4 It is an exemplary flowchart of the two-step projection method adopted by the ventricular conduction network construction module in a cardiac electrophysiological simulation system according to an embodiment of the present application. Refer to Figure 4 As shown, the two-step projection method includes:

[0079] Step S221: Obtain the vertex normal of the current node, where the vertex normal is the weighted sum of all the normals of the adjacent fractal grids of the current node;

[0080] Step S222: Project the current node onto the surface defined by the fractal grid through the vertex normal;

[0081] Step S223: Determine whether the projection result of the current node is inside the fractal grid;

[0082] Step S224: If the projection result of the current node is not inside the fractal grid, project the projection result onto the surface through the surface normal of the grid cell.

[0083] Among them, steps S221 - S222 are an additional pre - projection step based on single - step projection, and step S224 is the second projection. If the judgment result of step S223 is "yes", then step S224 is not required.

[0084] Figure 5 is a schematic diagram of a single - step projection method. As Figure 5 shown, when there are sharp angles on the surface, the single - step projection method has blind areas, making the projected points within the blind areas unable to be projected onto the grid surface. The blind area is the Figure 5 area shown as an inverted triangle including the projected point. As Figure 5 shown, the projected points P1 and P2 in

[0085] According to the above - mentioned embodiment, in step S222, the vertex normal is used to attempt a pre - projection once, and then according to the pre - projection result, the surface normal of the grid cell is used for re - projection, which can ensure that the projection result is on the surface defined by the fractal grid.

[0086] Figure 6 is a schematic diagram of the two - step projection method of an embodiment of the present application. As Figure 6 shown, first, in step S222, a pre - projection is made along the vertex normal 610 to obtain the projected point P3. The projected point P3 is outside the fractal grid. Secondly, in step S224, the projected point P3 is projected onto the grid surface through the surface normal 620 of the grid cell to obtain the projected point P4. The projected point P4 is within the fractal grid.

[0087] The above - mentioned embodiment adopts the two - step projection method, enabling the projected points within the blind area to also be projected onto the grid surface.

[0088] Figure 7 is an exemplary flowchart of the ventricular conduction network construction module in the cardiac electrophysiological simulation system of an embodiment of the present application projecting the projection result onto the surface through the surface normal of the grid cell. Referring to Figure 7 shown, in this embodiment, projecting the projection result onto the surface through the surface normal of the grid cell in step S224 includes:

[0089] Step S2241: Calculate the direction vectors of the k - 1 nodes before the current k - th node, where k is a positive integer;

[0090] Step S2242: Project the k - th node onto the surface according to the direction vectors and the branch segment lengths of all k - 1 nodes;

[0091] Step S2243: If the projected k - th node is within the fractal grid, or the node segment length is greater than or equal to a preset range, then continue to project the next node, otherwise stop the iterative process.

[0092] In some embodiments, the distance gradient is calculated using a central finite difference approximation. For example, the distance gradient is calculated using the following formula (1) :

[0093] (1)

[0094] where ϵ is a preset constant, e i represents the Cartesian basis vector, k represents the serial number of the current node on the ventricular conduction network, i is a component x, y, z of the three-dimensional coordinates, g represents the gradient of distance, p k represents the line segment length of the k-th node on the ventricular three-dimensional grid, dist cp represents a function that returns the distance from node k to the nearest node in the network.

[0095] In some embodiments, the direction vector d of the current k-th node in step S2241 is calculated using the following formula (2) k :

[0096] (2)

[0097] where w represents the repulsion parameter, d k-1 represents the direction vector of the (k - 1)-th node, represents the distance gradient of the (k - 1)-th node;

[0098] The projection of the k-th node onto the surface according to the direction vector d k and the branch segment lengths of all (k - 1) nodes is implemented using the following formula (3):

[0099] (3)

[0100] In some embodiments, before step S2241, an initialization step is further included. For example: the direction vector d is initialized using the following formula 0 :

[0101] (4)

[0102] where d 0 represents the initial direction vector of the initial node, n 0 represents the initial normal vector of the initial node, and α represents the single-step change angle of the branch angle. Among them, these initial parameters can all be set according to experience.

[0103] The following gives a pseudo-code example for implementing step S224:

[0104]

[0105] For k = 1 to the total number of generations

[0106] Calculate the gradient direction of all nodes p before the k-th node k-1 :

[0107] ,

[0108] At the same time, project the node onto the grid surface .

[0109] If p k Exceeds the grid or is less than the error

[0110] Break

[0111] It should be noted that p k Represents the line segment length of the k-th node on the ventricular three-dimensional grid, and this line segment length is calculated from the coordinates of the line segment in three-dimensional space. Therefore, p k Includes three-dimensional coordinate information. According to this three-dimensional coordinate information, it can be judged whether the line segment represented by p k Exceeds the grid. At the same time, according to the line segment length represented by p k To judge whether it is less than the error. This error can be set artificially according to experience.

[0112] Reference Figure 1 , the electrophysiological parameter simulation module 140 can simulate and generate electrophysiological parameters corresponding to the ventricular conduction network from the cellular level and tissue level, so that each first grid point on the ventricular conduction network has corresponding electrophysiological parameters.

[0113] In some embodiments, the electrophysiological parameter simulation module 140 obtains the current parameters of the ion channels corresponding to each first grid point on the ventricular conduction network based on the first ion channel model. The electrophysiological parameters can be built into the electrophysiological parameter simulation module 140 or set by the user through a graphical user interface. In one embodiment, the ion channel model is the Stewart model (Stewart, 2009). The transmembrane domain of the ion channel forms an aqueous pore that allows ions to pass through smoothly. It only allows specific ions to pass through and controls the transmembrane flow of specific ions by opening and closing a "gate". Many different types of ion channels are usually found on the myocardial cell membrane, among which sodium (Na+), potassium (K+), and calcium (Ca2+) channels are the main types of ion channels. The types of ion channels in this application are not limited. The ion current satisfies Ohm's law: Ix = Gxp(Vm - Ex), where Ix represents the ion current of ion x, Gx represents the maximum conductance of ion x, Vm represents the transmembrane potential, Ex represents the equilibrium potential of ion x, and p represents the open probability coefficient of the ion channel. The ion channel is voltage-controlled, that is, the opening and closing of the ion channel are affected by the transmembrane potential across the membrane.

[0114] In terms of tissue dimension, the electrophysiological parameters include the conductivity, electrical signal propagation rate, and pacing stimulation parameters of different cardiac regions. It should be noted that the conductivity and electrical signal propagation rate of Purkinje fibers in the normal region are different from those in the abnormal region. The pacing stimulation parameters can be set according to the clinical requirements of the patient. In some embodiments, the pacing stimulation parameters correspond to pacing stimulation, and the pacing stimulation includes a stimulation signal applied to the His bundle. For example, to simulate normal sinus rhythm, a stimulation signal simulating a normal cardiac pacing signal is applied to the His bundle. The position of the stimulation point can be the starting point of the His bundle, that is, the position of the atrioventricular node. To simulate abnormal heart rhythms, such as arrhythmia, a stimulation signal simulating an abnormal cardiac pacing signal is applied to the His bundle. The position of the stimulation point can be the position corresponding to any node in the ventricular conduction network.

[0115] Based on the above, the electrophysiological parameter simulation module 140 can simulate the conduction process of the cardiac action potential in the ventricle. On this basis, the dynamic simulation module 150 can simulate the cardiac electrical signal according to the conduction process of the cardiac action potential in the ventricle and obtain the simulation result. The dynamic simulation module 150 can visually present the simulation result.

[0116] Specifically, in one embodiment, the dynamic simulation module 150 simulates the cardiac electrical signal based on the Reaction-Diffusion process, and the simulation process includes:

[0117] Step S310: Take the transmembrane potential and the reset voltage on the time-space evolution scale as the contraction variables u, v and the nonlinear response functions f, g, which satisfy equations (5) and (6):

[0118] (5)

[0119] (6)

[0120] Among them, and are the coefficients corresponding to the contraction variables u, v, ∇ 2 is the Laplace operator, . More specifically, D1 and D2 are used to characterize the conductivity of the transmembrane current and the slow local recovery current in the ion channel model corresponding to the diffusion process.

[0121] It should be noted that the cardiac electrical activity in a single cell involves the coordinated transport of a large number of ions through various biological channels. The electrical wave also spreads from the cell to the whole heart, combining excitation with contraction and blood ejection. Some simplified assumptions and rules, such as the ability of the cellular automaton model to simulate cardiac electrical activity, are limited, especially in the simulation ability at the whole heart level. The Reaction-Diffusion model describes the distribution change of dynamic variables in space and time, which includes two processes: (1) the Reaction process: the dynamic variables interact to transform, and (2) the Diffusion process: the dynamic variables diffuse in space. The potential propagation process determined by the Reaction-Diffusion model is reflected in the time-space evolution as the boundary ∂Ω of the continuously diffusing contour map. Therefore, the Reaction-Diffusion model can more realistically simulate the electrical activity of the whole heart.

[0122] Step S320: Discretize equations (5) and (6) in step S310 in the time dimension, let t = 1, 2, …, T, and record the grid point numbers i = 1, 2, …, N in the ventricular conduction network, to obtain the following equations:

[0123] (7)

[0124] (8)

[0125] Step S330: Write the equations in step S320 into the following matrix:

[0126] (9)

[0127] Among them, , ; similarly, the matrix forms of B1, B2, F, and G can be determined. Among them, , . B1 and B2 are corresponding coefficient matrices, and the specific parameters of this matrix need to be determined by the finite element form of the Laplacian operator on the corresponding grid. For triangular meshes, the Laplacian operator acts on a variable u, , where is the abbreviation of ∇ 2 , M is called the mass matrix, and L is the Laplacian matrix.

[0128]

[0129] where θ ij and are the two interior angles opposite the line segment ij in the two triangles respectively. The mass matrix M is a diagonal matrix related to the area of the finite element triangle where each grid point is located

[0130] ,

[0131] where, , which is to find the area of the triangular finite element adjacent to i, and use the cross product of the mapping coordinates to obtain it. At this time, the coefficients of each term in formula (9) can be determined and satisfy the form of Ax = b, so it can be solved.

[0132] Step S340: Use an optimization method to solve the matrix in step S330 to obtain U t and V t .

[0133] The general optimization methods in this field can be used for solving, and this application does not limit the specific optimization method.

[0134] In one embodiment, in order to simulate the dynamic process in space-time, a nonlinear reaction-diffusion model is adopted. Specifically, in step S310, the Fitzhugh-Nagumo model is adopted, and the functions f and g are changed (specific forms are given to the functions f and g) to obtain:

[0135] (10)

[0136] (11)

[0137] Equations (10) and (11) correspond to functions f and g respectively. Among them, C1 and C2 are parameters related to the curve shape of function f, 1 / b is the average time constant for the slow current to pass through the ion channel, a is a parameter related to the resting voltage, d is the ratio of the transmembrane current to the magnitude of the slow local recovery current in the ion channel. These parameters a, b, C1, C2, and d are all related to the specific ion channel model, and their actual values need to be determined by the shape of the action potential curve of the model. For the functions f and g obtained according to the above Equations (10) and (11), the diffusion equations of variables u and v shown in Equations (5) and (6) satisfy:

[0138] .

[0139] In an embodiment using the Stewart ion channel model, the correlation coefficients can be set as follows: D1 = 1, D2 = 0, a = 0.13, b = 0.026, C1 = 0.52, C2 = 0.1, d = 1.

[0140] As Figure 1 shown, in some embodiments, the cardiac electrophysiological simulation system 100 of the present application may further include a fiber helix calculation module 160.

[0141] As Figure 1 shown, in some embodiments, the three-dimensional mesh reconstruction module 120 of the cardiac electrophysiological simulation system 100 of the present application is further configured to mark ablation points on multiple chest images and obtain a three-dimensional cardiac mesh by means of the finite element method. The ablation points include estimated ablation points to be ablated and / or actual ablation points that have been ablated. It should be noted that the three-dimensional cardiac mesh includes the ventricular three-dimensional mesh described above. In addition, the cardiac electrophysiological simulation system 100 of the present application further includes a fiber helix calculation module 160, which is configured to calculate the myocardial fiber helix according to the three-dimensional cardiac mesh to obtain the direction vector of the myocardial fiber helix corresponding to each second grid point on the three-dimensional cardiac mesh. In these embodiments, the electrophysiological parameter simulation module 140 is further configured to simulate the cardiac action potential excitation process according to the geometric position of the second grid point, the fiber helix corresponding to the second grid point, and the electrophysiological parameters. Among them, the electrophysiological parameters include cell-level parameters and tissue-level parameters. The cell-level parameters include the current parameters of the ion channel, and the tissue-level parameters include the conductivity of different cardiac regions, the pacing stimulation parameters, and the propagation rate of the electrical signal set according to the direction vector of the myocardial fiber helix. The three-dimensional mesh reconstruction module 120, the fiber helix calculation module 160, and the electrophysiological parameter simulation module 140 can be specifically implemented as the three-dimensional mesh reconstruction module, the fiber helix calculation module, and the electrophysiological parameter simulation module in Patent CN119028528A. Therefore, the relevant content can be referred to the content recorded in Patent CN119028528A and will not be elaborated here.

[0142] In an embodiment of the present application, the electrophysiological parameter simulation module 140 obtains the current parameters of the ion channels corresponding to each first grid point on the ventricular conduction network based on the first ion channel model, and obtains the current parameters of the ion channels corresponding to each second grid point on the three-dimensional cardiac grid based on the second ion channel model, and the first ion channel model and the second ion channel model are different.

[0143] In some embodiments, the first grid points and the second grid points share a grid point cloud, the ends of the ventricular conduction network are connected to the ends of the three-dimensional cardiac grid, and electrical signals can be conducted from the ventricular conduction network to the three-dimensional cardiac grid. The grid point cloud may include coordinate data corresponding to all coordinate positions of the entire heart. The first grid points and the second grid points may share the grid point cloud, while the corresponding parameters are different. Among them, the three-dimensional cardiac grid is used to simulate ordinary myocardial cells, and the parameters corresponding to the second grid points are the relevant parameters of ordinary myocardial cells; the ventricular conduction network is used to simulate the His bundle and the Purkinje fiber network, and the parameters corresponding to the first grid points are the relevant parameters of the His bundle and the Purkinje fiber network. Among them, when the pacing stimulus is applied via the His bundle, the electrical signal can be conducted from the end of the ventricular conduction network to the three-dimensional cardiac grid, so as to realize the coupled simulation of the two networks.

[0144] As Figure 1 shown, in some embodiments, the cardiac electrophysiological simulation system 100 of the present application further includes an evaluation module 170. The evaluation module 170 is used to give an evaluation result according to the dynamic simulation result. For example, evaluating a cardiac ablation plan, and the evaluation result includes whether to change the ablation point and performing simulation again using the electrophysiological parameter simulation module 140. The evaluation module 170 can be specifically implemented as the evaluation module in Patent CN119028528A. For the content recorded in Patent CN119028528A, reference can be made and will not be elaborated here.

[0145] The present application uses specific terms to describe the embodiments of the present application. Such as "an embodiment", "one embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the present application can be combined appropriately.

[0146] Some aspects of the present application may be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above-mentioned hardware or software may be referred to as a "data block", "module", "engine", "unit", "component", or "system". The processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. In addition, aspects of the present application may be embodied as a computer product located on one or more computer-readable media, which includes computer-readable program code. For example, the computer-readable media may include, but is not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes...), optical disks (e.g., compact disk CDs, digital versatile disks DVDs...), smart cards, and flash memory devices (e.g., cards, sticks, key drives...).

[0147] The computer-readable media may contain a propagated data signal having computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take many forms, including electromagnetic, optical, or the like, or any suitable combination thereof. The computer-readable media may be any computer-readable media other than a computer-readable storage media, which can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The program code located on the computer-readable media may be propagated through any appropriate medium, including radio, cable, fiber optic cable, radio frequency signal, or similar media, or any combination of the above media.

[0148] Similarly, it should be noted that, in order to simplify the description of the present application disclosure and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present application, sometimes multiple features are incorporated into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the object of the present application are more than the features mentioned. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0149] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used in the description of embodiments are, in some examples, modified by the modifiers "about", "approximately", or "substantially". Unless otherwise specified, "about", "approximately", or "substantially" indicate that the said numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in this application are all approximate values, and such approximate values may change according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this application to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

Claims

1. A cardiac electrophysiology simulation system, characterized in that, Comprising: An image acquisition module for acquiring multiple thoracic cavity images, wherein the thoracic cavity images contain ventricular images; A three-dimensional mesh reconstruction module for obtaining a ventricular three-dimensional mesh by means of the finite element method based on the multiple thoracic cavity images; A ventricular conduction network construction module for generating a ventricular conduction network based on the ventricular three-dimensional mesh to simulate the conduction of cardiac action potentials in the ventricles, wherein the ventricular conduction network includes the His bundle and the Purkinje fiber network, and the ventricular conduction network includes multiple first grid points, and the geometric positions of each first grid point share the point cloud of the ventricular three-dimensional mesh; An electrophysiological parameter simulation module for simulating the conduction process of cardiac action potentials in the ventricles according to the geometric positions and electrophysiological parameters of the first grid points, wherein the electrophysiological parameters include cell-level parameters and tissue-level parameters, the cell-level parameters include current parameters of ion channels, and the tissue-level parameters include conductivity, electrical signal propagation rate, and pacing stimulation parameters; and A dynamic simulation module for simulating cardiac electrical signals according to the conduction process of cardiac action potentials in the ventricles to obtain a dynamic simulation result; Wherein, the ventricular conduction network construction module is configured to simulate and generate the ventricular conduction network by using the following method: Forming a two-dimensional fractal network by using a fractal method; Projecting the two-dimensional fractal network onto the endocardial surface to generate a three-dimensional fractal network; and Using the three-dimensional fractal network as the ventricular conduction network.

2. The cardiac electrophysiological simulation system according to claim 1, wherein The endocardial surface is a non-smooth surface.

3. The cardiac electrophysiological simulation system according to claim 2, characterized in that, The forming of the two-dimensional fractal network by using a fractal method includes: Creating an initial node and an initial branch of the two-dimensional fractal network by using a fractal method, and the initial branch has an initial growth direction; Creating multiple new branches from the end of the initial branch, and iteratively creating the next-generation new branches from the new branches to generate the two-dimensional fractal network, wherein, during the iteration process, fractal motion parameters are set according to the geometric structure of the ventricle, and the fractal motion parameters include: branch length, branch angle, and repulsion parameter.

4. The cardiac electrophysiological simulation system according to claim 3, wherein, Each branch includes n line segments and n + 1 nodes, and the n + 1 nodes repel each other to generate a curve, wherein the direction vector of each node is determined by the direction vector of the previous node and the distance gradient, and n is a natural number.

5. The cardiac electrophysiological simulation system according to claim 4, wherein The projecting of the two-dimensional fractal network onto the endocardial surface to generate a three-dimensional fractal network includes: Obtaining the vertex normal of the current node, and the vertex normal is the weighted sum of all normals of the adjacent fractal grids of the current node; Projecting the current node onto the surface defined by the fractal grid through the vertex normal; Judging whether the projection result of the current node is located within the fractal grid; If the projection result of the current node is not located within the fractal grid, projecting the projection result onto the surface through the surface normal of the grid cell.

6. The cardiac electrophysiological simulation system according to claim 5, wherein The projecting of the projection result onto the surface through the surface normal of the grid cell includes: Calculating the direction vectors on all k - 1 nodes before the current kth node, where k is a positive integer; Project the k-th node onto the surface according to the direction vector and the branch segment lengths of all k - 1 nodes; If the projected k-th node is within the fractal grid or the node segment length is greater than or equal to a preset range, continue to project the next node; otherwise, stop the iterative process.

7. The cardiac electrophysiological simulation system according to claim 6, wherein The distance gradient is calculated using the following formula :[[]]END]] ， where ϵ is a preset constant, e i represents the Cartesian basis vector, k represents the serial number of the current node on the ventricular conduction network, i represents a certain component x, y, z of the three-dimensional coordinates, g represents the distance gradient, p k represents the line segment length of the k-th node on the ventricular three-dimensional grid, dist cp represents a function that returns the distance from node k to the nearest node in the network.

8. The cardiac electrophysiological simulation system according to claim 7, wherein, Use the following formula to calculate the direction vector d of the k-th node k :[[-END]] ， where w represents the repulsion parameter of the fractal structure, and d k-1 represents the direction vector of the (k - 1)-th node, and represents the distance gradient of the (k - 1)-th node; Project the k-th node onto the surface according to the following formula based on the direction vector d k and the branch segment lengths of all k - 1 nodes 。 9. The cardiac electrophysiological simulation system according to claim 7, wherein Initialize the direction vector using the following formula: ， Among them, d 0 represents the initial direction vector of the initial node, and n 0 represents the initial normal vector of the initial node, and α represents the single-step change angle of the branch angle.

10. The cardiac electrophysiological simulation system according to claim 3, characterized in that, The fractal method includes the k-d tree method.

11. The cardiac electrophysiological simulation system according to claim 1, wherein The pacing stimulation parameters correspond to pacing stimulation, and the pacing stimulation includes a stimulation signal applied to the His bundle.

12. The cardiac electrophysiological simulation system according to claim 1, wherein The three-dimensional grid reconstruction module is further configured to label ablation points on the multiple chest images and obtain a cardiac three-dimensional grid by means of the finite element method. The ablation points include estimated ablation points to be ablated and / or actual ablation points that have been ablated; The cardiac electrophysiological simulation system further includes: a fiber helix calculation module for calculating the myocardial fiber helix according to the cardiac three-dimensional grid to obtain the direction vector of the myocardial fiber helix corresponding to each second grid point on the cardiac three-dimensional grid; The electrophysiological parameter simulation module is further configured to simulate the cardiac action potential excitation process according to the geometric position of the second grid point, the fiber helix corresponding to the second grid point, and the electrophysiological parameters. Among them, the electrophysiological parameters include cell-level parameters and tissue-level parameters. The cell-level parameters include the current parameters of ion channels, and the tissue-level parameters include the conductivity of different cardiac regions, pacing stimulation parameters, and the propagation rate of electrical signals set according to the direction vector of the myocardial fiber helix.

13. The cardiac electrophysiological simulation system according to claim 12, wherein, The electrophysiological parameter simulation module obtains the current parameters of the ion channels corresponding to each first grid point on the ventricular conduction network based on the first ion channel model, and obtains the current parameters of the ion channels corresponding to each second grid point on the cardiac three-dimensional grid based on the second ion channel model. The first ion channel model and the second ion channel model are different.

14. The cardiac electrophysiological simulation system according to claim 13, wherein The first grid point and the second grid point share the point cloud of the ventricular three-dimensional grid. The end of the ventricular conduction network is connected to the end of the cardiac three-dimensional grid, and electrical signals can be conducted from the ventricular conduction network to the cardiac three-dimensional grid.

Images