A magnetic field simulation method based on electromechanical feedback of a real whole heart model

Abstract

This invention provides a magnetic field simulation method based on electromechanical feedback from a realistic whole-heart model. The method includes creating a realistic whole-heart-trunk model incorporating various cardiac tissue structures; performing electrophysiological modeling based on a single-domain reaction-diffusion model and setting relevant parameters; coupling the electrophysiological model with fiber orientation and principal stress models to solve for transmembrane potential and mechanical contraction; and calculating the body surface magnetic field signal based on the transmembrane potential, combined with current and magnetic field modules. This invention can provide more realistic and accurate predictions of body surface magnetic field signals, contributing to improved accuracy and efficiency in the diagnosis of cardiac diseases.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and more specifically, to a magnetic field simulation method based on electromechanical feedback from a real whole-heart model. Background Technology

[0002] There is an urgent need in the existing fields of cardiac electrophysiology and magnetophysiology for integrated simulation techniques of cardiac electrical and mechanical activity. Traditional cardiac electrophysiological models often neglect the mechanical properties of the heart, while mechanical models alone cannot reflect the influence of cardiac electrical activity. This separate research approach limits the ability to comprehensively understand cardiac function, especially in the development of diagnostic and treatment strategies for cardiac diseases. The interaction between cardiac electrical and mechanical activity, i.e., electromechanical feedback, is a key factor in cardiac function, but it has not been adequately simulated and analyzed in previous studies.

[0003] The development of cardiac magnetic field simulation technology, as a non-invasive diagnostic tool, is limited by existing technologies. Traditional simulation methods often rely on simplified cardiac models that cannot accurately reflect the complex anatomical structure and electrophysiological characteristics of the heart. Furthermore, these methods typically do not consider the anisotropy and mechanical stress of cardiac tissue when simulating cardiac electrical activity, leading to significant deviations between simulation results and actual cardiac activity. These limitations affect the accuracy and reliability of magnetic field simulation technology in clinical applications.

[0004] In the process of implementing the embodiments of the present invention, the inventors discovered that the prior art has at least the following problems or defects: the existing cardiac simulation methods cannot fully capture the complex interaction between cardiac electrophysiological and mechanical characteristics, resulting in limited accuracy and practicality of simulation results, especially in the diagnosis and treatment planning of heart diseases. Summary of the Invention

[0005] This invention provides a magnetic field simulation method based on electromechanical feedback from a real whole-heart model, comprising:

[0006] Step 1: Create a real whole heart-trunk model, which includes one or more of the following tissue structures: sinoatrial node (SAN), right atrium (RA), left atrium (LA), fossa ovalis (F0), Bachmann bundle (BB), atrioventricular node (AVN), right ventricle (RV), left ventricle (LV), right ventricular endocardial wall (ER), and left ventricular posterior basal region (PL).

[0007] Step 2: Based on the single-domain reaction-diffusion model combined with the dimensional ion current equation induced by the traction tensor, electromechanical feedback electrophysiological modeling of the real whole heart model is performed, and relevant parameters are set for different tissue structures of the heart. The relevant parameters include equation parameters, initial potential and boundary conditions.

[0008] Step 3: Couple the electromechanical feedback electrophysiological model constructed in Step 2 with the fiber orientation model and principal stress model to solve the transmembrane potential and mechanical contraction of the heart;

[0009] Step 4: Based on the transmembrane potential of the heart obtained in Step 3, and in conjunction with the current module and the magnetic field module, calculate and solve the surface magnetic field signal.

[0010] Furthermore, the specific steps for step 1 are as follows:

[0011] (1) Using the target heart image, the heart is divided into multiple regular-shaped myocardial blocks according to the anatomical structure. The heart geometric model is constructed by splicing them together. The left atrium, right atrium and ventricle are discrete and not connected to each other, forming three different solution domains.

[0012] (2) Construct a realistic trunk geometry model. The target trunk is scanned and imaged by target tomography or magnetic resonance imaging. The trunk geometry model is obtained by segmentation and reconstruction according to grayscale threshold. The trunk geometry model includes homogeneous and heterogeneous trunk volume conductor models. For the heterogeneous volume conductor model, the volume conductors such as lungs, liver, blood pool and bones will be assigned different electrical conductivities respectively.

[0013] (3) Integrate the heart geometry model and the trunk geometry model into a single unit.

[0014] Furthermore, step 2 includes the following operations:

[0015] (1) A single-domain reaction-diffusion model was constructed based on the two-domain model, dividing the cell into two regions: intracellular and extracellular. Let J i J represents intracellular current density. e σ represents the extracellular current density. i σ represents intracellular conductivity. e V represents extracellular conductivity. i V represents the intracellular electrical potential. e Represents extracellular potential, I represents the gradient operator. m This represents the total transmembrane current across the cell membrane, according to Ohm's law.

[0016]

[0017] and current conservation

[0018]

[0019] Formula (3) is derived:

[0020]

[0021] Let V mTo represent the transmembrane potential across the cell membrane, we obtain formula (4):

[0022] V m =V i -V e (4);

[0023] The cell membrane is simulated as a parallel structure of a resistor and a capacitor, let I... ion C represents the transmembrane ion current across the cell membrane. m Let t represent the membrane capacitance per unit area of ​​the cell membrane, and t represent time. Based on the corresponding physical principles, formula (5) is obtained:

[0024]

[0025] Transmembrane ion current I on the cell membrane ion Purely electric component I excited by excitation e and the traction tensor-induced ion channel opening part I f The composition can be expressed as formula (6):

[0026] I ion =I e +I f (6);

[0027] Considering only I e Then, by simultaneously solving equations (3), (4), and (5), we obtain equation (7):

[0028]

[0029] (2) The Fitzhugh-Nagumo (FHN) model was used to simulate cardiac electrophysiological activity, and its expression is given by formula (8):

[0030]

[0031] Where u represents the transmembrane potential, v represents the recovery variable, and D represents the diffusion coefficient. Let represent the gradient operator, c1 and c2 be used to control the amplitude of ion current excitation and refractory period, a, b and d be intermediate parameters, and t represent time; based on this, Rogers and McCulloch modified the model to obtain formula (9):

[0032]

[0033] Combining the two-domain model with the modified FHN model, for the sinoatrial node (SAN), its ion current I... e The expression for the restored variable v is shown in equation (10):

[0034]

[0035] For other subdomains of the heart, the ion current I e The expression for the restored variable v is shown in equation (11):

[0036]

[0037] Where k, c1, c2, A, B, and e are parameters that regulate the shape of the cell action potential curve, k represents the time scale of the model, c1 and c2 control the amplitude of ion current excitation and refractory period, affecting the time of action potential rise and fall, B represents membrane potential, A controls the amplitude of action potential, e controls the duration of action potential, and v represents the recovery variable, controlling the cell refractory period.

[0038] (3) In the field of electrophysiology, dimensional mapping is used to establish the transmembrane potential V of the myocardium. m The relationship between the dimensionless potential φ and the activation time t and dimensionless time τ is expressed by formula (12):

[0039]

[0040] Where β φ δ φ and β t The selection of parameters ensures that a physiological action potential response of -80 to +20 mV and a characteristic action potential duration can be obtained; in the electromechanical feedback calculation modeling, the stretch-induced current generation I in the transmembrane ion current on the cell membrane is considered. f Through the dimensionless mapping formula of ion flux (13):

[0041]

[0042] in It is a dimensionless mapping of ion current, where φ is the dimensionless potential, λ is the tension along the fiber direction, and G... s It is the maximum conductivity of the traction tensor-induced channel, φ s It is the resting potential of the traction tensor-induced ion channel. It is a switching function; feedback is enabled when λ>1, otherwise disabled; based on the current induced by the dimensionless traction tensor. Dimensional ion current I was obtained f The formula is (14):

[0043]

[0044] Combining formulas (6) and (14) with formulas (10) and (11) respectively, we obtain formula (15):

[0045]

[0046] Using formula (16)

[0047]

[0048] Calculate the transmembrane potential V in the myocardium m .

[0049] Furthermore, step 3 includes the following operations:

[0050] (1) The myocardium is simulated as an anisotropic hyperelastic material with three preferred directions: fiber direction a, sheet direction s, and perpendicular to the sheet direction n. Considering that the fiber tilt angle of the myocardial cells gradually undergoes transmural deformation from 60° in the endocardium to -60° in the epicardium, the fiber angle at the myocardial boundary is determined according to formula (17):

[0051]

[0052] Where, θ epi θ represents the inclination angle of the epicardial fibers. end Indicates the endocardial fiber tilt angle. Indicates the maximum fiber tilt angle of the epicardium. Indicates the maximum endocardial fiber tilt angle. This represents the Z-axis coordinate at the apex of the left ventricle; based on the fiber distance D to the epicardium. epi Fiber distance D from the endocardium ond , through formula (18)

[0053]

[0054] The dimensionless distance β is calculated, representing the fiber distance to the epicardial boundary; the fiber angle θ passing through the heart wall is calculated according to formula (19) θ=βθ end +(1-β)θ epi Linear change; the fiber distance to the endocardium and epicardium is obtained by solving the wall distance equation, the slice direction s is obtained by using the curve coordinate interface, and then the curve coordinate system is rotated around the slice direction to obtain the fiber direction a;

[0055] (2) Given that the myocardium is mainly composed of layered sheet-like myocardial cells (fibers) arranged in a preferred direction, the fiber orientation has a great influence on the mechanical and electrical properties of the tissue. The heart tissue is simulated as an anisotropic hyperelastic material. The tissue contraction in the hyperelastic model is achieved through the additive decomposition of the stress tensor. This additional stress changes with voltage, and the expression is equation (20):

[0056]

[0057] Where k represents the saturated active stress, ε(V) is the rate transformation function, and φ rRepresenting the resting potential, the specific expression for ε(V) is ε(V)=ε0+(ε0-ε1)exp(-exp(-ζ(V)). m -φ i ))), where ε0 and ε1 represent contraction rate constants, ζ represents the conversion rate, and φ i This indicates a phase shift; principal stresses are added to the second Piola-Kirchhoff stress in different percentages along the fiber, sheet, and normal, as expressed by equation (22):

[0058] Where S and S0 represent stress, and a0, s0, and n0 represent fiber, sheet, and direction perpendicular to sheet, respectively.

[0059] Furthermore, step 4 includes the following operations:

[0060] Using formula (23) Calculate the applied current density J in the heart e Where σ represents cardiac conductivity, V represents the gradient operator. m This represents the membrane voltage calculated instantaneously during the discrete-time diffusion of excitation within the heart; expressed by formula (24) J. t =J e +J c (24) represents the total current density of the torso, J. t J c This represents the conduction current; according to Ampere's law, use formula (25). In calculating the total current density of the torso J t The surface magnetic field is calculated at each instant, where H represents the magnetic field strength.

[0061] Furthermore, in formula (1), J i J is the intracellular current density. e σ is the extracellular current density. i σ represents intracellular conductivity. e V represents extracellular conductivity. i V is the intracellular potential. e extracellular potential, For gradient operators, they satisfy the following conditions: and The relationship is used to describe the physical relationship between current density inside and outside the cell and conductivity, electric potential, and gradient operator.

[0062] Furthermore, in formula (2), Im represents the total transmembrane current across the cell membrane, J i J is the intracellular current density. e extracellular current density, For gradient operators, they satisfy the following conditions: The relationship is used to describe the relationship between the total transmembrane current on the cell membrane and the current density inside and outside the cell, based on the law of conservation of current.

[0063] Furthermore, in formula (3) I m σ is the total transmembrane current across the cell membrane. i σ represents intracellular conductivity. e V represents intracellular conductivity. i V is the intracellular potential. e extracellular potential, For gradient operators, they satisfy the following conditions: The relationship between the total transmembrane current on the cell membrane and the intracellular and extracellular conductivity and potential is based on Ohm's law and the law of conservation of current.

[0064] Furthermore, in formula (4) V m V is the transmembrane potential on the cell membrane. i V is the intracellular potential. e The extracellular potentials are such that they satisfy V. m =V i -V e The relationship is used to represent the difference between the transmembrane potential on the cell membrane and the potential inside and outside the cell.

[0065] Furthermore, in formula (5), C m V is the membrane capacitance per unit area of ​​the cell membrane. m I is the transmembrane potential on the cell membrane. ion I is the transmembrane ion current across the cell membrane. m Let be the total transmembrane current across the cell membrane, and t be time. They satisfy the following relationship: The formula, based on a physical model of a capacitor and a resistor in parallel, describes the dynamic relationship between transmembrane ion current, transmembrane potential, total transmembrane current, and time on the cell membrane. This represents the rate of change of transmembrane potential with respect to time.

[0066] The embodiments of the present invention have at least the following beneficial effects: By creating a realistic whole-heart-trunk model including various cardiac tissue structures and combining it with a single-domain response diffusion model for electrophysiological modeling, the present invention can accurately simulate the electrophysiological activity and mechanical contraction of the heart. This method can provide more realistic and accurate predictions of surface magnetic field signals, helping to improve the accuracy and efficiency of heart disease diagnosis. Furthermore, by coupling the electrophysiological model with fiber orientation and principal stress models, the present invention can more comprehensively reflect the electromechanical feedback mechanism of the heart, thereby providing deeper insights into the research and treatment of heart diseases.

[0067] The magnetic field simulation method of this invention improves the accuracy of magnetic field simulation by comprehensively considering the electrophysiological and mechanical characteristics of the heart. This method can simulate not only the electrical activity of the heart but also its mechanical contractions, making the simulation results closer to actual cardiac activity. This comprehensive simulation capability can provide more reliable data support for the diagnosis and treatment of heart diseases, contributing to the development of new treatment strategies and improved treatment outcomes. Attached Figure Description

[0068] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein:

[0069] Figure 1 This is a flowchart illustrating a magnetic field simulation method based on electromechanical feedback from a real whole-heart model, provided as an embodiment of the present invention. Detailed Implementation

[0070] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0071] Those skilled in the art will understand that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0072] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0073] The following is for reference. Figure 1 , Figure 1 This is a flowchart illustrating a magnetic field simulation method based on electromechanical feedback from a real whole-heart model, provided in an embodiment of the present invention. Figure 1 As shown, a magnetic field simulation method 100 based on electromechanical feedback from a real whole-heart model includes:

[0074] Step 1: Create a real whole heart-trunk model, which includes one or more of the following tissue structures: sinoatrial node (SAN), right atrium (RA), left atrium (LA), fossa ovalis (FO), Bachmann bundle (BB), atrioventricular node (AVN), right ventricle (RV), left ventricle (LV), right ventricular endocardial wall (ER), and left ventricular posterior basal region (PL).

[0075] Step 2: Based on the single-domain reaction-diffusion model combined with the dimensional ion current equation induced by the traction tensor, electromechanical feedback electrophysiological modeling of the real whole heart model is performed, and relevant parameters are set for different tissue structures of the heart. The relevant parameters include equation parameters, initial potential and boundary conditions.

[0076] Step 3: Couple the electromechanical feedback electrophysiological model constructed in Step 2 with the fiber orientation model and principal stress model to solve the transmembrane potential and mechanical contraction of the heart;

[0077] Step 4: Based on the transmembrane potential of the heart obtained in Step 3, and in conjunction with the current module and the magnetic field module, calculate and solve the surface magnetic field signal.

[0078] It should be noted that this embodiment relates to a magnetic field simulation method based on electromechanical feedback from a realistic whole-heart model. This method first involves creating a realistic whole-heart-trunk model that includes various cardiac tissue structures. In this model, the realistic whole-heart model refers to a detailed three-dimensional geometric model constructed according to the actual anatomical structure of the heart, including but not limited to key tissues such as the sinoatrial node, atria, ventricles, fossa ovalis, Bachmann bundle, and atrioventricular node. Such a model provides an accurate anatomical basis for subsequent electrophysiological modeling.

[0079] Specifically, the process of constructing a realistic whole-heart-torso model utilizes images of the target heart, such as CT or MRI scans. Based on anatomical structure, the heart is divided into multiple regularly shaped myocardial blocks, and these blocks are then stitched together to construct the heart's geometric model. In this process, the left atrium, right atrium, and ventricle are discretized, forming three distinct solution domains. Simultaneously, a realistic torso geometric model is constructed, including homogeneous and heterogeneous torso volume conductor models. In the heterogeneous volume conductor model, components such as the lungs, liver, blood pool, and bones are assigned different electrical conductivities.

[0080] Preferably, to further improve the accuracy of the model, more advanced image processing techniques can be employed to increase the resolution of the heart and trunk geometric models. For example, advanced image segmentation techniques can be used to more accurately distinguish different tissue structures of the heart and trunk.

[0081] Furthermore, when constructing a heterogeneous torso volume conductor model, more detailed conductivity data can be used to reflect the actual conductivity characteristics of different tissues. These improvements can make the simulation results closer to the real electrophysiological and mechanical behavior of the heart.

[0082] In some embodiments, step 1 is performed as follows:

[0083] (1) Using the target heart image, the heart is divided into multiple regular-shaped myocardial blocks according to the anatomical structure. The heart geometric model is constructed by splicing them together. The left atrium, right atrium and ventricle are discrete and not connected to each other, forming three different solution domains.

[0084] (2) Construct a realistic trunk geometry model. The target trunk is scanned and imaged by target tomography or magnetic resonance imaging. The trunk geometry model is obtained by segmentation and reconstruction according to grayscale threshold. The trunk geometry model includes homogeneous and heterogeneous trunk volume conductor models. For the heterogeneous volume conductor model, the volume conductors such as lungs, liver, blood pool and bones will be assigned different electrical conductivities respectively.

[0085] (3) Integrate the heart geometry model and the trunk geometry model into a single unit.

[0086] It should be noted that this implementation details the specific operational steps for creating a realistic whole-heart-trunk model. This includes using a target heart image, dividing the heart into multiple regularly shaped myocardial blocks based on anatomical structure, and constructing a geometric model of the heart by stitching them together. In this process, myocardial blocks refer to the various parts of the heart, which are divided into regular geometric shapes to facilitate mathematical modeling and computation. The left atrium, right atrium, and ventricle are discretized, forming three distinct solution domains. This means that these heart parts are treated as independent regions in the model, each with its own electrophysiological properties and boundary conditions.

[0087] Specifically, when constructing a cardiac geometric model, various image processing techniques can be used to extract myocardial blocks from cardiac images. For example, threshold segmentation can be used to distinguish between myocardium and blood, then morphological operations can be used to clean the image, and finally, 3D reconstruction techniques can be used to construct the geometric model of the myocardial blocks. When constructing a trunk geometric model, the target trunk can be scanned and imaged using target tomography or magnetic resonance imaging, and then the trunk geometric model can be obtained by grayscale threshold segmentation and reconstruction.

[0088] More specifically, this model can include homogeneous and heterogeneous trunk volume conductor models, in which volume conductors such as lungs, liver, blood pool, and bones in the heterogeneous volume conductor model will be assigned different conductivities to reflect their different roles in electrophysiological activities.

[0089] Preferably, to improve the accuracy and usability of the model, more advanced image processing and reconstruction techniques can be employed. For example, deep learning algorithms can be used to improve the segmentation accuracy of myocardial blocks, or high-resolution imaging techniques can be used to obtain more detailed information about the heart and trunk structures.

[0090] Furthermore, when assigning different tissue conductivities, these values ​​can be based on experimental data or literature to ensure the physiological rationality of the model. These optimization measures can make the constructed model more closely resemble the electrophysiological characteristics of the real heart and torso, thereby improving the accuracy of magnetic field simulation.

[0091] In some embodiments, step 2 includes the following operations:

[0092] (1) A single-domain reaction-diffusion model was constructed based on the two-domain model, dividing the cell into two regions: intracellular and extracellular. Let J i J represents intracellular current density. e σ represents the extracellular current density. i σ represents intracellular conductivity. e V represents extracellular conductivity. i V represents the intracellular electrical potential. e Represents extracellular potential, I represents the gradient operator. m This represents the total transmembrane current across the cell membrane, according to Ohm's law.

[0093]

[0094] and current conservation

[0095]

[0096] Formula (3) is derived:

[0097]

[0098] Let V m To represent the transmembrane potential across the cell membrane, we obtain formula (4):

[0099] V m =V i -V e (4);

[0100] The cell membrane is simulated as a parallel structure of a resistor and a capacitor, let I... ion C represents the transmembrane ion current across the cell membrane. m Let t represent the membrane capacitance per unit area of ​​the cell membrane, and t represent time. Based on the corresponding physical principles, formula (5) is obtained:

[0101]

[0102] Transmembrane ion current I on the cell membrane ion Purely electric component I excited by excitation e and the traction tensor-induced ion channel opening part I f The composition can be expressed as formula (6):

[0103] I ion =I e +I f (6);

[0104] Considering only I e Then, by simultaneously solving equations (3), (4), and (5), we obtain equation (7):

[0105]

[0106] (2) The Fitzhugh-Nagumo (FHN) model was used to simulate cardiac electrophysiological activity, and its expression is given by formula (8):

[0107]

[0108] Where u represents the transmembrane potential, v represents the recovery variable, and D represents the diffusion coefficient. Let represent the gradient operator, c1 and c2 be used to control the amplitude of ion current excitation and refractory period, a, b and d be intermediate parameters, and t represent time; based on this, Rogers and McCulloch modified the model to obtain formula (9):

[0109]

[0110] Combining the two-domain model with the modified FHN model, for the sinoatrial node (SAN), its ion current I... e The expression for the restored variable v is shown in equation (10):

[0111]

[0112] For other subdomains of the heart, the ion current I e The expression for the restored variable v is shown in equation (11):

[0113]

[0114] Where k, c1, c2, A, B, and e are parameters that regulate the shape of the cell action potential curve, k represents the time scale of the model, c1 and c2 control the amplitude of ion current excitation and refractory period, affecting the time of action potential rise and fall, B represents membrane potential, A controls the amplitude of action potential, e controls the duration of action potential, and v represents the recovery variable, controlling the cell refractory period.

[0115] (3) In the field of electrophysiology, dimensional mapping is used to establish the transmembrane potential of myocardium. V The relationship between m and dimensionless potential φ, and between activation time t and dimensionless time τ, is expressed by formula (12):

[0116]

[0117] Where β φ δ φ and β t The selection of parameters ensures that a physiological action potential response of -80 to +20 mV and a characteristic action potential duration can be obtained; in the electromechanical feedback calculation modeling, the stretch-induced current generation I in the transmembrane ion current on the cell membrane is considered. f Through the dimensionless mapping formula of ion current (13):

[0118]

[0119] in, It is a dimensionless mapping of ion current, where φ is the dimensionless potential, λ is the tension along the fiber direction, and G... s It is the maximum conductivity of the traction tensor-induced channel, φ s It is the resting potential of the traction tensor-induced ion channel. It is a switching function; feedback is enabled when λ > 1, and disabled otherwise; based on the current induced by the dimensionless traction tensor. Dimensional ion current I was obtained f The formula is (14):

[0120]

[0121] Combining formulas (6) and (14) with formulas (10) and (11) respectively, we obtain formula (15):

[0122]

[0123] Using formula (16)

[0124]

[0125] Calculate the transmembrane potential V in the myocardium m .

[0126] It should be noted that this implementation involves the process of constructing a single-domain response-diffusion model based on a two-domain model, dividing the cell into intracellular and extracellular regions. Here, a two-domain model refers to an electrophysiological model that considers both intracellular and extracellular regions simultaneously, while a single-domain response-diffusion model refers to merging these two regions into a single continuous domain to describe cardiac electrophysiological activity. Intracellular current density and extracellular current density refer to the current flow inside and outside the cell, respectively.

[0127] Specifically, in formula (3) involved in the implementation method, the intracellular conductivity σ in and extracellular conductivity σ out These represent the electrical conductivity inside and outside the cell, respectively; the intracellular potential V. in and extracellular potential V out Represent the electrical potential energy inside and outside the cell, respectively, and the gradient operator. Used to describe the spatial variation of electric potential.

[0128] More specifically, the settings of these parameters need to be based on experimental data and theoretical models of cardiac electrophysiology to ensure the accuracy and reliability of the models. For example, conductivity can be set based on the ion channel characteristics and cell membrane permeability of cardiomyocytes, while potential can be determined based on the action potential of cardiomyocytes.

[0129] Furthermore, the heterogeneity of cardiac tissue, such as the different types and distributions of cardiomyocytes, and their impact on electrophysiological properties, can be considered. Dynamic changes in the cardiac electrophysiological model can also be incorporated, such as variations in potential and current during the cardiac cycle, and their effects on transmembrane potential. These refined operational procedures and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulations.

[0130] In some embodiments, step 3 includes the following operations:

[0131] (1) The myocardium is simulated as an anisotropic hyperelastic material with three preferred directions: fiber direction a, sheet direction s, and perpendicular to the sheet direction n. Considering that the fiber tilt angle of the myocardial cells gradually undergoes transmural deformation from 60° in the endocardium to -60° in the epicardium, the fiber angle at the myocardial boundary is calculated according to formula (17):

[0132]

[0133] Determine, where θ epi θ represents the inclination angle of the epicardial fibers. ond Indicates the endocardial fiber tilt angle. Indicates the maximum fiber tilt angle of the epicardium. Indicates the maximum endocardial fiber tilt angle. This represents the Z-axis coordinate at the apex of the left ventricle; based on the fiber distance D to the epicardium. epi Fiber distance D from the endocardium ond , through formula (18)

[0134]

[0135] The dimensionless distance β is calculated, representing the fiber distance to the epicardial boundary; the fiber angle θ passing through the heart wall is calculated according to formula (19) θ=βθ end +(1-β)θ epi Linear change; the fiber distance to the endocardium and epicardium is obtained by solving the wall distance equation, the slice direction s is obtained by using the curve coordinate interface, and then the curve coordinate system is rotated around the slice direction to obtain the fiber direction a;

[0136] (2) Given that the myocardium is mainly composed of layered sheet-like myocardial cells (fibers) arranged in a preferred direction, the fiber orientation has a great influence on the mechanical and electrical properties of the tissue. The heart tissue is simulated as an anisotropic hyperelastic material. The tissue contraction in the hyperelastic model is achieved through the additive decomposition of the stress tensor. This additional stress changes with voltage, and the expression is equation (20):

[0137]

[0138] Where k represents the saturated active stress, ε(V) is the rate transformation function, and φ r Representing the resting potential, the specific expression for ε(V) is ε(V)=ε0+(ε0-ε1)exp(-exp(-ζ(V)). m -φ i ))), where ε0 and ε1 represent contraction rate constants, ζ represents the conversion rate, and φ i This indicates a phase shift; principal stresses are added to the second Piola-Kirchhoff stress in different percentages along the fiber, sheet, and normal, as expressed by equation (22):

[0139] Where S and S0 represent stress, and a0, s0, and n0 represent fiber, sheet, and direction perpendicular to sheet, respectively.

[0140] It should be noted that this implementation details the process of coupling the electromechanical feedback electrophysiological model with the fiber orientation model and the principal stress model to solve for the transmembrane potential and mechanical contraction of the heart. In this context, coupling refers to connecting the outputs of two or more models as inputs to simulate their interactions. The fiber orientation model describes the orientation of myocardial fibers, while the principal stress model deals with the main stress distribution experienced by the myocardium during contraction.

[0141] Specifically, the coupling process first involves using the transmembrane potential calculated in the electrophysiological model as input, which is then passed to the fiber orientation model and the principal stress model. The fiber orientation model adjusts the potential distribution according to the orientation of the myocardial fibers, while the principal stress model adjusts the potential and stress distribution according to the mechanical properties of the myocardium.

[0142] More specifically, the parameters in these models, such as the conductivity, capacitance, and ion channel characteristics of the myocardium, need to be set based on experimental data or physiological parameters from the literature. Furthermore, parameters such as the myocardial fiber tilt angle, maximum fiber tilt angle, and axial coordinates at the ventricular apex also need to be determined based on the specific anatomical structure.

[0143] Preferably, to more accurately simulate the electromechanical feedback of the heart, the finite element method can be used to solve the coupled model. For example, the transmembrane potential and mechanical contraction of the heart can be solved, which provides more detailed spatial resolution and more accurate stress distribution.

[0144] Furthermore, the dynamic characteristics of the myocardium can be considered, such as changes in potential and stress during myocardial contraction and relaxation, and the impact of these changes on cardiac function. The electromechanical feedback characteristics under different cardiac disease states can also be explored to improve the clinical application value of the model. These refined operational procedures and alternatives can make the simulation results more closely resemble the actual physiological and pathological state of the heart.

[0145] In some embodiments, step 4 includes the following operations:

[0146] Using formula (23) Calculate the applied current density J in the heart e Where σ represents cardiac conductivity, V represents the gradient operator. m This represents the membrane voltage calculated instantaneously during the discrete-time diffusion of excitation within the heart; expressed by formula (24) J. t =J e +J c (24) represents the total current density of the torso, J. t J c This represents the conduction current; according to Ampere's law, use formula (25). In calculating the total current density of the torso J t The surface magnetic field is calculated at each instant, where H represents the magnetic field strength.

[0147] It should be noted that this embodiment describes the specific steps for calculating the body surface magnetic field signal based on the cardiac transmembrane potential. Here, the body surface magnetic field signal refers to the distribution of the magnetic field generated by the electrical activity of the heart on the body surface, which can be measured in a non-invasive manner. The current module and magnetic field module refer to the mathematical models or calculation tools used to calculate the current distribution and the resulting magnetic field.

[0148] Specifically, the implementation first utilizes the transmembrane potential of the heart, combined with a current module and a magnetic field module, to calculate the surface magnetic field signal. In this process, the transmembrane potential refers to the potential difference between the inside and outside of cardiomyocytes, which is the basis of cardiac electrical activity. The current module simulates the flow of current within the heart, while the magnetic field module calculates the corresponding magnetic field distribution based on the results from the current module.

[0149] More specifically, the parameter settings in these modules need to be based on the characteristics of cardiac electrophysiology, such as myocardial conductivity, membrane capacitance, and ion channel characteristics. These parameters are usually derived from experimental data or physiological literature.

[0150] Furthermore, dynamic changes in the heart, such as variations in potential and current during the cardiac cycle, and their impact on magnetic field signals, can be considered. Advanced image processing techniques, such as cardiac model reconstruction based on MRI or CT scans, can also be introduced to improve the spatial resolution of transmembrane potential calculations. These refined operational procedures and alternatives can make the calculation of surface magnetic field signals more accurate, thereby improving the accuracy of cardiac disease diagnosis.

[0151] In some embodiments, J in formula (1) i J is the intracellular current density. e σ is the extracellular current density. i σ represents intracellular conductivity. e V represents extracellular conductivity. i V is the intracellular potential. e extracellular potential, For gradient operators, they satisfy the following conditions: and The relationship is used to describe the physical relationship between current density inside and outside the cell and conductivity, electric potential, and gradient operator.

[0152] It should be noted that this embodiment describes in detail the physical relationships between intracellular and extracellular current density, conductivity, electric potential, and the gradient operator. Here, intracellular and extracellular current density refers to the current flow inside and outside the cell, while conductivity is a measure of a material's ability to conduct electricity. Electric potential refers to the potential energy at a point in an electric field, and the gradient operator is a mathematical operator used to describe the spatial rate of change of electric potential.

[0153] Specifically, in formula (1) involved in the implementation method, the intracellular current density (J) in ) and extracellular current density (J out The terms σ and σ represent the current flow inside and outside the cell, respectively, and the intracellular conductivity (σ) represents the intracellular conductivity (σ). in ) and extracellular conductivity (σ out This describes the electrical conductivity between the inside and outside of a cell. Intracellular potential (V) in ) and extracellular potential (V out ) represent the electrical potential energy inside and outside the cell, respectively, while the gradient operator These parameters are used to describe the spatial variation of electrical potential. The settings of these parameters need to be based on experimental data and theoretical models of cardiac electrophysiology to ensure the accuracy and reliability of the model.

[0154] Preferably, to more accurately simulate cardiac electrophysiological activity, a more detailed biophysical model can be used to describe the current density and conductivity inside and outside the cell. For example, an ion channel model of the cell membrane can be introduced, taking into account the conductivity and opening probability of different ion channels.

[0155] Furthermore, numerical methods, such as the finite element method, can be used to solve the equations describing the current density and potential inside and outside cells, providing a more accurate spatial resolution and thus simulating cardiac electrophysiological activity. The heterogeneity of cardiac tissue, such as the different types and distributions of cardiomyocytes and their impact on electrophysiological properties, can also be considered. These refined operational steps and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulation.

[0156] In some embodiments, I in formula (2) m J is the total transmembrane current across the cell membrane. i J is the intracellular current density. e extracellular current density, For gradient operators, they satisfy the following conditions: The relationship is used to describe the relationship between the total transmembrane current on the cell membrane and the current density inside and outside the cell, based on the law of conservation of current.

[0157] It should be noted that this embodiment describes the relationship between the total transmembrane current on the cell membrane and the current density inside and outside the cell, and interprets it based on the law of conservation of current. Here, the total transmembrane current on the cell membrane refers to the total amount of current passing through the cell membrane, while the law of conservation of current states that the total amount of current flowing into and out of any node in a circuit must be equal. The current density inside and outside the cell refers to the current distribution inside and outside the cell, respectively.

[0158] Specifically, in formula (2) involved in the implementation method, the total transmembrane current i on the cell membrane trans With intracellular current density J inand extracellular current density J out The relationship between the two currents satisfies the law of conservation of current. Transmembrane current is the integral of current density across the cell membrane, and its magnitude is determined by multiple factors. Transmembrane current is driven by the electric field across the membrane, which in turn drives ion flow through ion channels on the membrane. Specifically, transmembrane current is the result of the combined effects of membrane conductivity, the potential difference across the membrane, and the number and characteristics of ion channels. In practice, transmembrane current can be calculated using parameters such as membrane conductivity and the potential difference across the membrane, based on relevant physical formulas and physiological models. The specific parameter settings need to be determined according to the electrophysiological characteristics of the heart. For example, the conductivity and potential difference across the cell can be obtained through experimental measurements or theoretical calculations, and then the current density can be calculated.

[0159] Furthermore, the heterogeneity of cardiac tissue, such as the different types and distributions of cardiomyocytes, and their impact on electrophysiological properties, can be considered. Dynamic changes in the cardiac electrophysiological model can also be incorporated, such as variations in potential and current during the cardiac cycle, and their effects on transmembrane currents. These refined operational procedures and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulations.

[0160] In some embodiments, I in formula (3) m σ is the total transmembrane current across the cell membrane. i σ represents intracellular conductivity. e V represents intracellular conductivity. i V is the intracellular potential. e extracellular potential, For gradient operators, they satisfy the following conditions: The relationship between the total transmembrane current on the cell membrane and the intracellular and extracellular conductivity and potential is based on Ohm's law and the law of conservation of current.

[0161] It should be noted that this embodiment details the relationship between the total transmembrane current across the cell membrane and the conductivity and potential inside and outside the cell, based on Ohm's law and the law of conservation of current. Here, Ohm's law refers to the relationship between the current flowing through a conductor and the voltage across the conductor and the conductor's resistance, while the law of conservation of current states that in a closed system, the total inflow of current must equal the total outflow. The total transmembrane current across the cell membrane refers to the total current flowing through the cell membrane, while the conductivity and potential inside and outside the cell refer to the conductivity and potential inside and outside the cell, respectively.

[0162] Specifically, in the formulas involved in the implementation, the total transmembrane current I on the cell membrane... trans With intracellular and extracellular conductivity σ in , σ out and electric potential V in Vout The relationships between these parameters are described. The settings of these parameters need to be based on experimental data and theoretical models of cardiac electrophysiology to ensure the accuracy and reliability of the models. For example, intracellular and extracellular conductivity can be set based on the ion channel characteristics and cell membrane permeability of cardiomyocytes, while electrical potential can be determined based on the action potential of cardiomyocytes.

[0163] Preferably, to more accurately simulate cardiac electrophysiological activity, a more detailed biophysical model can be used to describe the conductivity and potential inside and outside the cell. For example, an ion channel model of the cell membrane can be introduced, considering the conductivity and opening probability of different ion channels.

[0164] Furthermore, the heterogeneity of cardiac tissue, such as the different types and distributions of cardiomyocytes, and their impact on electrophysiological properties, can be considered. These refined operational procedures and alternatives can make cardiac electrophysiological models more accurate, thereby improving the accuracy of magnetic field simulations.

[0165] In some embodiments, V in formula (4) m V is the transmembrane potential on the cell membrane. i V is the intracellular potential. e The extracellular potentials are such that they satisfy V. m =V i -V e The relationship is used to represent the difference between the transmembrane potential on the cell membrane and the potential inside and outside the cell.

[0166] It should be noted that this embodiment describes the relationship between the transmembrane potential and the potential difference across the cell membrane. Here, transmembrane potential refers to the potential difference across the cell membrane, a crucial physiological parameter as it is directly related to cell excitability and the transmission of electrical signals. Changes in transmembrane potential are fundamental to the generation and propagation of action potentials in cardiomyocytes.

[0167] Specifically, in the formulas involved in the implementation method, the transmembrane potential V on the cell membrane... m Defined as intracellular potential V in With extracellular potential V out The difference between these parameters. In practical applications, the settings of these parameters need to be based on experimental data and theoretical models of cardiac electrophysiology. For example, the intracellular potential can be set based on the potential of cardiomyocytes in the resting state, while the extracellular potential is usually regarded as a reference point, close to zero. The measurement and calculation of transmembrane potential are crucial for understanding and simulating cardiac electrical activity.

[0168] Furthermore, the heterogeneity of cardiac tissue, such as the different types and distributions of cardiomyocytes, and their impact on electrophysiological properties, can be considered. Dynamic changes in the cardiac electrophysiological model can also be introduced, such as potential variations during the cardiac cycle and their effects on transmembrane potential. These refined operational procedures and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulations.

[0169] In some embodiments, Cm in formula (5) is the membrane capacitance per unit area of ​​the cell membrane, V m I is the transmembrane potential on the cell membrane. ion I is the transmembrane ion current across the cell membrane. m Let be the total transmembrane current across the cell membrane, and t be time. They satisfy the following relationship: The formula, based on a physical model of a capacitor and a resistor in parallel, describes the dynamic relationship between transmembrane ion current, transmembrane potential, total transmembrane current, and time on the cell membrane. This represents the rate of change of transmembrane potential with respect to time.

[0170] It should be noted that this embodiment describes the dynamic relationship between transmembrane ion current, transmembrane potential, total transmembrane current, and time on the cell membrane. Here, transmembrane ion current refers to the flow of ions through the cell membrane, which is the basis of cellular electrophysiological activity. Transmembrane potential refers to the potential difference across the cell membrane, while total transmembrane current includes all currents passing through the cell membrane. Time here refers to the changes of these currents and potentials over time.

[0171] Specifically, the formulas involved in the implementation methods describe the transmembrane potential V on the cell membrane. m The rate of change of transmembrane ion current I ion and total transmembrane current I total The relationship between the two. In this model, the cell membrane is simulated as a parallel structure of a resistor and a capacitor, where the membrane capacitance C... m The membrane resistance R represents the cell membrane's ability to store electrical charge. m This indicates that the cell membrane hinders the flow of electric current.

[0172] More specifically, the settings of these parameters need to be based on experimental data and theoretical models of cardiac electrophysiology to ensure the accuracy and reliability of the models. For example, membrane capacitance can be calculated based on the surface area and dielectric constant of the cell membrane, while membrane resistance can be determined based on the permeability and thickness of the cell membrane.

[0173] Preferably, to more accurately simulate cardiac electrophysiological activity, more detailed biophysical models can be used to describe transmembrane ion currents and transmembrane potentials on the cell membrane. For example, more complex ion channel models can be introduced, taking into account the kinetic characteristics and interactions of different ion channels.

[0174] The various embodiments of the present invention have the following beneficial effects: The magnetic field simulation method based on electromechanical feedback of a real whole-heart model described in this invention can achieve accurate simulation of cardiac electrophysiological and mechanical activities. This method constructs a real whole-heart-trunk model containing various cardiac tissue structures and combines it with a single-domain reaction-diffusion model for electrophysiological modeling. This allows for precise setting of relevant parameters, thereby providing detailed descriptions of the electrophysiological characteristics of different cardiac tissue structures. Furthermore, by coupling the electrophysiological model with fiber orientation and principal stress models, the transmembrane potential and mechanical contraction of the heart can be solved, and the surface magnetic field signal can be calculated based on these results, providing more accurate data support for the diagnosis and treatment of heart diseases.

[0175] Furthermore, this method improves the accuracy and reliability of magnetic field simulation by comprehensively considering the electrophysiological and mechanical properties of the heart. It can simulate not only the electrical activity of the heart but also its mechanical contractions, making the simulation results closer to actual cardiac activity. This comprehensive simulation capability can provide profound insights into the study of heart diseases, contributing to the development of new treatment strategies and improved treatment outcomes. Particularly in the fields of cardiac electrophysiology and magnetophysiology research, it can provide a novel, non-invasive diagnostic tool.

[0176] Furthermore, the storage medium in the embodiments of this application stores program instructions capable of implementing all the above methods. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0177] The above description is merely a selection of preferred embodiments of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A magnetic field simulation method based on electromechanical feedback from a real whole-heart model, characterized in that, Includes the following steps: Step 1: Create a real whole heart-trunk model, which includes one or more of the following tissue structures: sinoatrial node (SAN), right atrium (RA), left atrium (LA), fossa ovalis (FO), Bachmann bundle (BB), atrioventricular node (AVN), right ventricle (RV), left ventricle (LV), right ventricular endocardial wall (ER), and left ventricular posterior basal region (PL). Step 2: Based on the single-domain reaction-diffusion model combined with the dimensional ion current equation induced by the traction tensor, electromechanical feedback electrophysiological modeling of the real whole heart model is performed, and relevant parameters are set for different tissue structures of the heart. The relevant parameters include equation parameters, initial potential and boundary conditions. Step 3: Couple the electromechanical feedback electrophysiological model constructed in Step 2 with the fiber orientation model and principal stress model to solve the transmembrane potential and mechanical contraction of the heart; Step 4: Based on the transmembrane potential of the heart obtained in Step 3, and combining the current module and the magnetic field module, calculate and solve for the surface magnetic field signal. The specific operations in step 3 are as follows: The myocardium is simulated as an anisotropic hyperelastic material with three preferred directions: fiber direction a, sheet direction s, and direction perpendicular to the sheet direction n. Considering that the fiber tilt angle of the myocardial cells gradually undergoes transmural deformation from 60° in the endocardium to -60° in the epicardium, the fiber angle at the myocardial boundary is determined according to formula (17): Where, θ epi θ represents the inclination angle of the epicardial fibers. end Indicates the endocardial fiber tilt angle. Indicates the maximum fiber tilt angle of the epicardium. Indicates the maximum endocardial fiber tilt angle. This represents the Z-axis coordinate at the apex of the left ventricle; based on the fiber distance D to the epicardium. epi Fiber distance D from the endocardium end The dimensionless distance β is calculated using formula (18); The dimensionless distance β represents the fiber distance to the epicardial boundary; the fiber angle θ passing through the heart wall is determined by formula (19) θ=βθ end +(1-β)θ epi Linear change; The fiber distances to the endocardium and epicardium are obtained by solving the wall distance equation. The sheet direction s is obtained using the curve coordinate interface. The fiber direction a is then obtained by rotating the curve coordinate system around the sheet direction. The heart tissue is simulated as an anisotropic hyperelastic material. The tissue contraction in the hyperelastic model is achieved through the additive decomposition of the stress tensor. The additional stress varies with the voltage, and the expression is equation (20): Where k represents the saturated active stress, ε(V) is the rate transformation function, and φ r Representing the resting potential, the specific expression for ε(V) is ε(V)=ε0+(ε0-ε1)exp(-exp(-ζ(V)). m -φ i ))), where ε0 and ε1 represent the contraction rate constants, ζ represents the conversion rate, and φ i Indicates phase shift; The principal stresses are added to the second Piola-Kirchhoff stress in different percentages along the fiber, sheet, and normal, as expressed by equation (22): Where S and S0 represent stress, and a0, s0, and n0 represent fiber, sheet, and direction perpendicular to sheet, respectively.

2. The magnetic field simulation method based on electromechanical feedback of a real whole heart model according to claim 1, characterized in that, Step 1 is performed as follows: Using the target heart image, the heart is divided into multiple regularly shaped myocardial blocks according to the anatomical structure. The geometric model of the heart is constructed by stitching them together. The left atrium, right atrium and ventricle are discrete and not connected to each other, forming three different solution domains. A realistic torso geometric model is constructed by scanning the target torso with a target tomographic scan or magnetic resonance imaging, and reconstructing the torso geometric model according to grayscale threshold segmentation. The torso geometric model includes homogeneous and heterogeneous torso volume conductor models. For the heterogeneous volume conductor model, the volume conductors such as lungs, liver, blood pool and bones will be assigned different electrical conductivities. The heart geometry model and the torso geometry model are integrated into a single unit.

3. The magnetic field simulation method based on electromechanical feedback of a real whole-heart model according to claim 1, characterized in that, Step 2 includes the following operations: A single-domain reaction-diffusion model was constructed based on the two-domain model, dividing the cell into intracellular and extracellular regions, and letting J... i J represents intracellular current density. e σ represents the extracellular current density, and σ represents the intracellular conductivity. e V represents extracellular conductivity. i V represents the intracellular electrical potential. e Represents extracellular potential, I represents the gradient operator. m The total transmembrane current across the cell membrane is represented by formula (3) derived from Ohm's law (1) and the current conservation formula (2): Let V m To represent the transmembrane potential across the cell membrane, we obtain formula (4): V m =V i -V e (4); The cell membrane is simulated as a parallel structure of a resistor and a capacitor, let I... ion C represents the transmembrane ion current across the cell membrane. m Let t represent the membrane capacitance per unit area of ​​the cell membrane, and t represent time. Based on the corresponding physical principles, formula (5) is obtained: Transmembrane ion current I on the cell membrane ion Purely electric component I excited by excitation e and the traction tensor-induced ion channel opening part I f The composition can be expressed as formula (6): I ion =I e +I f (6); Considering only I e Then, by simultaneously solving equations (3), (4), and (5), we obtain equation (7): The Fitzhugh-Nagumo model was used to simulate cardiac electrophysiological activity, and its expression is given by formula (8): Where u represents the transmembrane potential, v represents the recovery variable, and D represents the diffusion coefficient. Let represent the gradient operator, c1 and c2 be used to control the amplitude of ion current excitation and refractory period, a, b and d be intermediate parameters, and t represent time; based on this, Rogers and McCulloch modified the model to obtain formula (9): Combining the two-domain model with the modified FHN model, for the sinoatrial node (SAN), its ion current I... e The expression for the restored variable v is shown in equation (10): For other subdomains of the heart, the ion current I e The expression for the restored variable v is shown in equation (11): Where k, c1, c2, A, B, and e are parameters that regulate the shape of the cell action potential curve, k represents the time scale of the model, c1 and c2 control the amplitude of ion current excitation and refractory period, affecting the time of action potential rise and fall, B represents membrane potential, A controls the amplitude of action potential, e controls the duration of action potential, and v represents the recovery variable, controlling the cell refractory period. The transmembrane potential V of the myocardium was established using dimensional mapping. m The relationship between the dimensionless potential φ and the activation time t and dimensionless time τ is expressed by formula (12): Where β φ δ φ and β t The selection of parameters ensures that a physiological action potential response of -80 to +20 mV and a characteristic action potential duration can be obtained; in the electromechanical feedback calculation modeling, the stretch-induced current generation I in the transmembrane ion current on the cell membrane is considered. f Through the dimensionless mapping formula of ion current (13): in, It is a dimensionless mapping of ion current, where φ is the dimensionless potential, λ is the tension along the fiber direction, and G... s It is the maximum conductivity of the traction tensor-induced channel, φ s It is the resting potential of the traction tensor-induced ion channel. It is a switching function; feedback is enabled when λ>1, otherwise disabled; based on the current induced by the dimensionless traction tensor. Dimensional ion current I was obtained f The formula is (14): Combining formulas (6) and (14) with formulas (10) and (11) respectively, we obtain formula (15): Using formula (16) Calculate the transmembrane potential V in the myocardium m .

4. The magnetic field simulation method based on electromechanical feedback of a real whole heart model according to claim 1, characterized in that, Step 4 includes the following operations: Using formula (23) Calculate the applied current density J in the heart e Where σ represents cardiac conductivity, V represents the gradient operator. m This represents the membrane voltage calculated instantaneously during the discrete-time diffusion of excitation within the heart. The total current density J of the torso is expressed by formula (24). t , J t *J e +J c (24) Among them, J c This represents the conduction current; according to Ampere's law, use formula (25). In calculating the total current density of the torso J t The surface magnetic field is calculated at each instant, where H represents the magnetic field strength.

5. The magnetic field simulation method based on electromechanical feedback of a real whole-heart model according to claim 1, characterized in that, In formula (1), J i J is the intracellular current density. e σ is the extracellular current density. i σ represents intracellular conductivity. e V represents extracellular conductivity. i V is the intracellular electrical potential. e extracellular potential, For gradient operators; satisfy and 6. The magnetic field simulation method based on electromechanical feedback of a real whole-heart model according to claim 1, characterized in that, In formula (2) I m J is the total transmembrane current across the cell membrane. i J is the intracellular current density. e extracellular current density, For gradient operators; satisfy 7. The magnetic field simulation method based on electromechanical feedback of a real whole-heart model according to claim 1, characterized in that, In formula (3) I m σ is the total transmembrane current across the cell membrane. i σ represents intracellular conductivity. e V represents intracellular conductivity. i V is the intracellular electrical potential. e extracellular potential, For gradient operators; satisfy 8. The magnetic field simulation method based on electromechanical feedback of a real whole heart model according to claim 1, characterized in that, In formula (4) V m V is the transmembrane potential on the cell membrane. i V is the intracellular electrical potential. e extracellular potential; Satisfy V m =V i -V e .

9. The magnetic field simulation method based on electromechanical feedback of a real whole heart model according to claim 3, characterized in that, In formula (5), C m V is the membrane capacitance per unit area of ​​the cell membrane. m I is the transmembrane potential on the cell membrane. ion I is the transmembrane ion current across the cell membrane. m t represents the total transmembrane current across the cell membrane, and time is t. satisfy in, This represents the rate of change of transmembrane potential with respect to time.

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