Magnetic field simulation method based on electromechanical feedback of real whole heart model

Abstract

The invention provides a magnetic field simulation method based on electromechanical feedback of a real whole heart model. The method comprises the following steps: creating a real whole heart-trunk model containing various tissue structures of the heart; performing electrophysiology modeling based on the single-domain reaction diffusion model, and setting related parameters; coupling the electrophysiological model with the fiber direction and principal stress model, and solving transmembrane potential and mechanical shrinkage; and calculating a body surface magnetic field signal according to the transmembrane potential in combination with the current and magnetic field module. According to the invention, more real and accurate body surface magnetic field signal prediction can be provided, and the accuracy and efficiency of heart disease diagnosis can be improved.

Description

Technical Field

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

[0002] In the existing field of cardiac electrophysiology and magnetophysiology research, there is an urgent need for comprehensive simulation technology of cardiac electrical and mechanical activities. Traditional cardiac electrophysiology models often ignore the mechanical properties of the heart, while the mechanical model alone cannot reflect the impact of cardiac electrical activity. This separate research method limits the ability to fully understand cardiac function, especially in the diagnosis of heart disease and the formulation of treatment strategies. The interaction between cardiac electrical and mechanical activity, i.e., electromechanical feedback, is a key factor in cardiac function, but it has not been fully simulated and analyzed in previous studies.

[0003] As a non-invasive diagnostic tool, the development of cardiac magnetic field simulation technology is limited by existing technologies. Traditional simulation methods often rely on simplified heart models that cannot accurately reflect the complex anatomical structure and electrophysiological characteristics of the heart. In addition, these methods usually do not consider the anisotropy and mechanical stress of cardiac tissue when simulating cardiac electrical activity, resulting in a large deviation between the 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 found that there are at least the following problems or defects in the prior art: the existing cardiac simulation methods are unable to fully capture the complex interactions between the electrophysiological and mechanical properties of the heart, resulting in limited accuracy and practicality of the simulation results, especially in the diagnosis and treatment planning of heart disease. Summary of the invention

[0005] The present invention provides a magnetic field simulation method based on electromechanical feedback of a real whole heart model, comprising:

[0006] Step 1: creating a real whole heart-trunk model, wherein the real whole heart model includes one or more tissue structures of sinoatrial node SAN, right atrium RA, left atrium LA, fossa ovalis F0, Bachmann's bundle BB, atrioventricular node AVN, right ventricle RV, left ventricle LV, right ventricular endocardial wall ER and left ventricular posterior basal area PL;

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

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

[0009] Step 4: Based on the cardiac transmembrane potential obtained in step 3, the current module and the magnetic field module are combined to calculate and solve the body surface magnetic field signal.

[0010] Furthermore, the specific operations of step 1 are as follows:

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

[0012] (2) constructing a realistic trunk geometry model, scanning and imaging the target trunk through target tomography or magnetic resonance imaging, and reconstructing the trunk geometry model according to grayscale threshold segmentation. The trunk geometry model includes homogeneous and inhomogeneous trunk volume conductor models. For the inhomogeneous volume conductor model, the volume conductors such as the lungs, liver, blood pool, and bones will be assigned different conductivities respectively;

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

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

[0015] (1) Based on the dual-domain model, a single-domain reaction-diffusion model is constructed, in which the cell is divided into two regions: intracellular and extracellular. i represents the intracellular current density, J e represents the extracellular current density, σ i represents the intracellular conductivity, σ e represents the extracellular conductivity, V i represents the intracellular potential, V e represents the extracellular potential, represents the gradient operator, I m Represents the total transmembrane current on the cell membrane, according to Ohm's law

[0016]

[0017] and current conservation

[0018]

[0019] Formula (3) is obtained:

[0020]

[0021] Let V mRepresents the transmembrane potential on the cell membrane, and the formula (4) is obtained:

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

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

[0024]

[0025] Ion current across the cell membrane ion Purely electric part I inspired by excitement e and tension tensor-induced ion channel opening part I f Composition can be expressed as formula (6):

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

[0027] Consider only I e When , formula (3), (4) and (5) are combined to obtain formula (7):

[0028]

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

[0030]

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

[0032]

[0033] Combining the dual-domain model with the modified FHN model, for the sinoatrial node (SAN), its ionic current I e The expression of the recovery variable v is shown in formula (10):

[0034]

[0035] For other subdomains of the heart, the ionic current I e The expression of the recovery variable v is shown in formula (11):

[0036]

[0037] Among them, k, c1, c2, A, B, and e are parameters that regulate the shape of the cell action potential curve, respectively. 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 action potential amplitude, e represents the control of action potential duration, and v represents the recovery variable, which controls the cell refractory period.

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

[0039]

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

[0041]

[0042] in is the dimensionless mapping of ion flow, φ is the dimensionless potential, λ is the tension in the fiber direction, G s is the maximum conductance of the channel induced by the pulling tensor, φ s is the resting potential of the pulling tensor-induced ion channel, is a switching function, which turns on the feedback when λ>1 and turns off otherwise; according to the current induced by the dimensionless traction tensor Get the dimensionless ion current I f , formula (14):

[0043]

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

[0045]

[0046] Using formula (16)

[0047]

[0048] Calculation of transmembrane potential V in cardiac muscle 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 myocardial cell fiber inclination angle gradually changes from 60° in the endocardium to -60° in the epicardium, the fiber angle on the myocardial boundary is determined according to formula (17):

[0051]

[0052] Among them, θ epi represents the epicardial fiber inclination angle, θ end represents the inclination angle of endocardial fibers, represents the maximum epicardial fiber inclination, represents the maximum fiber inclination of the endocardium, represents the Z-axis coordinate of the left ventricular apex; based on the fiber distance D to the epicardium epi Distance D between fibers and endocardium ond , through formula (18)

[0053]

[0054] The dimensionless distance β is calculated to represent the fiber distance to the epicardial border; the fiber angle θ passing through the heart wall is calculated according to the 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 slice direction s is obtained using the curvilinear coordinate interface, and the curvilinear coordinate system is rotated around the slice direction to obtain the fiber direction a;

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

[0056]

[0057] Where k represents the saturation active stress, ε(V) is the rate conversion function, and φ rRepresents the resting potential, and the specific expression of 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 represents the phase shift; the principal stress is added to the second Piola-Kirchhoff stress along the fiber, sheet and normal direction at different percentages, expressed as equation (22):

[0058] Where S and S0 represent stress, and a0, s0, and n0 represent the fiber, sheet, and perpendicular sheet directions, 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 the heart conductivity, represents the gradient operator, V m represents the membrane voltage calculated at discrete time instants during the diffusion of excitation in the heart; by formula (24)J t =J e +J c (24) represents the total current density J of the trunk t , where J c represents the conduction current; according to Ampere's law, using formula (25) In calculating the total trunk current density J t The body surface magnetic field at each moment is calculated, where H represents the magnetic field strength.

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

[0062] Furthermore, in formula (2), Im is the total transmembrane current on the cell membrane, J i is the intracellular current density, J e is the extracellular current density, is the gradient operator, and they satisfy It 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 is the total transmembrane current on the cell membrane, σ i is the intracellular conductivity, σ e is the intracellular conductivity, V i is the intracellular potential, V e is the extracellular potential, is the gradient operator, and they satisfy This relationship is based on Ohm's law and the law of conservation of current, and is used to describe the relationship between the total transmembrane current on the cell membrane and the conductivity and potential inside and outside the cell.

[0064] Furthermore, V in formula (4) m is the transmembrane potential on the cell membrane, V i is the intracellular potential, V e is the extracellular potential, and V m =V i -V e It is used to express 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 is the membrane capacitance per unit area of ​​the cell membrane, V m is the transmembrane potential on the cell membrane, I ion is the transmembrane ion current on the cell membrane, I m is the total transmembrane current on the cell membrane, t is the time, and they satisfy The formula is based on the physical model of a capacitor and a resistor in parallel, and describes the dynamic relationship between transmembrane ion current, transmembrane potential, total transmembrane current and time on the cell membrane. It represents the rate of change of transmembrane potential with time.

[0066] The above-mentioned embodiments of the present invention have at least the following beneficial effects: by creating a realistic whole heart-trunk model including various tissue structures of the heart, and combining the single-domain reaction-diffusion model for electrophysiological modeling, the present invention can accurately simulate the electrophysiological activity and mechanical contraction of the heart. This method can provide a more realistic and accurate prediction of surface magnetic field signals, which helps to improve the accuracy and efficiency of heart disease diagnosis. In addition, by coupling the electrophysiological model with the fiber direction model and the principal stress model, the present invention can more comprehensively reflect the electromechanical feedback mechanism of the heart, thereby providing deeper insights in the research and treatment of heart disease.

[0067] The magnetic field simulation method of the present invention can improve the accuracy of magnetic field simulation by comprehensively considering the electrophysiological characteristics and mechanical characteristics of the heart. This method can not only simulate the electrical activity of the heart, but also simulate the mechanical contraction of the heart, so that the simulation results are closer to the actual heart activity. This comprehensive simulation capability can provide more reliable data support for the diagnosis and treatment of heart diseases, and help develop new treatment strategies and improve treatment effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0069] Figure 1 A schematic flow chart of a magnetic field simulation method based on electromechanical feedback of a real whole heart model provided by an embodiment of the present invention. DETAILED DESCRIPTION

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

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

[0072] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0073] Reference below Figure 1 , Figure 1 FIG. 1 is a flow chart of a magnetic field simulation method based on electromechanical feedback of a real whole heart model provided by an embodiment of the present invention. Figure 1 As shown, a magnetic field simulation method 100 based on electromechanical feedback of a real whole heart model includes:

[0074] Step 1: creating a real whole heart-trunk model, wherein the real whole heart model includes one or more tissue structures of sinoatrial node SAN, right atrium RA, left atrium LA, fossa ovalis FO, Bachmann's bundle BB, atrioventricular node AVN, right ventricle RV, left ventricle LV, right ventricular endocardial wall ER and left ventricular posterior basal area PL;

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

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

[0077] Step 4: Based on the cardiac transmembrane potential obtained in step 3, the current module and the magnetic field module are combined to calculate and solve the body surface magnetic field signal.

[0078] It should be noted that this embodiment relates to a magnetic field simulation method based on electromechanical feedback of a real whole heart model, which first includes creating a real whole heart-trunk model containing multiple tissue structures of the heart. In this model, the real whole heart model refers to a detailed three-dimensional geometric model, which is constructed according to the anatomical structure of the actual heart, including but not limited to key tissues such as the sinoatrial node, atrium, ventricle, fossa ovalis, Bachmann's bundle, and atrioventricular node. Such a model can provide an accurate anatomical basis for subsequent electrophysiological modeling.

[0079] Specifically, in the process of building a realistic whole heart-torso model, target heart images, such as CT or MRI scan images, are used to divide the heart into multiple regularly shaped myocardial blocks according to the anatomical structure, and the geometric model of the heart is constructed by splicing these myocardial blocks. In this process, the left atrium, right atrium, and ventricle are discretely dispersed to form three different solution domains. At the same time, a realistic torso geometric model is also constructed, including homogeneous and inhomogeneous torso volume conductor models, in which the lungs, liver, blood pool, and bones in the inhomogeneous volume conductor model are given different conductivities.

[0080] Preferably, in order to further improve the accuracy of the model, more advanced image processing techniques can be used to improve the resolution of the geometric models of the heart and trunk. 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 non-homogeneous 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 actual electrophysiological and mechanical behavior of the heart.

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

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

[0084] (2) constructing a realistic trunk geometry model, scanning and imaging the target trunk through target tomography or magnetic resonance imaging, and reconstructing the trunk geometry model according to grayscale threshold segmentation. The trunk geometry model includes homogeneous and inhomogeneous trunk volume conductor models. For the inhomogeneous volume conductor model, the volume conductors such as the lungs, liver, blood pool, and bones will be assigned different conductivities respectively;

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

[0086] It should be noted that this embodiment describes in detail the specific operational steps for creating a realistic whole heart-torso model. This includes using a target heart image to divide the heart into multiple regularly shaped myocardial blocks according to the anatomical structure, and constructing a heart geometric model by splicing. In this process, myocardial blocks refer to various parts of the heart that are divided into regular geometric shapes for easier mathematical modeling and calculation. The left atrium, right atrium, and ventricle are discretely dispersed to form three different solution domains, which means that these parts of the heart are regarded as independent regions in the model, each with its own electrophysiological characteristics and boundary conditions.

[0087] Specifically, when constructing a heart geometric model, a variety of image processing techniques can be used to extract myocardial masses from a heart image. For example, a threshold segmentation technique can be used to distinguish myocardium from blood, and then morphological operations can be used to clean up the image, and finally a three-dimensional reconstruction technique can be used to construct a geometric model of the myocardial mass. When constructing a trunk geometric model, the target trunk can be scanned and imaged by target tomography or magnetic resonance imaging, and then the trunk geometric model can be obtained by grayscale threshold segmentation and reconstruction.

[0088] More specifically, the model may include homogeneous and non-homogeneous torso volume conductor models, wherein volume conductors such as lungs, liver, blood pool and bones in the non-homogeneous volume conductor model will be assigned different conductivities to reflect their different roles in electrophysiological activities.

[0089] Preferably, in order to improve the accuracy and practicality of the model, more advanced image processing and reconstruction techniques can be used. 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 heart and trunk structure information.

[0090] Furthermore, when assigning different tissue conductivity, it can be set based on experimental data or conductivity values ​​in the literature to ensure the physiological rationality of the model. These optimization measures can make the constructed model closer to the electrophysiological characteristics of the real heart and trunk, thereby improving the accuracy of the magnetic field simulation.

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

[0092] (1) Based on the dual-domain model, a single-domain reaction-diffusion model is constructed, in which the cell is divided into two regions: intracellular and extracellular. i represents the intracellular current density, J e represents the extracellular current density, σ i represents the intracellular conductivity, σ e represents the extracellular conductivity, V i represents the intracellular potential, V e represents the extracellular potential, represents the gradient operator, I m Represents the total transmembrane current on the cell membrane, according to Ohm's law

[0093]

[0094] and current conservation

[0095]

[0096] Formula (3) is obtained:

[0097]

[0098] Let V m Represents the transmembrane potential on the cell membrane, and the formula (4) is obtained:

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

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

[0101]

[0102] Ion current across the cell membrane ion Purely electric part I inspired by excitement e and tension tensor-induced ion channel opening part I f Composition can be expressed as formula (6):

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

[0104] Consider only I e When , formula (3), (4) and (5) are combined to obtain formula (7):

[0105]

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

[0107]

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

[0109]

[0110] Combining the dual-domain model with the modified FHN model, for the sinoatrial node (SAN), its ionic current I e The expression of the recovery variable v is shown in formula (10):

[0111]

[0112] For other subdomains of the heart, the ionic current I e The expression of the recovery variable v is shown in formula (11):

[0113]

[0114] Among them, k, c1, c2, A, B, and e are parameters that regulate the shape of the cell action potential curve, respectively. 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 action potential amplitude, e represents the control of action potential duration, and v represents the recovery variable, which controls the cell refractory period.

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

[0116]

[0117] where β φ , δ φ and β t The selection of ensures that the physiological action potential response and characteristic action potential duration of -80 to +20 mV can be obtained; when modeling the electromechanical feedback calculation, 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 flow (13):

[0118]

[0119] in, is the dimensionless mapping of ion flow, φ is the dimensionless potential, λ is the tension in the fiber direction, G s is the maximum conductance of the channel induced by the pulling tensor, φ s is the resting potential of the pulling tensor-induced ion channel, is a switching function, the feedback is turned on when λ>1, otherwise it is turned off; according to the current induced by the dimensionless traction tensor Get the dimensionless ion current I f , formula (14):

[0120]

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

[0122]

[0123] Using formula (16)

[0124]

[0125] Calculation of transmembrane potential V in cardiac muscle m .

[0126] It should be noted that this embodiment involves the process of constructing a single-domain reaction-diffusion model based on a dual-domain model, and the cell is divided into two regions, intracellular and extracellular. Here, the dual-domain model refers to an electrophysiological model that considers both the intracellular and extracellular regions, while the single-domain reaction-diffusion model refers to merging these two regions into a 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 the formula (3) involved in the implementation method, the intracellular conductivity σ in and the extracellular conductivity σ out Represent the conductivity of the cell inside and outside, the intracellular potential V in and the extracellular potential V out Respectively represent the potential energy inside and outside the cell, the gradient operator Used to describe the spatial variation of electric potential.

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

[0129] Furthermore, the heterogeneity of cardiac tissue can be considered, such as the different types and distribution of cardiomyocytes and their effects on electrophysiological properties. Dynamic changes in the cardiac electrophysiological model can also be introduced, such as changes in potential and current during the heartbeat cycle, and the effects of these changes on transmembrane potential. These refined operating steps and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulation.

[0130] In some embodiments, step 3 comprises 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 myocardial cell fiber inclination angle gradually changes from 60° in the endocardium to -60° in the epicardium, the fiber angle on the myocardial boundary is calculated according to formula (17):

[0132]

[0133] Determine, where θ epi represents the epicardial fiber inclination angle, θ ond represents the inclination angle of endocardial fibers, represents the maximum epicardial fiber inclination angle, represents the maximum fiber inclination of the endocardium, represents the Z-axis coordinate of the left ventricular apex; based on the fiber distance D to the epicardium epi Distance D between fibers and endocardium ond , through formula (18)

[0134]

[0135] The dimensionless distance β is calculated to represent the fiber distance to the epicardial border; the fiber angle θ passing through the heart wall is calculated according to the 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 slice direction s is obtained using the curvilinear coordinate interface, and the curvilinear coordinate system is rotated around the slice direction to obtain the fiber direction a;

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

[0137]

[0138] Where k represents the saturation active stress, ε(V) is the rate conversion function, and φ r Represents the resting potential, and the specific expression of 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 represents the phase shift; the principal stress is added to the second Piola-Kirchhoff stress along the fiber, sheet and normal direction at different percentages, expressed as equation (22):

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

[0140] It should be noted that this embodiment describes in detail the process of coupling the electromechanical feedback electrophysiological model with the fiber orientation model and the principal stress model to solve 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 the interaction between them. The fiber orientation model refers to a model that describes the orientation of myocardial fibers, while the principal stress model involves the main stress distribution experienced by the myocardium during contraction.

[0141] Specifically, the coupling process first involves passing the transmembrane potential calculated in the electrophysiological model as input 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 properties of the myocardium, need to be set based on experimental data or physiological parameters in the literature. In addition, parameters such as the fiber inclination, maximum fiber inclination, and axis coordinates at the apex of the ventricle also need to be determined based on the specific anatomical structure.

[0143] Preferably, in order to more accurately simulate the electromechanical feedback of the heart, a 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 can provide more detailed spatial resolution and more accurate stress distribution.

[0144] Furthermore, the dynamic characteristics of the myocardium can be considered, such as the 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 heart disease states can also be explored to improve the clinical application value of the model. These detailed operation steps and alternatives can make the simulation results closer to the actual physiological and pathological states 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 the heart conductivity, represents the gradient operator, V m represents the membrane voltage calculated at discrete time instants during the diffusion of excitation in the heart; by formula (24)J t =J e +J c (24) represents the total current density J of the trunk t , where J c represents the conduction current; according to Ampere's law, using formula (25) In calculating the total trunk current density J t The body surface magnetic field at each moment is calculated, where H represents the magnetic field strength.

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

[0148] Specifically, the implementation method first uses the transmembrane potential of the heart, combined with the current module and the 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 the myocardial cell, which is the basis of the heart's electrical activity. The current module is responsible for simulating the flow of current inside the heart, while the magnetic field module calculates the corresponding magnetic field distribution based on the results of the current module.

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

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

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

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

[0153] Specifically, in the formula (1) involved in the embodiment, the intracellular current density (J in ) and extracellular current density (J out ) represent the current flow inside and outside the cell, and the intracellular conductivity (σ in ) and extracellular conductivity (σ out ) describes the ability of a cell to conduct electricity inside and outside the cell. in ) and the extracellular potential (V out ) represent the potential energy inside and outside the cell, respectively, and the gradient operator Used to describe the spatial variation of electric potential. The setting of these parameters needs to be based on experimental data and theoretical models of cardiac electrophysiology to ensure the accuracy and reliability of the model.

[0154] Preferably, in order to simulate cardiac electrophysiological activity more accurately, 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 to consider the conductivity and open 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 the cells, which can provide more accurate spatial resolution and thus accurately simulate cardiac electrophysiological activity. The heterogeneity of cardiac tissue can also be considered, such as the different types and distribution of cardiomyocytes and their effects on electrophysiological properties. These refined operating steps and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulation.

[0156] In some embodiments, in formula (2), m is the total transmembrane current on the cell membrane, J i is the intracellular current density, J e is the extracellular current density, is the gradient operator, and they satisfy It 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 explains 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, and the law of conservation of current means that the total amount of current flowing into and out of any node in the 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 the formula (2) involved in the embodiment, the total transmembrane current i on the cell membrane is trans The intracellular current density J inand the extracellular current density J out The relationship between satisfies the law of conservation of current. The transmembrane current is the integral of the current density on the cell membrane, and its size is determined by multiple factors. The transmembrane current is driven by the electric field inside and outside the membrane to drive the flow of ions, and the flow of ions is achieved through the ion channels on the membrane. Specifically, the transmembrane current is the result of the combined action of the membrane's conductivity, the potential difference between the inside and outside, and the number and characteristics of the ion channels. In actual situations, the transmembrane current can be calculated based on parameters such as the membrane's conductivity and the potential difference between the two sides of the membrane, according to the corresponding physical formulas and physiological models. The specific setting of the parameters needs to be determined according to the characteristics of cardiac electrophysiology. For example, the conductivity and potential difference inside and outside the cell can be obtained by experimental measurement or theoretical calculation, and then the current density can be calculated.

[0159] Furthermore, the heterogeneity of cardiac tissue can be considered, such as the different types and distribution of cardiomyocytes, and their effects on electrophysiological properties. Dynamic changes in the cardiac electrophysiological model can also be introduced, such as changes in potential and current during the heartbeat cycle, and the effects of these changes on transmembrane current. These refined operating steps and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulation.

[0160] In some embodiments, in formula (3), m is the total transmembrane current on the cell membrane, σ i is the intracellular conductivity, σ e is the intracellular conductivity, V i is the intracellular potential, V e is the extracellular potential, is the gradient operator, and they satisfy This relationship is based on Ohm's law and the law of conservation of current, and is used to describe the relationship between the total transmembrane current on the cell membrane and the conductivity and potential inside and outside the cell.

[0161] It should be noted that this embodiment describes in detail the relationship between the total transmembrane current on 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 passing through a conductor and the voltage across the conductor and the resistance of the conductor, while the law of conservation of current means that in a closed system, the total inflow of current must be equal to the total outflow. The total transmembrane current on the cell membrane refers to the total amount of current passing 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 formula involved in the embodiment, the total transmembrane current I trans The conductivity σ in , σ out and potential V in , Vout The relationship between is described. The setting of these parameters needs to be based on experimental data and theoretical models of cardiac electrophysiology to ensure the accuracy and reliability of the model. For example, the intracellular and extracellular conductivity can be set according to the ion channel characteristics of cardiomyocytes and the permeability of cell membranes, while the potential can be determined according to the action potential of cardiomyocytes.

[0163] Preferably, in order to simulate cardiac electrophysiological activity more accurately, 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 to consider the conductivity and open probability of different ion channels.

[0164] Furthermore, the heterogeneity of cardiac tissue can be considered, such as the different types and distribution of cardiomyocytes and their effects on electrophysiological properties. These refined operation steps and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulation.

[0165] In some embodiments, V in formula (4) m is the transmembrane potential on the cell membrane, V i is the intracellular potential, V e is the extracellular potential, and V m =V i -V e It is used to express 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 difference between the transmembrane potential on the cell membrane and the potential inside and outside the cell. Here, the transmembrane potential refers to the potential difference between the two sides of the cell membrane, which is a key physiological parameter because it is directly related to the excitability of the cell and the transmission of electrical signals. The change of transmembrane potential is the basis for the generation and propagation of action potentials in cardiomyocytes.

[0167] Specifically, in the formula involved in the embodiment, the transmembrane potential V m Defined as the intracellular potential V in The extracellular potential V out In practical applications, the setting of these parameters needs to be based on experimental data and theoretical models of cardiac electrophysiology. For example, the intracellular potential can be set according to the potential of myocardial cells 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 is crucial to understanding and simulating cardiac electrical activity.

[0168] Furthermore, the heterogeneity of cardiac tissue can be considered, such as the different types and distribution of cardiomyocytes and their effects on electrophysiological properties. Dynamic changes in the cardiac electrophysiological model can also be introduced, such as changes in potential during the heartbeat cycle and the effects of these changes on transmembrane potential. These refined operation steps and alternatives can make the cardiac electrophysiological model more accurate, thereby improving the accuracy of magnetic field simulation.

[0169] In some embodiments, Cm in formula (5) is the membrane capacitance per unit area of ​​the cell membrane, V m is the transmembrane potential on the cell membrane, I ion is the transmembrane ion current on the cell membrane, I m is the total transmembrane current on the cell membrane, t is the time, and they satisfy The formula is based on the physical model of a capacitor and a resistor in parallel, and describes the dynamic relationship between transmembrane ion current, transmembrane potential, total transmembrane current and time on the cell membrane. It represents the rate of change of transmembrane potential with 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 cell electrophysiological activity. Transmembrane potential refers to the potential difference between the two sides of the cell membrane, and total transmembrane current includes all currents passing through the cell membrane. Time here refers to the change of these currents and potentials over time.

[0171] Specifically, the formula involved in the embodiment describes the transmembrane potential V on the cell membrane m The rate of change of transmembrane ion current I ion and the total transmembrane current I total In this model, the cell membrane is simulated as a parallel structure of a resistor and a capacitor, where the membrane capacitance C m Represents the ability of the cell membrane to store charge, while the membrane resistance R m Represents the cell membrane's resistance to the flow of electric current.

[0172] More specifically, the setting of these parameters needs to be based on experimental data and theoretical models of cardiac electrophysiology to ensure the accuracy and reliability of the model. 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, in order to simulate cardiac electrophysiological activity more accurately, a more detailed biophysical model can be used to describe the transmembrane ion current and transmembrane potential on the cell membrane. For example, a more complex ion channel model can be introduced to consider the kinetic characteristics and interactions of different ion channels.

[0174] The above-mentioned 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 the present invention can realize accurate simulation of cardiac electrophysiological activities and mechanical activities. This method constructs a real whole heart-trunk model containing multiple cardiac tissue structures, and combines the single-domain reaction-diffusion model for electrophysiological modeling, so that relevant parameters can be accurately set, thereby providing a detailed description of the electrophysiological characteristics of different cardiac tissue structures. In addition, by coupling the electrophysiological model with the fiber direction model and the principal stress model, the transmembrane potential and mechanical contraction of the heart can be solved, and then the surface magnetic field signal can be calculated based on these results, providing more accurate data support for the diagnosis and treatment of heart disease.

[0175] In addition, this method can improve the accuracy and reliability of magnetic field simulation by comprehensively considering the electrophysiological and mechanical properties of the heart. This method can not only simulate the electrical activity of the heart, but also simulate the mechanical contraction of the heart, making the simulation results closer to the actual heart activity. This comprehensive simulation capability can provide in-depth insights into the study of heart disease, help develop new treatment strategies and improve treatment effects, especially in the field of cardiac electrophysiology and magnetic physiology research, and can provide a new, non-invasive diagnostic tool.

[0176] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0177] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A magnetic field simulation method based on electromechanical feedback of a real whole heart model, characterized in that: The following steps are involved: Step 1: creating a real whole heart-trunk model, wherein the real whole heart model includes one or more tissue structures of sinoatrial node SAN, right atrium RA, left atrium LA, fossa ovalis FO, Bachmann's bundle BB, atrioventricular node AVN, right ventricle RV, left ventricle LV, right ventricular endocardial wall ER and left ventricular posterior basal area PL; Step 2: Based on the single-domain reaction-diffusion model combined with the dimensional ion flow equation induced by the traction tensor, electromechanical feedback electrophysiological modeling is performed on the real whole heart model, and relevant parameters are set for different cardiac tissue structures, including equation parameters, initial potentials, and boundary conditions; Step 3: Couple the electromechanical feedback electrophysiological model constructed in step 2 with the fiber direction model and the principal stress model to solve the transmembrane potential and mechanical contraction of the heart; Step 4: Based on the cardiac transmembrane potential obtained in step 3, the current module and the magnetic field module are combined to calculate and solve the body surface magnetic field signal.

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: Using the target heart image, the heart is divided into multiple regularly shaped myocardial blocks according to the anatomical structure, and a heart geometric model is constructed by splicing. The left atrium, right atrium and ventricle are discrete and dispersed, and are not connected to each other, forming three different solution domains. Constructing a real trunk geometric model, scanning and imaging the target trunk through target tomography or magnetic resonance imaging, and reconstructing the trunk geometric model according to grayscale threshold segmentation, the trunk geometric model includes homogeneous and inhomogeneous trunk volume conductor models, and for the inhomogeneous volume conductor model, the volume conductors such as the lung, liver, blood pool and bones will be given different conductivities respectively; The heart geometry model and the trunk geometry model are integrated into a joint body.

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: Based on the dual-domain model, a single-domain reaction-diffusion model is constructed, and the cell is divided into two regions: intracellular and extracellular. i represents the intracellular current density, J e represents the extracellular current density, σ i represents the intracellular conductivity, σ e represents the extracellular conductivity, V i represents the intracellular potential, V e represents the extracellular potential, represents the gradient operator, I m Represents the total transmembrane current on the cell membrane. According to Ohm's law formula (1) and current conservation formula (2), formula (3) is obtained: Let V m Represents the transmembrane potential on the cell membrane, and the formula (4) is obtained: V m =V i -V e (4); The cell membrane is simulated as a parallel structure of a resistor and a capacitor, and I ion represents the transmembrane ion current on the cell membrane, C m represents the membrane capacitance per unit area of ​​the cell membrane, t represents the time, and according to the corresponding physical principles, formula (5) is obtained: Ion current across the cell membrane ion Purely electric part I inspired by excitement e and tension tensor-induced ion channel opening part I f Composition can be expressed as formula (6): I ion =I e +I f (6); Consider only I e When , formula (3), (4) and (5) are combined to obtain formula (7): The Fitzhugh-Nagumo model is used to simulate cardiac electrophysiological activity, and its expression is formula (8): Where u represents the transmembrane potential, v represents the recovery variable, and D represents the diffusion coefficient. represents the gradient operator, c1 and c2 are used to control the amplitude of ion current excitation and refractory period, a, b and d are intermediate parameters, and t represents time; on this basis, Rogers and McCulloch modified the model and obtained formula (9): Combining the dual-domain model with the modified FHN model, for the sinoatrial node (SAN), its ionic current I e The expression of the recovery variable v is shown in formula (10): For other subdomains of the heart, the ionic current I e The expression of the recovery variable v is shown in formula (11): Among them, k, c1, c2, A, B, and e are parameters that regulate the shape of the cell action potential curve, respectively. 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 action potential amplitude, e represents the control of action potential duration, and v represents the recovery variable, controlling the cell refractory period. Using dimensional mapping to establish the myocardial transmembrane potential V m The relationship between the dimensionless potential φ and the activation time t and the dimensionless time τ is expressed as (12): where β φ ,δ φ and β t The selection of ensures that the physiological action potential response and characteristic action potential duration of -80 to +20 mV can be obtained; when modeling the electromechanical feedback calculation, 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 flow (13): in, is the dimensionless mapping of ion flow, φ is the dimensionless potential, λ is the tension in the fiber direction, G s is the maximum conductance of the channel induced by the pulling tensor, φ s is the resting potential of the pulling tensor-induced ion channel, is a switching function, which turns on the feedback when λ>1 and turns off otherwise; according to the current induced by the dimensionless traction tensor Get the dimensionless ion current I f , formula (14): Combining formula (6) and (14) with formula (10) and (11) respectively, we get formula (15): I ion =I e +I f Using formula (16) Calculation of transmembrane potential V in cardiac muscle 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 3 includes the following operations: 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 myocardial cell fiber inclination angle gradually changes from 60° in the endocardium to -60° in the epicardium, the fiber angle on the myocardial boundary is determined according to formula (17): Among them, θ epi represents the epicardial fiber inclination angle, θ end represents the inclination angle of endocardial fibers, represents the maximum epicardial fiber inclination, represents the maximum fiber inclination of the endocardium, represents the Z-axis coordinate of the left ventricular apex; based on the fiber distance D to the epicardium epi Distance D between fibers and endocardium end , the dimensionless distance β is calculated by formula (18); The dimensionless distance β represents the fiber distance to the epicardial border; the fiber angle θ through the heart wall is based on the formula (19) θ = βθ end +(1-β)θ epi Linear changes; The fiber distances to the endocardium and epicardium are obtained by solving the wall distance equation, and the slice direction s is obtained using the curvilinear coordinate interface. The curvilinear coordinate system is then rotated around the slice direction to obtain the fiber direction a. The cardiac tissue is simulated as an anisotropic hyperelastic material. The tissue contraction in the hyperelastic model is realized by the additive decomposition of the stress tensor. The additional stress varies with the voltage and is expressed as Equation (20): Where k represents the saturation active stress, ε(V) is the rate conversion function, and φ r Represents the resting potential, and the specific expression of 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 represents phase shift; The principal stresses are added to the second Piola-Kirchhoff stresses in different percentages along the fiber, sheet and normal directions, expressed as Equation (22): Where S and S0 represent stress, and a0, s0, and n0 represent the fiber, sheet, and perpendicular sheet directions, respectively.

5. 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 the heart conductivity, represents the gradient operator, V m represents the membrane voltage calculated at discrete time instants during the spread of excitation within the heart; The total current density J of the trunk is expressed by formula (24): t , I t =J e +J c (24) Among them, J c represents the conduction current; according to Ampere's law, using formula (25) (25) In calculating the total trunk current density J t The body surface magnetic field at each moment is calculated, where H represents the magnetic field strength.

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

10. 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 is the membrane capacitance per unit area of ​​the cell membrane, V m is the transmembrane potential on the cell membrane, I ion is the transmembrane ion current on the cell membrane, I m is the total transmembrane current on the cell membrane, and t is the time; satisfy in, It represents the rate of change of transmembrane potential with time.

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