Reduction method, device and equipment for current source in magnetocardiogram inverse problem

Abstract

The invention provides a method, a device and equipment for restoring a current source in a magnetocardiogram inverse problem, and the method comprises the steps: constructing a forward and inverse problem model of conductor conduction, and deducing to obtain a guide field matrix corresponding to each grid point in a whole heart model; for any target wave band in the magnetocardiogram, magnetocardiogram inverse problem solving is carried out in the range of the heart local tissue model by using magnetocardiogram data corresponding to the target wave band and a guide field matrix corresponding to each grid point in the heart local tissue model corresponding to the target wave band; determining at least one current source reduction result in the heart local tissue model corresponding to the target wave band; and determining the current source reduction results corresponding to the multiple target wavebands as the current source reduction results of the heart of the patient. In this way, the calculation efficiency can be improved by limiting the solving range; the method is helpful for improving the accuracy and authenticity of positioning, and provides a good basis for researchers to predict myocardial ischemia and position ischemia.

Description

Technical Field

[0001] The present application relates to the field of biomedical technology, and in particular to a method, device and equipment for restoring a current source in a magnetic cardioversion problem. Background Art

[0002] Magnetocardiography is a non-invasive examination technology with no radiation, no contact, and high spatial resolution. The inverse magnetocardiography problem refers to the process of inferring cardiac electrical activity from the measured magnetic field data in magnetocardiography measurements; more specifically, current source reduction is to restore the current source inside the heart based on the magnetocardiography data in the magnetocardiogram for display, which is of great significance for researchers to detect myocardial ischemia and determine the location of ischemia.

[0003] However, the existing technology only performs current source restoration based on the acquired magnetic field data, but does not combine the knowledge of the electrical activity of the human heart, so that the restoration results lack authenticity and accuracy, and it is also difficult for clinical researchers to locate myocardial ischemia. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide a method, device and equipment for restoring the current source in the magnetocardiogram inverse problem. According to the prior knowledge of cardiac electrical activity, for different bands in the magnetocardiogram, the current source restoration calculation of the magnetocardiogram inverse problem is performed in the corresponding local tissue model of the heart; in this way, by limiting the scope of the solution, the efficiency of the restoration calculation can be improved; it helps to improve the accuracy and authenticity of positioning, and provide a good basis for researchers to predict myocardial ischemia and ischemia positioning.

[0005] The present application provides a method for restoring a current source in a magnetocentric inverse problem, the method comprising:

[0006] According to the medical imaging data of the patient's heart, establish the patient's overall heart model and multiple heart local tissue models;

[0007] Constructing a forward and inverse problem model of conductor conduction in the patient's heart, and deriving a guidance field matrix corresponding to each grid point in the overall heart model based on the forward and inverse problem model; wherein the guidance field matrix corresponding to each grid point is used to characterize the relationship between the current at the grid point and the generated extracorporeal magnetic field;

[0008] For any target band in the magnetocardiogram of the patient, the magnetocardiogram data corresponding to the target band and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band are used to solve the magnetocardiogram inverse problem within the local cardiac tissue model to determine at least one current source restoration result in the local cardiac tissue model corresponding to the target band; wherein the local cardiac tissue model corresponding to the target band is determined based on prior knowledge of cardiac electrical activity;

[0009] The current source restoration results corresponding to the multiple target bands are determined as the current source restoration results of the patient's heart.

[0010] Furthermore, the guidance field matrix corresponding to each grid point in the overall heart model is derived according to the forward and inverse problem model, including:

[0011] Equivalently treat the current source as a dipole, and substitute the equivalent formula into the forward and inverse problem model;

[0012] Combined with the boundary element algorithm, the equations of the forward and inverse problem model are discretized, and the position vector in each channel in the patient's magnetocardiogram in the direction perpendicular to the chest plane is substituted into the discretized equation to obtain the guidance field matrix corresponding to each grid point in the overall heart model.

[0013] Further, for any target band in the magnetocardiogram, the magnetocardiogram data corresponding to the target band of the patient's magnetocardiogram and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band are used to solve the magnetocardiogram inverse problem within the scope of the local cardiac tissue model to determine at least one current source restoration result in the local cardiac tissue model corresponding to the target band, including:

[0014] For each grid point in the local cardiac tissue model corresponding to the target band, determining the spatial correlation between the guidance field matrix corresponding to the grid point and the magnetocardiogram data corresponding to the target band;

[0015] According to the spatial correlation corresponding to each grid point, the target grid point with the highest spatial correlation is selected from the cardiac local tissue model corresponding to the target band;

[0016] The position of the target grid point is determined as the position of the first current source, and a current source restoration result of the first current source is determined.

[0017] Further, for any target band in the magnetocardiogram of the patient, using the magnetocardiogram data corresponding to the target band and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band, solving the magnetocardiogram inverse problem within the local cardiac tissue model to determine at least one current source restoration result in the local cardiac tissue model corresponding to the target band, further comprising:

[0018] Determine whether the spatial correlation corresponding to the target grid point is greater than a preset correlation threshold;

[0019] If it is not greater than, determining the first magnetic field generated by the first current source according to the guidance field matrix corresponding to the target grid point;

[0020] Determine a residual magnetic field according to the first magnetic field and the magnetocardiogram data corresponding to the target band; wherein the residual magnetic field is used to represent the difference between the magnetocardiogram data corresponding to the target band and the magnetic field generated by the determined current source;

[0021] According to the residual magnetic field, the grid point with the highest spatial correlation is continuously iterated from the local cardiac tissue model corresponding to the target band as the position of the second current source, and the residual magnetic field is updated according to the first current source and the second current source for the next round of iterative calculation; until any selected grid point with the highest spatial correlation meets a preset condition, the iterative calculation is terminated;

[0022] The determined first current source and each second current source are determined as current sources in the local cardiac tissue model corresponding to the target band, and current source restoration results of the first current source and each second current source are determined.

[0023] Further, according to the residual magnetic field, the grid point with the highest spatial correlation is continuously iteratively selected from the local cardiac tissue model corresponding to the target band as the position of the second current source, including:

[0024] For each grid point in the local cardiac tissue model corresponding to the target band, determining the spatial correlation between the guidance field matrix corresponding to the grid point and the residual magnetic field;

[0025] According to the spatial correlation corresponding to each grid point, the grid point with the highest spatial correlation is selected from the cardiac local tissue model corresponding to the target band;

[0026] Determine the correlation between the guidance field matrix corresponding to the grid point and the guidance field matrix of the grid point corresponding to the last determined current source;

[0027] If the correlation is not greater than a preset threshold, the position of the grid point is determined as the position of a second current source.

[0028] Furthermore, when any selected grid point with the highest spatial correlation meets a preset condition, the iterative calculation is terminated, including:

[0029] For any selected grid point with the highest spatial correlation, determine the correlation between the guidance field matrix corresponding to the grid point and the guidance field matrix of the grid point corresponding to the last determined current source;

[0030] If the correlation is greater than a preset threshold, the iterative calculation ends.

[0031] Furthermore, the local cardiac tissue model includes: a left ventricular muscle model, a right ventricular muscle model, and a ventricular septum model; based on the medical imaging data of the patient's heart, the patient's overall heart model and multiple local cardiac tissue models are established, including:

[0032] According to the data labels in the medical image data, segmentation is performed to obtain a heart surface contour point set, a right ventricular epicardium point set, and a left ventricular epicardium point set;

[0033] According to the heart surface contour point set, the heart overall model is established by a mesh reconstruction algorithm;

[0034] According to the right ventricular epicardial point set, a right ventricular model is established by a grid reconstruction algorithm;

[0035] According to the left ventricular epicardial point set, a left ventricular model is established by a grid reconstruction algorithm;

[0036] Constructing a first cutting plane to segment a left ventricular muscle model and a ventricular septum model from the left ventricular model;

[0037] A second cutting plane is constructed to segment the right ventricular muscle model from the right ventricular model.

[0038] Furthermore, the local cardiac tissue model corresponding to the Q band is the ventricular septum model; the local cardiac tissue model corresponding to the R band is the left ventricular muscle model and the right ventricular muscle model; the local cardiac tissue model corresponding to the S band is the left ventricular muscle model; and the local cardiac tissue model corresponding to the P band is the left atrium model and the right atrium model.

[0039] The embodiment of the present application also provides a device for restoring a current source in a magnetocentric inverse problem, the device comprising:

[0040] A model building module, used to build a patient's heart overall model and multiple heart local tissue models based on the patient's heart medical imaging data;

[0041] A matrix derivation module is used to construct a forward and inverse problem model of conductor conduction in the patient's heart, and derive a guidance field matrix corresponding to each grid point in the overall heart model according to the forward and inverse problem model; wherein the guidance field matrix corresponding to each grid point is used to characterize the relationship between the current at the grid point and the generated extracorporeal magnetic field;

[0042] A current source solving module is used for solving a magnetic cardiology inverse problem within the scope of a local cardiac tissue model for any target band in the magnetic cardiology map of the patient, using the magnetic cardiology data corresponding to the target band of the magnetic cardiology map and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band, to determine at least one current source restoration result in the local cardiac tissue model corresponding to the target band; wherein the local cardiac tissue model corresponding to the target band is determined based on prior knowledge of cardiac electrical activity;

[0043] The determination module is used to determine the current source restoration results corresponding to the multiple target bands as the current source restoration results of the patient's heart.

[0044] An embodiment of the present application also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the current source restoration method in the above-mentioned magnetocardiocentrism inverse problem are performed.

[0045] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for restoring the current source in the above-mentioned magnetocardiocentrism inverse problem are executed.

[0046] The embodiments of the present application provide a method, device and equipment for restoring the current source in the magnetocardiogram inverse problem. According to the prior knowledge of cardiac electrical activity, for different bands in the magnetocardiogram, the current source restoration calculation of the magnetocardiogram inverse problem is performed in the corresponding local cardiac tissue model. In this way, by limiting the scope of the solution, the efficiency of the restoration calculation can be improved, which helps to improve the accuracy and authenticity of positioning and provide a good basis for researchers to predict myocardial ischemia and ischemia positioning.

[0047] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A flow chart showing a method for restoring a current source in a magnetocentric inverse problem provided by an embodiment of the present application is shown;

[0050] Figure 2 A schematic diagram of a point set of the inner and outer membranes of the left ventricle provided in an embodiment of the present application is shown;

[0051] Figure 3 A schematic diagram showing a triangulated mesh surface of the inner and outer membranes of the left ventricle obtained by a mesh reconstruction algorithm provided in an embodiment of the present application;

[0052] Figure 4 A schematic diagram showing a point set inside the left ventricle obtained by Delaunay triangulation provided in an embodiment of the present application is shown;

[0053] 5(a) and (b) are schematic diagrams showing a right ventricular muscle segmented by a cutting plane at different viewing angles provided in an embodiment of the present application;

[0054] 6(a) and (b) are schematic diagrams showing a cutting plane segmenting the left ventricular muscle and the ventricular septum at different viewing angles provided in an embodiment of the present application;

[0055] Figure 7 A schematic structural diagram of a current source restoration device in a magnetocentric inverse problem provided by an embodiment of the present application is shown;

[0056] Figure 8 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0057] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0058] Research has found that magnetocardiography is a non-invasive examination technology with no radiation, no contact, and high spatial resolution. The inverse magnetocardiography problem refers to the process of inferring cardiac electrical activity from the measured magnetic field data in magnetocardiography measurements; more specifically, current source reduction is to restore the current source inside the heart based on the magnetocardiography data in the magnetocardiogram for display, which is of great significance for researchers to detect myocardial ischemia and determine the location of ischemia.

[0059] However, the existing technology only performs current source restoration based on the acquired magnetic field data, but does not combine the knowledge of the electrical activity of the human heart, so that the restoration results lack authenticity and accuracy, and it is also difficult for clinical researchers to locate myocardial ischemia.

[0060] Based on this, an embodiment of the present application provides a method for restoring the current source in the magnetic cardioinverse problem to improve the efficiency of the restoration calculation, help improve the accuracy and authenticity of positioning, and provide a good basis for researchers to predict myocardial ischemia and ischemia localization.

[0061] See also Figure 1 , Figure 1 This is a flow chart of a method for restoring a current source in a magnetic-centrocentric inverse problem provided by an embodiment of the present application. Figure 1 As shown in, the reduction method provided in the embodiment of the present application comprises:

[0062] S101. Establishing an overall heart model and multiple local heart tissue models of the patient based on medical imaging data of the patient's heart.

[0063] Wherein, the local cardiac tissue model includes: a left ventricular muscle model, a right ventricular muscle model, and a ventricular septum model.

[0064] In a possible implementation, step S101 may include:

[0065] S1011. Segment the heart surface contour point set, the right ventricular epicardial point set, and the left ventricular epicardial point set according to the data labels in the medical image data.

[0066] In this step, the heart data obtained by CTA scanning can be processed, and the heart surface contour point set, the right ventricular epicardium point set, and the left ventricular epicardium point set can be separated according to different labels of the data. Figure 2 As shown, Figure 2 This is a schematic diagram of a point set of the left ventricular endocardium and endocardium provided in an embodiment of the present application. The blue color is the point set of the left ventricular endocardium, and the brown color is the point set of the left ventricular endocardium.

[0067] S1012: Establish the whole heart model according to the heart surface contour point set by using a mesh reconstruction algorithm.

[0068] S1013. Establish a right ventricular model based on the right ventricular epicardial point set using a grid reconstruction algorithm.

[0069] S1014: Establish a left ventricular model based on the left ventricular epicardial point set using a grid reconstruction algorithm.

[0070] For steps S1012 to 1014, the point set on the heart surface may be firstly reconstructed into a grid; then the interior surrounded by the surface may be gridded to obtain a corresponding heart model. Corresponding to the above example, the left ventricle model is used as an example for explanation. It should be understood that the right ventricle and the heart surface contour are also processed using the same method.

[0071] First, a mesh reconstruction algorithm is used according to the point set to reconstruct the triangular mesh of the surface of the left ventricle and the ventricular epicardium. For example, a Poisson reconstruction algorithm can be used. Figure 3 As shown, Figure 3 A schematic diagram of a triangulated mesh surface of the left ventricular endocardium obtained by a mesh reconstruction algorithm provided in an embodiment of the present application. The green and black colors are respectively the surfaces of the left ventricular endocardium and the left ventricular endocardium after triangulated mesh reconstruction.

[0072] After that, the inner and outer membranes of the left ventricle are taken as the surface, and the interior surrounded by the surface is subjected to Delaunay triangulation, and finally the internal grid points of the left ventricle after meshing are obtained. Figure 4 As shown, Figure 4 This is a schematic diagram of a point set inside the left ventricle obtained by Delaunay triangulation provided in an embodiment of the present application, wherein the grid points of the ventricular muscle are the vertices of the internal triangles.

[0073] S1015, constructing a first cutting plane to segment the left ventricular muscle model and the ventricular septum model from the left ventricular model.

[0074] S1016: Construct a second cutting plane to segment the right ventricular muscle model from the right ventricular model.

[0075] For steps S1015 and 1016, first construct a coordinate system: the xoy plane is the person's lying plane, which can also be considered as the frontal plane. The multi-channel magnetocardiogram detector is above the person's lying plane and parallel to the lying plane. The z direction is the direction of the line between the heart and the magnetocardiogram detector, which is perpendicular to the chest cavity and points to the detector. The positive direction of the x-axis is to the right, and the positive direction of the y-axis is from the bottom to the head.

[0076] In the specific implementation, the principal component analysis method is first used to calculate the long axis of the right ventricle in the x and y directions. The long axis is a unit vector and is recorded as (A, B), where A, B>0, and its short axis is obviously (B, -A). The center point of the right ventricle is recorded as (x0, y0, z0), and the short axis (B, -A) is used as the normal vector to construct the equation of the tangent plane 1: B(x-x0)-A(y-y0)=0.

[0077] Take all the right ventricle points with x>x0, and calculate the distance from these points to the plane, give the maximum distance d1 and the point is recorded as (x1, y1, z1). Since the right ventricle is thin, the cutting plane 1 is moved in the direction of the short axis by d2=5d1 / 9, and the equation of the cutting plane 2 is obtained:

[0078] B(x-x2)-A(y-y2)=0

[0079] Where x2=x0+B·d2, y2=y0-A·d2.

[0080] The points in the right ventricle whose distance to the cutting plane 2 is greater than 0 are the right ventricular muscle. As shown in Figures 5(a) and (b) below, Figures 5(a) and (b) are schematic diagrams of the right ventricular muscle segmented by the cutting plane under different viewing angles provided by the embodiment of the present application. Among them, the colored grid is the right ventricle, the blue plane is the cutting plane 2, and the part on the left is the right ventricular muscle obtained according to the algorithm.

[0081] Afterwards, the method of dividing the left ventricle into the ventricular septum and the left ventricular muscle is similar to the method of obtaining the right ventricular muscle. The above-mentioned right ventricle is replaced by the internal area composed of the inner and outer membranes of the left ventricle, and d2=d1 / 2 is modified. This is because the ventricular septum is wider than the right ventricular muscle. The other steps are similar. As shown in Figures 6(a) and (b) below, Figures 6(a) and (b) are schematic diagrams of cutting planes that segment the left ventricular muscle and the ventricular septum under different perspectives provided in an embodiment of the present application. Among them, the blue plane is cutting plane 2, and its left and right sides are the ventricular septum and left ventricular muscle obtained according to the algorithm.

[0082] Furthermore, by adopting different cutting algorithms for cutting, the local cardiac tissue model may also include: a left atrium model and a right atrium model, etc.

[0083] S102, constructing a forward and inverse problem model of conductor conduction in the patient's heart, and deriving a guidance field matrix corresponding to each grid point in the overall heart model based on the forward and inverse problem model.

[0084] The guidance field matrix corresponding to each grid point is used to characterize the relationship between the current at the grid point and the generated in vitro magnetic field.

[0085] The following first introduces the process of constructing the forward and inverse problem model of conductor conduction in the patient's heart. In the embodiment of the present application, the forward and inverse problem model of conductor conduction includes the forward and inverse problem model of volume conductor conduction and the forward and inverse problem model of horizontal layered conductor conduction.

[0086] For the forward and inverse problem model of volume conductor conduction, according to the quasi-static Maxwell equations, the magnetic field and current measured at any channel r = (x, y, z) can be expressed as follows:

[0087]

[0088] Where S ′ 1,S ′ 2,S ′ 3 are curved surfaces formed by the surfaces of the heart, the body, and the lungs, which can be determined by medical imaging data or empirical data; l = 1, 2, 3 are the internal and external conductivity of different parts respectively; B0(r) is the magnetic field directly generated by the current, V' is the inside of the heart, J P (r′) is the initial current inside the heart.

[0089]

[0090] The second half of B(r) is the magnetic field generated by the body potential effect of the secondary current, and V(r) is the magnetic field generated by S on different surfaces. ′ k , the potential of k=1,2,3.

[0091]

[0092]

[0093] For the forward and inverse problem model of horizontal layered conductor conduction, the above formula (1) can be simplified by only considering B0(r) in the above formula (1), and the forward and inverse problem model of horizontal layered conductor conduction is obtained.

[0094]

[0095] Furthermore, in a specific implementation, the step S102 of deriving the guidance field matrix corresponding to each grid point in the overall heart model according to the forward and inverse problem model may include:

[0096] The current source is equivalent to a dipole, and the equivalent formula is substituted into the forward and inverse problem model; the equations of the forward and inverse problem model are discretized by combining the boundary element algorithm, and the position vector in the direction perpendicular to the chest plane in each channel of the magnetocardiogram is substituted into the discretized equation to obtain the guidance field matrix corresponding to each grid point in the overall heart model.

[0097] In this step, the current source can be equivalent to a dipole, and the equivalent formula J P (r′)=J(r″)δ(r″-r′) is substituted into the forward and inverse problem model. Wherein, r″ represents the grid point in the overall heart model, and J(r″) is the direction of the current source at the corresponding point r″. For the forward and inverse problem model of the above-mentioned volume conductor conduction, the equivalent formula is substituted into the above equations (2) and (4).

[0098] Then, the above equations (1)-(4) are discretized by combining the boundary element algorithm, and the position vector perpendicular to the chest plane is taken. Finally, the forward and inverse problem model of volume conductor conduction at each grid point r″ can be obtained, as shown in the following equation (6). Then, the guidance field matrix LF(r″) corresponding to each grid point r″ can be derived from the following equation (6) through mathematical transformation:

[0099] B z =(B z (r1),B z (r2),…,B z (r M )) T =LF(r″)J(r″) (6)

[0100] Among them, B z For a multi-channel magnetocardiographic detector, the M channels r1, r2, ..., r M The position vector measured perpendicular to the chest plane is: LF(r″) is the guide field matrix at the grid point r″; J(r″) is the direction of the current source at the grid point r″, which is generally the product of the current direction (unit vector) and the intensity.

[0101] It should be noted that the guidance field matrix corresponding to each grid point in the overall heart model is only related to the position of the magnetic field channel in the magnetocardiogram detector and the position of each grid point in the overall heart model, and has nothing to do with the specific magnetocardiogram data; in subsequent steps, the data of the magnetocardiogram is brought into the guidance field matrix to obtain the current source result of each grid.

[0102] S103. For any target band in the magnetocardiogram of the patient, use the magnetocardiogram data corresponding to the target band and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band to solve the magnetocardiogram inverse problem within the local cardiac tissue model to determine at least one current source restoration result in the local cardiac tissue model corresponding to the target band.

[0103] The local cardiac tissue model corresponding to the target band is determined based on prior knowledge of cardiac electrical activity.

[0104] Here, during the depolarization of the ventricles in the heart during the three QRS bands, the depolarization positions corresponding to different bands are different. Specifically, the initial depolarization of the ventricles begins with the Q wave, and the depolarization position corresponding to the Q band is the middle part of the left and right ventricles, called the ventricular septum. Subsequently, the electrical conduction activity is transmitted to the left and right ventricles via the left and right bundle branches and Purkinje fibers, and the corresponding band is the R band. Therefore, the depolarization position of the R band is the left and right ventricular muscles. Finally, because the ventricular muscles of the right ventricle are thinner, its depolarization stops at the end of the R band, so in the S band, only the left ventricle is still depolarized.

[0105] Therefore, it can be determined that the local cardiac tissue model corresponding to the Q band is the ventricular septum model; the local cardiac tissue model corresponding to the R band is the left ventricular muscle model and the right ventricular muscle model; and the local cardiac tissue model corresponding to the S band is the left ventricular muscle model.

[0106] Similarly, the local cardiac tissue models corresponding to the P band are the left atrium model and the right atrium model, and the local cardiac tissue models corresponding to the TT band are the left ventricular muscle model and the right ventricular muscle model.

[0107] In a possible implementation, step S103 may include:

[0108] S1031. For each grid point in the local cardiac tissue model corresponding to the target band, determine the spatial correlation between the guidance field matrix corresponding to the grid point and the magnetocardiogram data corresponding to the target band.

[0109] In this step, the magnetic cardio data B of a single frame in any target band can be calculated by the following formula: z The spatial correlation between each grid point r″ and the corresponding cardiac local tissue model:

[0110] ρ=subcorr{LF(r″),B z}

[0111] Where subcorr{a,b} gives the subspace correlation of a and b.

[0112] S1032. Filter out target grid points with the highest spatial correlation from the cardiac local tissue model corresponding to the target band according to the spatial correlation corresponding to each grid point.

[0113] Since LF is the guidance field matrix, the subspace correlation is a vector, and max is used to select the maximum value. Then the spatial correlation of the target grid point P0 can be expressed as:

[0114] ρ=maxsubcorr{LF(r″),B z}

[0115] S1033. Determine the position of the target grid point as the position of the first current source, and determine a current source restoration result of the first current source.

[0116] Here, the spatial correlation of the target grid point is the highest, indicating that as a current source, the magnetic field generated by the current generated by this point is most similar to the actually measured magnetocardiographic data, so this target grid point is the first current source.

[0117] In this step, the position of the target grid point P0 can be determined as the position of the first current source. Further, the guidance field matrix LF(r″) corresponding to P0 is determined by the position of the first current source P0 and the guidance field matrix LF(r″) corresponding to each grid point r″ derived from the above formula (6); according to the guidance field matrix LF(r″) corresponding to P0, the magnetic field data and the above formula (6), the least squares method is calculated to solve the current source direction and intensity at the first current source P0. Specifically, the formula J(r″)=[LF(r″)LF(r″) T ] -1 LF(r″) T B z , which is the current source direction of the first final current source (the product of the unit current direction and intensity). Then, the position, current source direction and intensity of the first current source are jointly determined as the current source restoration result of the first current source.

[0118] Furthermore, step S103 may also include:

[0119] Step a: for each grid point in the local cardiac tissue model corresponding to the target band, determine the spatial correlation between the guidance field matrix corresponding to the grid point and the magnetocardiogram data corresponding to the target band.

[0120] Step b: According to the spatial correlation corresponding to each grid point, the target grid point with the highest spatial correlation is selected from the cardiac local tissue model corresponding to the target band.

[0121] Among them, steps a and b can refer to the description in S1031 and S1032 above, and can achieve the same technical effect, which will not be repeated here.

[0122] Step c: determine whether the spatial correlation corresponding to the target grid point is greater than a preset correlation threshold.

[0123] Step d: If it is greater than, the position of the target grid point is determined as the position of the first current source.

[0124] Here, the embodiment of the present application presets a correlation threshold (for example, 0.95). If the spatial correlation corresponding to the target grid point is greater than the preset correlation threshold, it means that the current generated by the single first current source can form the magnetic field reflected by the magnetocardiogram. If the spatial correlation corresponding to the target grid point is not greater than the preset correlation threshold, it means that the magnetic field reflected by the magnetocardiogram may not be formed by the current generated by the first current source alone. Correspondingly, the embodiment of the present application also provides the following method for restoring multiple current sources. That is to say, the restoration of the inverse magnetocardiogram problem is no longer limited to a single current source, which can better characterize some characteristics of myocardial ischemia. This is because the embodiment of the present application has found in medical practice that myocardial ischemia will cause the electrical activity of the heart to no longer be continuous, which is reflected in the current source as multiple current sources.

[0125] Step e: If it is not greater than, determining the first magnetic field generated by the first current source according to the guidance field matrix corresponding to the target grid point.

[0126] In this step, as mentioned above, the first current source J(r″) can be solved by the least squares method according to the guidance field matrix corresponding to the target grid point, that is, the above formula (6) and the magnetic field data; and then the first magnetic field B0 generated by the first current source P0 is determined by the first current source J(r″) and the guidance field matrix.

[0127] Step f: determining a residual magnetic field according to the first magnetic field and the magnetocardiogram data corresponding to the target band.

[0128] The residual magnetic field is used to represent the difference between the magnetic field data corresponding to the magnetic field map in the target band and the magnetic field generated by the determined current source. e =B z -B0; In the subsequent iterative calculation process, the residual magnetic field needs to be updated according to the first current source and the second current source determined in each round for the next round of iterative calculation.

[0129] Step g: According to the residual magnetic field, the grid point with the highest spatial correlation is continuously selected from the local cardiac tissue model corresponding to the target band as the position of the second current source, and the residual magnetic field is updated according to the first current source and each second current source for the next round of iterative calculation. The iterative calculation is terminated until any selected grid point with the highest spatial correlation meets the preset condition.

[0130] The formula is expressed as:

[0131] ρ=max subcorr{LF(r″),B e}

[0132] Here, in step g: according to the residual magnetic field, continuously iteratively selecting the grid point with the highest spatial correlation from the local cardiac tissue model corresponding to the target band as the position of the second current source includes:

[0133] For each grid point in the local cardiac tissue model corresponding to the target band, determine the spatial correlation between the guidance field matrix corresponding to the grid point and the residual magnetic field; based on the spatial correlation corresponding to each grid point, screen out the grid point with the highest spatial correlation from the local cardiac tissue model corresponding to the target band; determine the correlation between the guidance field matrix corresponding to the grid point and the guidance field matrix of the grid point corresponding to the last determined current source; if the correlation is not greater than a preset threshold, determine the position of the grid point as the position of a second current source.

[0134] Here, if the correlation between the guidance field matrix corresponding to the grid point solved in a certain round of iterative calculation and the guidance field matrix of the grid point corresponding to the last determined current source is too high, it means that the grid point should be included in the last solved current source, and should not be used as a solution to a new current source, so as to avoid noise and overfitting.

[0135] Correspondingly, when any selected grid point with the highest spatial correlation meets the preset condition, the iterative calculation is terminated, including:

[0136] For any selected grid point with the highest spatial correlation, determine the correlation between the guidance field matrix corresponding to the grid point and the guidance field matrix of the grid point corresponding to the last determined current source; if the correlation is greater than a preset threshold, end the iterative calculation.

[0137] As an example, in the first round of iterative calculation, first according to ρ = max subcorr {LF (r ″), B e}, the grid point P1 with the highest spatial correlation can be solved; then, the correlation between the guidance field matrix corresponding to P1 and the guidance field matrix corresponding to P0 is determined; if the correlation is less than a preset threshold, P1 is determined as a second current source, and the second round of iterative calculation is started.

[0138] In the second round of iterative calculation, the difference between the magnetocardiogram data corresponding to the target band and the magnetic field generated by the first current source P0 and the second current source P1 is first determined, that is, the residual magnetic field B is updated according to the magnetic field generated by the determined current source. e , B e =B z-B0-B1; then, the grid point P2 with the highest spatial correlation can be solved; then, the correlation between the guidance field matrix corresponding to P2 and the guidance field matrix corresponding to P1 is determined; if the correlation is greater than the preset threshold, it means that P2 should not be used as a current source, and the iterative calculation ends. Correspondingly, the current sources finally solved are the first current source P0 and the second current source P1.

[0139] Step h: determine the first current source and each second current source that have been determined as the current source in the local cardiac tissue model corresponding to the target band, and determine the current source restoration results of the first current source and each second current source.

[0140] Similarly, in this step, the positions of the first current source and each second current source can be determined as the positions of the current sources in the local tissue of the heart; further, based on the positions of the grid points corresponding to each current source, the magnetic field data and the above formula (6), the current source direction and strength at each current source can be solved, and then the position, current source direction and strength of each current source can be jointly determined as the current source restoration result.

[0141] S104: Determine the current source restoration results corresponding to the multiple target bands as the current source restoration results of the patient's heart.

[0142] In this step, each target band and the current source restoration result corresponding to the band can be integrated to jointly determine the current source restoration result of the patient's heart, so that the current source restoration result can reflect the corresponding relationship between the heart band and the current source, making the result more intuitive and clear, which helps to improve the accuracy and authenticity of positioning. For example, for the Q-band magnetic heart signal, the magnetic heart inverse problem is solved in the ventricular septum model according to the guidance field matrix of its internal grid points; for the R-band magnetic heart signal, the magnetic heart inverse problem is solved in the left and right ventricular muscle models according to the guidance field matrix of its internal grid points; for the S-band magnetic heart signal, the magnetic heart inverse problem is solved in the left ventricular muscle model according to the guidance field matrix of its internal grid points. Finally, the multi-current source restoration positioning results of the three bands are combined to obtain the patient's cardiac electrophysiological activity results.

[0143] An embodiment of the present application provides a method for restoring the current source in the magnetocardiogram inverse problem. Based on the prior knowledge of cardiac electrical activity, the current source restoration calculation of the magnetocardiogram inverse problem is performed in the corresponding local cardiac tissue model for different bands in the magnetocardiogram. In this way, by limiting the scope of the solution, the efficiency of the restoration calculation can be improved, which helps to improve the accuracy and authenticity of positioning and provide a good basis for researchers to predict myocardial ischemia and ischemia positioning.

[0144] Furthermore, the embodiments of the present application also provide multi-current source restoration and positioning calculation, that is, the restoration of the cardiac magnetic inversion problem is no longer limited to a single current source, which can better characterize some characteristics of myocardial ischemia, because myocardial ischemia will make the heart's electrical activity no longer continuous, which is reflected in the current source as multiple current sources.

[0145] Among them, in the multi-current source restoration positioning calculation, the embodiment of the present application determines whether to use the newly solved grid point as the current source by judging the correlation between the guidance field matrix corresponding to the newly solved grid point and the guidance field matrix corresponding to the determined current source, so as to avoid noise interference and overfitting in the solution process, thereby improving the accuracy of the current source restoration result.

[0146] See also Figure 7 , Figure 7 This is a schematic diagram of the structure of a current source restoration device in a magnetic-centrocentric inverse problem provided by an embodiment of the present application. Figure 7 As shown in , the reduction device 700 includes:

[0147] A model building module 710 is used to build a patient's heart overall model and multiple heart local tissue models based on the patient's heart medical imaging data;

[0148] A matrix derivation module 720 is used to construct a forward and inverse problem model of conductor conduction in the patient's heart, and to derive a guidance field matrix corresponding to each grid point in the overall heart model according to the forward and inverse problem model; wherein the guidance field matrix corresponding to each grid point is used to characterize the relationship between the current at the grid point and the generated extracorporeal magnetic field;

[0149] The current source solving module 730 is used to solve the inverse magnetocardiogram problem within the scope of any target band in the magnetocardiogram of the patient using the magnetocardiogram data corresponding to the target band and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band, and determine at least one current source restoration result in the local cardiac tissue model corresponding to the target band; wherein the local cardiac tissue model corresponding to the target band is determined based on prior knowledge of cardiac electrical activity;

[0150] The determination module 740 is used to determine the current source restoration results corresponding to the multiple target bands as the current source restoration results of the patient's heart.

[0151] Furthermore, when the matrix derivation module 720 is used to derive the guidance field matrix corresponding to each grid point in the whole heart model according to the forward and inverse problem model, the matrix derivation module 720 is used to:

[0152] Equivalently treat the current source as a dipole, and substitute the equivalent formula into the forward and inverse problem model;

[0153] Combined with the boundary element algorithm, the equations of the forward and inverse problem model are discretized, and the position vector in each channel in the patient's magnetocardiogram in the direction perpendicular to the chest plane is substituted into the discretized equation to obtain the guidance field matrix corresponding to each grid point in the overall heart model.

[0154] Furthermore, when the current source solving module 730 is used for solving the inverse magnetocardiogram problem within the scope of the local cardiac tissue model for any target band in the magnetocardiogram of the patient, using the magnetocardiogram data corresponding to the target band and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band, and determining at least one current source restoration result in the local cardiac tissue model corresponding to the target band, the current source solving module 730 is used to:

[0155] For each grid point in the local cardiac tissue model corresponding to the target band, determining the spatial correlation between the guidance field matrix corresponding to the grid point and the magnetocardiogram data corresponding to the target band;

[0156] According to the spatial correlation corresponding to each grid point, the target grid point with the highest spatial correlation is selected from the cardiac local tissue model corresponding to the target band;

[0157] The position of the target grid point is determined as the position of the first current source, and a current source restoration result of the first current source is determined.

[0158] Furthermore, when the current source solving module 730 is used for solving the inverse magnetocardiogram problem within the scope of the local cardiac tissue model for any target band in the magnetocardiogram of the patient, using the magnetocardiogram data corresponding to the target band and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band, and determining at least one current source restoration result in the local cardiac tissue model corresponding to the target band, the current source solving module 730 is further used to:

[0159] Determine whether the spatial correlation corresponding to the target grid point is greater than a preset correlation threshold;

[0160] If it is not greater than, determining the first magnetic field generated by the first current source according to the guidance field matrix corresponding to the target grid point;

[0161] Determine a residual magnetic field according to the first magnetic field and the magnetocardiogram data corresponding to the target band; wherein the residual magnetic field is used to represent the difference between the magnetocardiogram data corresponding to the target band and the magnetic field generated by the determined current source;

[0162] According to the residual magnetic field, the grid point with the highest spatial correlation is continuously iterated from the local cardiac tissue model corresponding to the target band as the position of the second current source, and the residual magnetic field is updated according to the first current source and the second current source for the next round of iterative calculation; until any selected grid point with the highest spatial correlation meets a preset condition, the iterative calculation is terminated;

[0163] The determined first current source and each second current source are determined as current sources in the local cardiac tissue model corresponding to the target band, and current source restoration results of the first current source and each second current source are determined.

[0164] Furthermore, when the current source solving module 730 is used to iteratively select the grid point with the highest spatial correlation from the local cardiac tissue model corresponding to the target band as the position of the second current source according to the residual magnetic field, the current source solving module 730 is used to:

[0165] For each grid point in the local cardiac tissue model corresponding to the target band, determining the spatial correlation between the guidance field matrix corresponding to the grid point and the residual magnetic field;

[0166] According to the spatial correlation corresponding to each grid point, the grid point with the highest spatial correlation is selected from the cardiac local tissue model corresponding to the target band;

[0167] Determine the correlation between the guidance field matrix corresponding to the grid point and the guidance field matrix of the grid point corresponding to the last determined current source;

[0168] If the correlation is not greater than a preset threshold, the position of the grid point is determined as the position of a second current source.

[0169] Furthermore, when the current source solving module 730 is used to terminate the iterative calculation if any selected grid point with the highest spatial correlation satisfies a preset condition, the current source solving module 730 is used to:

[0170] For any selected grid point with the highest spatial correlation, determine the correlation between the guidance field matrix corresponding to the grid point and the guidance field matrix of the grid point corresponding to the last determined current source;

[0171] If the correlation is greater than a preset threshold, the iterative calculation ends.

[0172] Furthermore, the local cardiac tissue model includes: a left ventricular muscle model, a right ventricular muscle model, and a ventricular septum model; when the model building module 710 is used to establish the patient's heart overall model and multiple local cardiac tissue models according to the medical imaging data of the patient's heart, the model building module is used to:

[0173] According to the data labels in the medical image data, segmentation is performed to obtain a heart surface contour point set, a right ventricular epicardium point set, and a left ventricular epicardium point set;

[0174] According to the heart surface contour point set, the heart overall model is established by a mesh reconstruction algorithm;

[0175] According to the right ventricular epicardial point set, a right ventricular model is established by a grid reconstruction algorithm;

[0176] According to the left ventricular epicardial point set, a left ventricular model is established by a grid reconstruction algorithm;

[0177] Constructing a first cutting plane to segment a left ventricular muscle model and a ventricular septum model from the left ventricular model;

[0178] A second cutting plane is constructed to segment the right ventricular muscle model from the right ventricular model.

[0179] Furthermore, the local cardiac tissue model corresponding to the Q band is the ventricular septum model; the local cardiac tissue model corresponding to the R band is the left ventricular muscle model and the right ventricular muscle model; the local cardiac tissue model corresponding to the S band is the left ventricular muscle model; and the local cardiac tissue model corresponding to the P band is the left atrium model and the right atrium model.

[0180] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 8 As shown in , the electronic device 800 includes a processor 810 , a memory 820 and a bus 830 .

[0181] The memory 820 stores machine-readable instructions executable by the processor 810. When the electronic device 800 is running, the processor 810 communicates with the memory 820 via the bus 830. When the machine-readable instructions are executed by the processor 810, the above-mentioned Figure 1 The steps of the method for restoring the current source in the magnetic-centrocentric inverse problem in the method embodiment shown in the figure, and the specific implementation manner can be referred to the method embodiment, which will not be repeated here.

[0182] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The steps of the method for restoring the current source in the magnetic-centrocentric inverse problem in the method embodiment shown in the figure, and the specific implementation manner can be referred to the method embodiment, which will not be repeated here.

[0183] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0184] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0187] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method 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.

[0188] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for restoring the current source in the magnetic inverse problem, characterized in that: The reduction method comprises: According to the medical imaging data of the patient's heart, establish the patient's overall heart model and multiple heart local tissue models; Constructing a forward and inverse problem model of conductor conduction in the patient's heart, and deriving a guidance field matrix corresponding to each grid point in the overall heart model based on the forward and inverse problem model; wherein the guidance field matrix corresponding to each grid point is used to characterize the relationship between the current at the grid point and the generated extracorporeal magnetic field; For any target band in the magnetocardiogram of the patient, the magnetocardiogram data corresponding to the target band and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band are used to solve the magnetocardiogram inverse problem within the local cardiac tissue model to determine at least one current source restoration result in the local cardiac tissue model corresponding to the target band; wherein the local cardiac tissue model corresponding to the target band is determined based on prior knowledge of cardiac electrical activity; The current source restoration results corresponding to the multiple target bands are determined as the current source restoration results of the patient's heart.

2. The reduction method according to claim 1, characterized in that: The guiding field matrix corresponding to each grid point in the overall heart model is derived according to the forward and inverse problem model, including: Equivalently treat the current source as a dipole, and substitute the equivalent formula into the forward and inverse problem model; Combined with the boundary element algorithm, the equations of the forward and inverse problem model are discretized, and the position vector in each channel in the patient's magnetocardiogram in the direction perpendicular to the chest plane is substituted into the discretized equation to obtain the guidance field matrix corresponding to each grid point in the overall heart model.

3. The reduction method according to claim 1, characterized in that: For any target band in the magnetocardiogram, using the magnetocardiogram data corresponding to the target band of the patient's magnetocardiogram and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band, solving the magnetocardiogram inverse problem within the scope of the local cardiac tissue model, and determining at least one current source restoration result in the local cardiac tissue model corresponding to the target band, including: For each grid point in the local cardiac tissue model corresponding to the target band, determining the spatial correlation between the guidance field matrix corresponding to the grid point and the magnetocardiogram data corresponding to the target band; According to the spatial correlation corresponding to each grid point, the target grid point with the highest spatial correlation is selected from the cardiac local tissue model corresponding to the target band; The position of the target grid point is determined as the position of the first current source, and a current source restoration result of the first current source is determined.

4. The method according to claim 3, characterized in that: For any target band in the magnetocardiogram of the patient, using the magnetocardiogram data corresponding to the target band and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band, solving the magnetocardiogram inverse problem within the local cardiac tissue model, and determining at least one current source restoration result in the local cardiac tissue model corresponding to the target band, further comprising: Determine whether the spatial correlation corresponding to the target grid point is greater than a preset correlation threshold; If it is not greater than, determining the first magnetic field generated by the first current source according to the guidance field matrix corresponding to the target grid point; Determine a residual magnetic field according to the first magnetic field and the magnetocardiogram data corresponding to the target band; wherein the residual magnetic field is used to represent the difference between the magnetocardiogram data corresponding to the target band and the magnetic field generated by the determined current source; According to the residual magnetic field, the grid point with the highest spatial correlation is continuously iterated from the local cardiac tissue model corresponding to the target band as the position of the second current source, and the residual magnetic field is updated according to the first current source and the second current source for the next round of iterative calculation; until any selected grid point with the highest spatial correlation meets a preset condition, the iterative calculation is terminated; The determined first current source and each second current source are determined as current sources in the local cardiac tissue model corresponding to the target band, and current source restoration results of the first current source and each second current source are determined.

5. The method according to claim 4, characterized in that According to the residual magnetic field, continuously iteratively selecting a grid point with the highest spatial correlation from the local cardiac tissue model corresponding to the target band as the position of the second current source includes: For each grid point in the local cardiac tissue model corresponding to the target band, determining the spatial correlation between the guidance field matrix corresponding to the grid point and the residual magnetic field; According to the spatial correlation corresponding to each grid point, the grid point with the highest spatial correlation is selected from the cardiac local tissue model corresponding to the target band; Determine the correlation between the guidance field matrix corresponding to the grid point and the guidance field matrix of the grid point corresponding to the last determined current source; If the correlation is not greater than a preset threshold, the position of the grid point is determined as the position of a second current source.

6. The reduction method according to claim 5, characterized in that: When any selected grid point with the highest spatial correlation meets a preset condition, the iterative calculation is terminated, including: For any selected grid point with the highest spatial correlation, determine the correlation between the guidance field matrix corresponding to the grid point and the guidance field matrix of the grid point corresponding to the last determined current source; If the correlation is greater than a preset threshold, the iterative calculation ends.

7. The method according to claim 1, characterized in that The local cardiac tissue model includes: a left ventricular muscle model, a right ventricular muscle model, and a ventricular septum model; based on the medical imaging data of the patient's heart, the patient's overall heart model and multiple local cardiac tissue models are established, including: According to the data labels in the medical image data, segmentation is performed to obtain a heart surface contour point set, a right ventricular epicardium point set, and a left ventricular epicardium point set; According to the heart surface contour point set, the heart overall model is established by a mesh reconstruction algorithm; According to the right ventricular epicardial point set, a right ventricular model is established by a grid reconstruction algorithm; According to the left ventricular epicardial point set, a left ventricular model is established by a grid reconstruction algorithm; Constructing a first cutting plane to segment a left ventricular muscle model and a ventricular septum model from the left ventricular model; A second cutting plane is constructed to segment the right ventricular muscle model from the right ventricular model.

8. The method according to any one of claims 1 to 7, characterized in that: The local cardiac tissue model corresponding to the Q band is the ventricular septum model; the local cardiac tissue model corresponding to the R band is the left ventricular muscle model and the right ventricular muscle model; the local cardiac tissue model corresponding to the S band is the left ventricular muscle model; the local cardiac tissue model corresponding to the P band is the left atrium model and the right atrium model.

9. A device for restoring the current source in the magnetic inverse problem, characterized in that: The reduction device comprises: A model building module, used to build a patient's heart overall model and multiple heart local tissue models based on the patient's heart medical imaging data; A matrix derivation module is used to construct a forward and inverse problem model of conductor conduction in the patient's heart, and derive a guidance field matrix corresponding to each grid point in the overall heart model according to the forward and inverse problem model; wherein the guidance field matrix corresponding to each grid point is used to characterize the relationship between the current at the grid point and the generated extracorporeal magnetic field; A current source solving module is used for solving a magnetic cardiology inverse problem within the scope of a local cardiac tissue model for any target band in the magnetic cardiology map of the patient, using the magnetic cardiology data corresponding to the target band of the magnetic cardiology map and the guiding field matrix corresponding to each grid point in the local cardiac tissue model corresponding to the target band, to determine at least one current source restoration result in the local cardiac tissue model corresponding to the target band; wherein the local cardiac tissue model corresponding to the target band is determined based on prior knowledge of cardiac electrical activity; The determination module is used to determine the current source restoration results corresponding to the multiple target bands as the current source restoration results of the patient's heart.

10. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the method for restoring the current source in the magnetocardiocentrism inverse problem as described in any one of claims 1 to 8.

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