Heart model calibration method and system, electronic equipment and medium

Abstract

The invention provides a heart model calibration method and system, electronic equipment and a medium. The heart model calibration method comprises the following steps: acquiring to-be-calibrated magnetocardiogram data by utilizing a sensor array; processing the to-be-calibrated magnetocardiogram data by using a time sequence so as to identify an R moment corresponding to a ventricular depolarization peak value; calculating based on the spatial distribution of the sensor array to obtain the direction of an equivalent current dipole at the R moment; and constructing a rotation matrix according to the long axis direction of the general heart model and the direction of the equivalent current dipole at the R moment, and calibrating the heart model corresponding to the to-be-calibrated magnetocardiogram data by using the rotation matrix. According to the heart model calibration method, the magnetocardiogram data can be automatically calibrated, and an accurate personalized heart model can be generated without depending on individual historical physiological data.

Description

Technical Field

[0001] This application belongs to the field of cardiac magnetic data processing technology, and relates to a cardiac model calibration method, and particularly to a cardiac model calibration method, system, electronic device and medium. Background Technology

[0002] Magnetocardiography (MCG), as an emerging non-invasive functional imaging technique, has attracted significant attention in cardiac research due to its unique advantages of being radiation-free, non-contact, and possessing high spatial resolution. It reflects the physiological and pathological state of the heart by detecting the weak magnetic field generated by the heart's electrical activity, i.e., the magnetocardiogram (MCG) signal. One of its core scientific problems is the so-called "inverse magnetocardiogram problem"—that is, based on electromagnetic field theory, reversing the flow of magnetocardiogram data collected from the body surface to locate and reconstruct the equivalent current source inside the heart. Successfully solving this problem is of great significance for critical clinical tasks such as accurately diagnosing arrhythmia lesions and assessing ischemic areas of the myocardium.

[0003] Currently, mainstream methods for solving the inverse cardiac magnetic problem heavily rely on individualized cardiac anatomical information. Typically, a three-dimensional anatomical model of the patient's heart needs to be obtained beforehand using medical imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI), and then the current source can be located within the source space defined by this geometric model. However, this reliance on prior anatomical information constitutes a significant bottleneck in practical clinical applications. For most individuals undergoing health checkups or first-time medical visits, historical imaging data is often unavailable, thus failing to provide reliable and accurate predictive evidence for clinical decision-making and limiting its widespread application in general screening and early diagnosis scenarios. Summary of the Invention

[0004] This application provides a cardiac model calibration method, system, electronic device, and medium to address the problems of insufficient applicability of general models and over-reliance on individual historical physiological data in the prior art.

[0005] In a first aspect, this application provides a method for calibrating a cardiac model. The method includes: acquiring magnetic field data to be calibrated using a sensor array; processing the magnetic field data using a time series analysis to identify time R corresponding to the peak of ventricular depolarization; calculating the direction of the equivalent current dipole at time R based on the spatial distribution of the sensor array; and constructing a rotation matrix according to the major axis direction of a general cardiac model and the direction of the equivalent current dipole at time R, thereby calibrating the cardiac model corresponding to the magnetic field data to be calibrated using the rotation matrix.

[0006] In this application, time R is identified using the magnetocardiogram (MCC) data to be calibrated. A rotation matrix is ​​constructed based on the direction of the equivalent current dipole at time R and the major axis of a general heart model. This rotation matrix is ​​then used to calibrate the heart model corresponding to the MCC data. This heart model calibration method can automatically calibrate MCC data, generating accurate personalized heart models without relying on individual historical physiological data.

[0007] In one implementation of the first aspect, constructing a rotation matrix based on the major axis direction of the universal heart model and the direction of the equivalent current dipole at time R includes: obtaining a heart point set based on the magnetocardiogram data to be calibrated; analyzing the heart point set using principal component analysis to obtain a unit vector of the major axis direction of the universal heart model; calculating a universal offset angle based on the unit vector of the major axis direction of the universal heart model; calculating a target rotation angle based on the universal offset angle and the direction of the equivalent current dipole at time R; and constructing the rotation matrix using the target rotation angle.

[0008] In one implementation of the first aspect, the cardiac model calibration method further includes: performing a dot product between the transpose of the rotation matrix and the cardiac point set to obtain a calibrated cardiac point set; and using the calibrated cardiac point set to calibrate the cardiac model corresponding to the magnetic cardiomyography data to be calibrated.

[0009] In one implementation of the first aspect, the formula for calculating the rotation matrix is: ,in, For rotation matrix, Rotate by the target angle.

[0010] In one implementation of the first aspect, the calculation based on the spatial distribution of the sensor array to obtain the direction of the equivalent current dipole at time R includes: obtaining the magnetogram corresponding to time R; performing gradient calculation based on the magnetogram corresponding to time R to obtain the point with the maximum point source intensity; obtaining the current direction with the maximum point source intensity corresponding to the point with the maximum point source intensity; and performing vector calculation based on the current direction with the maximum point source intensity to obtain the actual offset angle of the heart as the direction of the equivalent current dipole at time R.

[0011] In one implementation of the first aspect, processing the magnetic cardiomyocyte data to be calibrated using a time series to identify the R-time corresponding to the ventricular depolarization peak includes: processing the magnetic cardiomyocyte data to be calibrated using a peak detection algorithm to identify the R-time corresponding to the ventricular depolarization peak.

[0012] In one implementation of the first aspect, acquiring the magnetic field data to be calibrated using a sensor array further includes: acquiring multi-channel magnetic field data of the heart using a superconducting quantum interference device; and preprocessing the multi-channel magnetic field data of the heart to obtain the magnetic field data to be calibrated.

[0013] Secondly, this application provides a cardiac model calibration system. The cardiac model calibration system includes: a data acquisition module for acquiring cardiac magnetic data to be calibrated using a sensor array; a data processing module for processing the cardiac magnetic data to be calibrated using time series analysis to identify time R corresponding to the peak of ventricular depolarization; a direction calculation module for calculating the direction of the equivalent current dipole at time R based on the spatial distribution of the sensor array; and a calibration processing module for constructing a rotation matrix based on the major axis direction of a general cardiac model and the direction of the equivalent current dipole at time R, so as to calibrate the cardiac model corresponding to the cardiac magnetic data to be calibrated using the rotation matrix.

[0014] Thirdly, this application provides an electronic device. The electronic device includes: a memory for storing a computer program; and a processor for executing the computer program stored in the memory to cause the electronic device to perform the cardiac model calibration method as described in any one of the first aspects.

[0015] Fourthly, this application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the cardiac model calibration method described in any one of the first aspects.

[0016] As described above, the cardiac model calibration method, system, electronic device, and medium described in this application have the following beneficial effects:

[0017] By utilizing acquired magnetocardiogram data and a general cardiac model, real-time calibration of individual cardiac models can be achieved. This method can obtain accurate individual cardiac models without requiring historical physiological data of individuals, overcoming the limitations of existing magnetocardiogram analysis and improving the source localization accuracy of the inverse magnetocardiogram problem, thereby enabling high-precision prediction of heart disease risk. Attached Figure Description

[0018] Figure 1 The diagram shown illustrates an application scenario of the cardiac model calibration method described in this application.

[0019] Figure 2 This diagram illustrates the structure of the mid-cloud interaction scenario in these implementation methods.

[0020] Figure 3 The diagram shown is a flowchart of the cardiac model calibration method described in the embodiments of this application.

[0021] Figure 4 The diagram shown is a flowchart of the cardiac model calibration method described in the embodiments of this application.

[0022] Figure 5A The diagram shown is a schematic representation of the universal left ventricular model described in the embodiments of this application.

[0023] Figure 5B The diagram shown is a set of calibrated heart models as described in the embodiments of this application.

[0024] Figure 5C The diagram shown is a set of calibrated heart models as described in the embodiments of this application.

[0025] Figure 6 The diagram shown is a flowchart of the cardiac model calibration method described in the embodiments of this application.

[0026] Figure 7 The diagram shown is a structural schematic of the cardiac model calibration system described in an embodiment of this application.

[0027] Figure 8 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application.

[0028] Component designation explanation

[0029] 1 Magnetocardiogram Analysis Device 11 Magnetocardiogram (MCC) acquisition equipment 12 Local processor 13 Display terminal 2 End-to-Cloud Interaction System 20 terminal 21 cloud server 100 Heart model calibration system 110 Data acquisition module 120 Data processing module 130 Direction Calculation Module 140 Calibration processing module 800 electronic devices 810 memory 820 processor 830 monitor S11~S14 step S131~S134 step S141~S145 step Detailed Implementation

[0030] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0031] It should be noted that in the embodiments of this application, the words "optionally" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "optionally" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "optionally" or "for example" is intended to present the relevant concepts in a specific manner.

[0032] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0033] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0034] Magnetocardiography (MCG) is a radiation-free, non-contact, high spatial resolution non-invasive cardiac detection technology. The inverse magnetocardiogram (IMC) problem refers to the rapid reconstruction of the cardiac magnetic data from the MCG into an internal cardiac current source using electromagnetic principles. This is of great significance for the detection of arrhythmias and myocardial ischemia. Typical methods for locating the source of the IMC source first involve building a cardiac model based on medical images such as CT or MRI, and then locating the source based on a set of points within the heart. However, for individuals undergoing physical examinations or first-time visits, there are no available medical images to construct a cardiac model. If a generic cardiac model is used, variations in the heart's deflection angle may affect the localization of the current source in the IMC, making accurate predictions impossible.

[0035] At least to address the aforementioned problems, embodiments of this application provide a method for calibrating a cardiac model. The method includes: acquiring cardiac magnetic data to be calibrated using a sensor array; processing the cardiac magnetic data to be calibrated using a time series to identify time R corresponding to the peak of ventricular depolarization; calculating the direction of the equivalent current dipole at time R based on the spatial distribution of the sensor array; and constructing a rotation matrix according to the major axis direction of a general cardiac model and the direction of the equivalent current dipole at time R, using the rotation matrix to calibrate the cardiac model corresponding to the cardiac magnetic data to be calibrated.

[0036] In this embodiment, time R is identified using the magnetocardiogram data to be calibrated. A rotation matrix is ​​constructed based on the direction of the equivalent current dipole at time R and the major axis of the general heart model. This rotation matrix is ​​then used to calibrate the heart model corresponding to the magnetocardiogram data to be calibrated. The heart model calibration method of this application can automatically calibrate magnetocardiogram data, generating accurate personalized heart models without relying on individual historical physiological data.

[0037] Figure 1 This diagram illustrates an application scenario of the cardiac model calibration method described in this application. The magnetocardiogram analysis device 1 can be used to implement the cardiac model calibration method provided in this application embodiment, but the application scenarios of the cardiac model calibration method provided in this application embodiment are not limited to this. Figure 1 The magnetocardiogram analysis device 1 shown is as follows. Figure 1 As shown, the magnetocardiogram (MCG) analysis device 1 includes a MCG acquisition device 11, a local processor 12, and a display terminal 13. The cardiac model calibration method provided in this embodiment can be applied to the local processor 12.

[0038] in, Figure 1 The local processor 12 can be a single local processor, a cluster of multiple local processors, or a cloud computing center, etc., and is not specifically limited here. Although Figure 1 Only one magnetocardiogram (MCC) acquisition device 11, one local processor 12, and one display terminal 13 are shown in the diagram, but it should be understood that... Figure 1 The examples in this paper are only for understanding this solution. The specific number of local processors 12 and display terminals 13 should be flexibly determined based on the actual situation.

[0039] In some other implementations, the magnetocardiogram (MCG) analysis device 1 may not include a display terminal 13, but only a local processor 12 with display functionality and a MCG acquisition device 11. The cardiac model calibration method provided in this application embodiment can be applied to the local processor 12. The local processor 12 with display functionality may include tablet computers, laptops, PDAs, mobile phones, personal computers, and voice interaction devices, or it may be a monitoring device, a facial recognition device, etc., which are not limited here.

[0040] In some other implementations, the cardiac model calibration method described in this application can be applied to edge-cloud interaction scenarios. Figure 2 This diagram illustrates the structure of the mid-cloud interaction scenario in these implementation methods. For example... Figure 2 As shown, the terminal-cloud interaction system 2 includes a terminal 20 and a cloud server 21. The terminal 20 and the cloud server 21 can communicate with each other, and the communication method is not limited to wired or wireless.

[0041] The terminal 20 can be mobile or fixed. For example, it can be a wireless terminal or a wired terminal. A wireless terminal can refer to a device with wireless transceiver capabilities, which can be deployed indoors, outdoors, and in industrial workshops. The terminal 20 can be a mobile phone, tablet computer, laptop computer, etc., and is not limited thereto. The cloud server 21 can include one or more servers, or one or more processing nodes, or one or more virtual machines running on the server. The cloud server 21 can also be referred to as a server cluster, management platform, data processing center, etc., and is not limited thereto in this embodiment.

[0042] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0043] The following embodiments of this application provide a method for calibrating a cardiac model, which, for example, can be achieved through... Figure 1 The local processor 12 shown or Figure 2 The cloud server 21 shown is used to implement this. Figure 3 The diagram shown is a flowchart illustrating the cardiac model calibration method described in an embodiment of this application. Figure 3 As shown, the cardiac model calibration method includes steps S11 to S14.

[0044] Step S11: Use a sensor array to acquire the magnetocardiogram data to be calibrated.

[0045] Step S12: The time series data of the magnetic cardiomyocytes to be calibrated is processed to identify the R time corresponding to the peak of ventricular depolarization.

[0046] Step S13: Calculate the direction of the equivalent current dipole at time R based on the spatial distribution of the sensor array.

[0047] Step S14: Construct a rotation matrix based on the major axis direction of the general heart model and the direction of the equivalent current dipole at time R, so as to use the rotation matrix to calibrate the heart model corresponding to the magnetic cardiomyography data to be calibrated.

[0048] In some possible implementations, a high-sensitivity magnetic sensor array is used to acquire the cardiac magnetic data to be calibrated. For example, a superconducting quantum interference device placed in the chest region can be used to acquire multi-channel magnetic field data of the heart, and the data is preprocessed to obtain the cardiac magnetic data to be calibrated. According to knowledge of cardiac electrophysiology, at time R, the comprehensive vector of cardiac depolarization basically coincides with the direction of the heart's long axis. This comprehensive vector describes the total direction and intensity of the electric current generated by the depolarization of all myocardial cells at a given instant in the heart's electrical activity (depolarization). The cardiac magnetic data to be calibrated is processed using time series analysis, and the time signal analysis technique using a peak detection algorithm is used to identify the time R corresponding to the peak value of ventricular depolarization. The direction of the equivalent current dipole at time R is calculated based on the spatial distribution of the sensor array. The target rotation angle of the cardiac magnetic data to be calibrated is obtained according to the long axis direction of the general cardiac model and the direction of the equivalent current dipole at time R. A rotation matrix is ​​then constructed based on the target rotation angle to calibrate the cardiac model corresponding to the cardiac magnetic data to be calibrated.

[0049] In this embodiment, time R is identified using the magnetocardiogram data to be calibrated. A rotation matrix is ​​constructed based on the direction of the equivalent current dipole at time R and the major axis of the general heart model. This rotation matrix is ​​then used to calibrate the heart model corresponding to the magnetocardiogram data to be calibrated. The heart model calibration method of this application can automatically calibrate magnetocardiogram data, generating accurate personalized heart models without relying on individual historical physiological data.

[0050] Figure 4 The diagram shown is a flowchart illustrating the cardiac model calibration method described in an embodiment of this application. Figure 4 As shown, step S14 includes steps S141 to S145.

[0051] Step S141: Obtain the cardiac point set based on the magnetic cardiometry data to be calibrated.

[0052] Step S142: Analyze the heart point set using principal component analysis to obtain the unit vector along the major axis of the heart in the general heart model.

[0053] Step S143: Calculate the universal offset angle based on the unit vector of the long axis direction of the heart in the universal heart model.

[0054] Step S144: Calculate the target rotation angle based on the universal offset angle and the direction of the equivalent current dipole at time R.

[0055] Step S145: Construct a rotation matrix using the target rotation angle.

[0056] In some possible implementations, a cardiac point set is obtained based on the magnetocardiogram data to be calibrated. , where N is the number of heart point sets. Principal component analysis is used to analyze the heart point sets to obtain the unit vector along the major axis of a general heart model. ,in, , , These represent the vertical, horizontal, and anterior-posterior directions of the heart's long axis, respectively. The angles on the xoy plane are calculated based on the unit vector along the long axis of the heart in a universal heart model to obtain the universal offset angles. According to the general offset angle The direction of the equivalent current dipole at time R The difference is calculated to obtain the target rotation angle. A rotation matrix is ​​constructed using the target rotation angle, and then the rotation matrix is ​​used to calibrate the magnetocardiogram data to be calibrated.

[0057] In some other possible implementations, Figure 5A The diagram shown is a schematic representation of the universal left ventricular model described in an embodiment of this application. Figure 5A As shown, the red arrow in the universal left ventricular model points in the direction of the long axis of the left ventricle, and the universal offset angle of the left ventricular model is -47.3 degrees. Figure 5B The diagram shown is a set of calibrated heart models as described in the embodiments of this application. Figure 5B As shown, the calculated direction of the equivalent current dipole at time R is -55.6 degrees. The target rotation angle of 8.6 degrees is obtained by calculating the difference between the universal offset angle and the direction of the equivalent current dipole at time R. The direction of the equivalent current dipole at time R is then calibrated using this target rotation angle, rotating the heart clockwise by 8.6 degrees. The calibrated left ventricular model angle is -55.9 degrees, which roughly matches the subject's actual left ventricular angle of -58 degrees. Figure 5C The diagram shown is a set of calibrated heart models as described in the embodiments of this application. Figure 5C As shown, the calculated direction of the equivalent current dipole at time R is -42.9 degrees. Using the difference between the universal offset angle and the direction of the equivalent current dipole at time R, a target rotation angle of 4.1 degrees is obtained. The direction of the equivalent current dipole at time R is then calibrated using this target rotation angle, rotating the heart counterclockwise by 4.1 degrees. The calibrated left ventricular model angle is -43.2 degrees, which roughly matches the subject's actual left ventricular angle of -40.1 degrees.

[0058] In one embodiment of this application, the cardiac model calibration method further includes: performing a dot product between the transpose of the rotation matrix and the cardiac point set to obtain a calibrated cardiac point set; and using the calibrated cardiac point set to calibrate the cardiac model corresponding to the cardiac magnetic data to be calibrated.

[0059] In some possible implementations, the rotation matrix is ​​used. transpose matrix With heart point set Perform dot product processing to obtain the calibrated cardiac point set. The calibrated cardiac point set is used to update the cardiac model corresponding to the magnetic resonance imaging (MRI) data to be calibrated, in order to obtain a personalized cardiac model corresponding to the MRI data to be calibrated.

[0060] In one embodiment of this application, the formula for calculating the rotation matrix is:

[0061] ,

[0062] in, For rotation matrix, Rotate by the target angle.

[0063] Figure 6 The diagram shown is a flowchart illustrating the cardiac model calibration method described in an embodiment of this application. Figure 6 As shown, step S13 includes steps S131 to S134.

[0064] Step S131: Obtain the magnetogram corresponding to time R.

[0065] Step S132: Calculate the gradient based on the magnetogram corresponding to time R to obtain the point with the maximum point source intensity.

[0066] Step S133: Obtain the current direction with the highest point source intensity based on the point with the highest point source intensity.

[0067] Step S134: Perform vector calculation based on the direction of the current with the greatest point source intensity to obtain the actual offset angle of the heart as the direction of the equivalent current dipole at time R.

[0068] In some possible implementations, the magnetograph at time R is obtained. Gradient calculation is then performed based on the magnetograph at time R to identify the point source with the highest intensity. The formula for calculating the square of the magnetic field gradient is defined as follows:

[0069] ,

[0070] in, Let be the magnetic field strength measured in the z-direction, x-direction be the direction from right to left of the human body, y-direction be the direction from feet to head of the human body, and z-direction be the direction from back to chest of the human body. The gradient of the magnetic field is calculated by differentiating the magnetic field strength in the z-direction with respect to the y- and x-directions.

[0071] The direction of the current with the highest point source intensity is obtained from the point with the highest point source intensity. The formula for the direction of the current with the highest power source intensity is:

[0072] ,

[0073] in, , These are the unit vectors in the x and y directions, respectively. The value represents the magnetic field strength measured in the z-direction, the x-direction represents the direction from right to left of the human body, and the y-direction represents the direction from feet to head of the human body.

[0074] Vector calculations are performed based on the direction of the current with the greatest point source intensity to obtain the actual offset angle of the heart as the direction of the equivalent current dipole at time R. The formula for calculating the actual offset angle of the heart is:

[0075] ,

[0076] in, This represents the actual offset angle of the heart. The value represents the magnetic field strength measured in the z-direction, the x-direction represents the direction from right to left of the human body, and the y-direction represents the direction from feet to head of the human body.

[0077] In one embodiment of this application, processing the magnetic cardiomyocyte data to be calibrated using time series to identify the R-time corresponding to the peak value of ventricular depolarization includes: using a peak detection algorithm to process the magnetic cardiomyocyte data to be calibrated to identify the R-time corresponding to the peak value of ventricular depolarization.

[0078] In some possible implementations, time-series data processing is used to process the magnetocardiogram (MCG) data to be calibrated. Peak detection algorithms are employed in time signal analysis to identify the R-moment corresponding to the peak of ventricular depolarization. The R-moment corresponding to the peak of cardiac depolarization refers to the instant when the R wave reaches its highest point on the electrocardiogram. The R-moment corresponding to the peak of cardiac depolarization represents the moment when the main myocardial depolarization activity of the left ventricle reaches its zenith, and is a key benchmark for analyzing the sequence of cardiac electrical activation, performing image gating, and calculating various ECG parameters.

[0079] In one embodiment of this application, acquiring the cardiac magnetic data to be calibrated using a sensor array further includes: acquiring multi-channel magnetic field data of the heart using a superconducting quantum interference device; and preprocessing the multi-channel magnetic field data of the heart to obtain the cardiac magnetic data to be calibrated.

[0080] In some possible implementations, a superconducting quantum interference device (SQUID) is used to acquire multi-channel magnetic field data from the heart. This data is then preprocessed, such as through noise reduction, to obtain the cardiac magnetic field data to be calibrated. The SQUID is a sensor based on quantum mechanics principles capable of measuring extremely weak magnetic fields.

[0081] Figure 7 The diagram shown is a structural schematic of the cardiac model calibration system described in an embodiment of this application. Figure 7 As shown, the cardiac model calibration system 100 includes a data acquisition module 110, a data processing module 120, a direction calculation module 130, and a calibration processing module 140.

[0082] The data acquisition module 110 is used to acquire the magnetic heart data to be calibrated using a sensor array.

[0083] The data processing module 120 is used to process the time series of the magnetic cardiomyocyte data to be calibrated to identify the R time corresponding to the peak of ventricular depolarization.

[0084] The direction calculation module 130 is used to perform calculations based on the spatial distribution of the sensor array to obtain the direction of the equivalent current dipole at time R.

[0085] The calibration processing module 140 is used to construct a rotation matrix based on the major axis direction of the general heart model and the direction of the equivalent current dipole at time R, so as to use the rotation matrix to calibrate the heart model corresponding to the magnetic cardiomyography data to be calibrated.

[0086] In some possible implementations, the data acquisition module 110 is used to acquire the cardiac magnetic data to be calibrated using a high-sensitivity magnetic sensor array, such as using a superconducting quantum interference device placed in the chest region to acquire multi-channel magnetic field data of the heart, and preprocessing the data to obtain the cardiac magnetic data to be calibrated. According to knowledge of cardiac electrophysiology, at time R, the comprehensive vector of cardiac depolarization basically coincides with the direction of the heart's long axis. The comprehensive vector of cardiac depolarization describes the total direction and intensity of the electric current generated by the depolarization of all myocardial cells at a certain instant of cardiac electrical activity (depolarization). The data processing module 120 is used to process the cardiac magnetic data to be calibrated through time series processing, using peak detection algorithm time signal analysis technology to identify time R corresponding to the peak value of ventricular depolarization. The direction calculation module 130 is used to calculate based on the spatial distribution of the sensor array to obtain the direction of the equivalent current dipole at time R. The calibration processing module 140 is used to obtain the target rotation angle of the cardiac magnetic data to be calibrated according to the long axis direction of the general cardiac model and the direction of the equivalent current dipole at time R, and to construct a rotation matrix based on the target rotation angle for calibrating the cardiac model corresponding to the cardiac magnetic data to be calibrated.

[0087] In this embodiment, time R is identified using the magnetocardiogram data to be calibrated. A rotation matrix is ​​constructed based on the direction of the equivalent current dipole at time R and the major axis of the general heart model. The rotation matrix is ​​then used to calibrate the heart model corresponding to the magnetocardiogram data to be calibrated. The heart model calibration system 100 of this application can automatically calibrate magnetocardiogram data and generate accurate personalized heart models without relying on individual historical physiological data.

[0088] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0089] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0090] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0091] This application also provides an electronic device. Figure 8 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application. Figure 8 As shown, in this embodiment, the electronic device 800 includes a memory 810 and a processor 820.

[0092] The memory 810 is used to store computer programs; preferably, the memory 810 includes various media that can store program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk.

[0093] Specifically, memory 810 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. Electronic device 800 may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 810 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application. It is understood that memory 810 may be volatile memory or non-volatile memory, or both. Non-volatile memory may be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memories.

[0094] The processor 820 is connected to the memory 810 and is used to execute the computer program stored in the memory 810 so that the electronic device 800 performs the heart model calibration method described in any embodiment of this application.

[0095] Optionally, the processor 820 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0096] Optionally, the electronic device 800 in this embodiment may further include a display 830. The display 830 is communicatively connected to the memory 810 and the processor 820, and is used to display the relevant graphical user interface (GUI) of the cardiac model calibration method described in this application embodiment.

[0097] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the program implements the cardiac model calibration method described in any embodiment of this application. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0098] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0099] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0100] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for calibrating a heart model, characterized in that, include: The magnetic field data to be calibrated is acquired using a sensor array. The magnetocardiogram data to be calibrated is processed using time series analysis to identify the R time corresponding to the peak of ventricular depolarization. The direction of the equivalent current dipole at time R is obtained by calculating based on the spatial distribution of the sensor array. A rotation matrix is ​​constructed based on the major axis direction of the general heart model and the direction of the equivalent current dipole at time R, so as to calibrate the heart model corresponding to the magnetic cardiomyography data to be calibrated using the rotation matrix.

2. The cardiac model calibration method according to claim 1, characterized in that, The rotation matrix is ​​constructed based on the major axis direction of the general heart model and the direction of the equivalent current dipole at time R, including: Obtain a cardiac point set based on the magnetic cardiometry data to be calibrated; The heart point set was analyzed using principal component analysis to obtain the unit vector along the major axis of the heart in a general heart model. The universal offset angle is calculated based on the unit vector along the long axis of the heart in the universal heart model. The target rotation angle is calculated based on the general offset angle and the direction of the equivalent current dipole at time R. The rotation matrix is ​​constructed using the target rotation angle.

3. The cardiac model calibration method according to claim 2, characterized in that, Also includes: The heart point set is obtained by performing a dot product between the transpose of the rotation matrix and the heart point set. The calibrated cardiac point set is used to calibrate the cardiac model corresponding to the magnetic cardiac data to be calibrated.

4. The cardiac model calibration method according to claim 2, characterized in that, The formula for calculating the rotation matrix is: ， in, Let be a rotation matrix. Rotate by the target angle.

5. The cardiac model calibration method according to claim 1, characterized in that, The direction of the equivalent current dipole at time R is calculated based on the spatial distribution of the sensor array, including: Obtain the magnetogram corresponding to time R; Gradient calculation is performed based on the magnetogram corresponding to time R to obtain the point with the maximum point source intensity. Based on the point with the highest point source intensity, obtain the current direction corresponding to the point source intensity with the highest current. Vector calculations are performed based on the direction of the current with the greatest intensity from the point source to obtain the actual offset angle of the heart as the direction of the equivalent current dipole at time R.

6. The cardiac model calibration method according to claim 1, characterized in that, The time series analysis of the magnetocardiogram data to be calibrated was used to identify the R-time corresponding to the peak of ventricular depolarization, including: The magnetic field data to be calibrated is processed using a peak detection algorithm to identify the R time corresponding to the peak value of ventricular depolarization.

7. The cardiac model calibration method according to claim 1, characterized in that, Acquiring magnetocardiogram data to be calibrated using a sensor array also includes: Acquiring multi-channel magnetic field data of the heart using superconducting quantum interference devices; The multi-channel magnetic field data of the heart is preprocessed to obtain the cardiac magnetic data to be calibrated.

8. A cardiac model calibration system, characterized in that, include: The data acquisition module is used to acquire the magnetic field data to be calibrated using a sensor array; The data processing module is used to process the magnetic cardiomyography data to be calibrated using time series data to identify the R time corresponding to the peak of ventricular depolarization. The orientation calculation module is used to calculate the orientation of the equivalent current dipole at time R based on the spatial distribution of the sensor array. The calibration processing module is used to construct a rotation matrix based on the major axis direction of the general heart model and the direction of the equivalent current dipole at time R, so as to use the rotation matrix to calibrate the heart model corresponding to the magnetic cardiomyography data to be calibrated.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing a computer program stored in the memory to cause the electronic device to perform the cardiac model calibration method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the cardiac model calibration method according to any one of claims 1 to 7.

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