Sensor array error evaluation method for magnetocardiogram source imaging

Abstract

The invention discloses a sensor array error evaluation method for magnetocardiogram source imaging, which relates to the technical field of biomedical signal analysis, and comprises the following steps: performing tissue segmentation and three-dimensional reconstruction on a CT image to generate a CT model; carrying out registration on the 3D scanning model and the CT model; error types existing in the information of the magnetocardiogram sensor array are set; simulating and generating a magnetocardiogram signal containing error influence; and evaluating the influence of the error on the source imaging performance. The method can systematically and comprehensively quantify the influence of crosstalk, gain error, sensitive axis angle error and position error on the imaging performance of the magnetocardiogram source and three core dimensions of positioning precision to construct a unified multi-dimensional evaluation system, overcomes the defect of lack of comprehensive evaluation standards in the prior art, can also provide priority guidance for error calibration of an MCG system, and improves the accuracy of the MCG system. And the imaging accuracy, the clinical availability and the application value are obviously improved.

Description

Technical Field

[0001] This invention relates to the field of biomedical signal analysis technology, and in particular to a method for evaluating sensor array errors for cardiac magnetic field imaging. Background Technology

[0002] Magnetocardiography (MCG) is a non-invasive detection method that obtains information about cardiac electrophysiological activity by measuring the weak magnetic field generated by the heart. This technology has significant applications in the diagnosis and surgical planning of cardiac diseases. A typical MCG system uses a multi-channel magnetometer array to record the magnetic field signal on the surface of the thorax to obtain the spatiotemporal characteristics of cardiac electrical activity. However, in actual measurements, the sensor array is susceptible to four key types of errors: crosstalk between sensors, gain error, sensitive axis angle deviation, and positional error. These errors can degrade the quality of the magnetic field signal or cause distortion of the lead field matrix, thus severely affecting the accuracy of cardiac source imaging. Currently, research on MCG array errors is not systematic, especially lacking a comprehensive examination of the above four types of errors, and failing to comprehensively evaluate their impact from multiple key dimensions such as reconstruction accuracy, spatial resolution, and related source localization accuracy. This deficiency means that MCG systems lack reliable error calibration criteria in practical use, limiting further improvements in their imaging performance and their clinical translation.

[0003] In the process of implementing the embodiments of the present invention, the inventors discovered that the prior art has at least the following problems or defects: due to the lack of an evaluation method based on real human anatomy that can systematically quantify the impact of various array errors on the multidimensional performance of cardiac source imaging, the existing MCG system is difficult to effectively calibrate and optimize key errors, thereby restricting its clinical application effect and promotion value. Summary of the Invention

[0004] In view of the problems existing in the sensor array error assessment methods for magnetocardiography, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is to address the lack of an evaluation method for existing magnetocardiography systems that is based on real human anatomy and can systematically quantify the impact of errors from multiple sensor arrays on the multidimensional performance of cardiac imaging.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a sensor array error assessment method for cardiac magneto-optical imaging, which includes performing a CT scan on the upper body of a subject with positioning markers attached, acquiring CT images, performing tissue segmentation and three-dimensional reconstruction on the CT images, and generating a CT model.

[0008] Three-dimensional data of the subject's torso surface with a magnetocardiogram sensor array and positioning markers were acquired using a 3D scanner, a 3D scanning model was established, and the 3D scanning model was registered with the CT model.

[0009] Based on the registered magnetocardiogram sensor array information, the error types present in the magnetocardiogram sensor array information are defined.

[0010] Based on the registered heart-lung-trunk volume conductor model, combined with the cardiac equivalent current dipole source model, and considering the errors present in the information of the magnetic sensor array, the boundary element method is used to calculate the lead field matrix and simulate the generation of magnetic signals containing the influence of errors.

[0011] Based on the lead field matrix and simulated magnetic-cardiographic signals that include the influence of errors, an inverse problem-solving algorithm is used to perform cardiac source imaging and evaluate the impact of errors on source imaging performance.

[0012] As a preferred embodiment of the sensor array error assessment method for magnetocardiography imaging described in this invention, the positioning markers include those attached to the side waist of the subject. The side waist markers are used to provide a registration reference during CT scanning and 3D scanning model modeling. The settings can reduce the impact of abdominal fluctuations caused by breathing on the model accuracy and are not affected by the installation position of the magnetocardiography acquisition plate.

[0013] As a preferred embodiment of the sensor array error evaluation method for magnetocardiography imaging described in this invention, the registration of the 3D scanning model with the CT model is achieved through an iterative nearest-point algorithm.

[0014] As a preferred embodiment of the sensor array error evaluation method for magnetocardiography source imaging described in this invention, the error types include crosstalk, gain error, sensitive axis angle error, and position error.

[0015] The steps involved in setting the errors present in the magnetocardiogram sensor array information are as follows:

[0016] The crosstalk is used to simulate the magnetic field coupling effect between adjacent units when multiple sensors work synchronously. The horizontal setting is 1% to 5%, and it follows the attenuation law. The point with the smallest distance between multiple sensors bears the full crosstalk. The crosstalk effect decreases with the increase of the sensor distance in an inverse cubic relationship.

[0017] The gain error is used to reflect the difference in response amplitude of each sensor channel, and the measurement signal is simulated by a uniformly distributed random scaling factor with a mean of zero and a maximum deviation of 1% to 5%.

[0018] The sensitive axis angle error is used to represent the orientation deviation of the sensitive axis of multiple sensors. A fixed angle offset of 1° to 5° is set for each sensor, and the direction of the fixed angle offset is randomly generated in the tangential plane.

[0019] The positional error is used to simulate the installation deviation of the multi-sensor array, which is achieved by translating the multi-sensor array away from the body surface by 1 mm to 5 mm along the normal direction.

[0020] As a preferred embodiment of the sensor array error assessment method for magnetocardiography imaging according to the present invention, the generation process of the magnetocardiographic signal includes the following steps:

[0021] Using the M grid vertices of the heart as the source space, each vertex is considered as an equivalent current dipole. Activated current dipoles are selected from the source space, and three cases are considered: single current dipoles, paired current dipoles, and current dipoles with coupling relationships.

[0022] The single current dipole includes a source activation time series using a zero-mean Gaussian signal.

[0023] The paired current dipoles include those with spacings of 4 mm, 6 mm and 8 mm, and each uses a mutually orthogonal zero-mean Gaussian signal as the activation sequence.

[0024] The coupled current dipoles include randomly selecting positions in the source space after determining the number of sources, and constructing a source activation sequence through a sinusoidal mixed signal;

[0025] The boundary element method is used to calculate the lead field matrix corresponding to the current dipole. If there is an error type in the information of the magnetocardiogram sensor array, the error parameter is included in the calculation process to obtain the lead field matrix containing the error.

[0026] Based on the source activation sequence and the calculated lead field matrix, a preliminary magnetocardiogram measurement signal is generated, and zero-mean Gaussian noise is added to simulate the actual sensor noise.

[0027] If there is a set crosstalk or gain error, it will be applied to the noisy measurement signal; the output includes the magnetocardiogram measurement signal affected by array error and noise.

[0028] As a preferred embodiment of the sensor array error evaluation method for magnetocardiography source imaging described in this invention, the inverse problem solving algorithm includes a linearly constrained minimum variance beamformer and an alternating projection algorithm.

[0029] The linearly constrained minimum variance beamformer is used to reconstruct the source time series of a single current dipole and pairs of independent current dipoles. The formula for calculating the spatial filter weight vector is as follows:

[0030] ,

[0031] in, Indicates position The lead field vector corresponding to the unit dipole. The source time series is reconstructed by passing the filter through the covariance matrix of the measured signal.

[0032] The alternating projection algorithm is used to locate the source of multiple current dipoles with coupling relationships, and the source position is estimated through iterative optimization.

[0033] The iteration terminates when the change in the Euclidean distance of all estimated source locations in two consecutive iterations is less than a set threshold, and the final source location estimation result is output.

[0034] As a preferred embodiment of the sensor array error evaluation method for magnetocardiography source imaging described in this invention, the impact of the evaluation error on the source imaging performance is systematically evaluated using corresponding indicators for reconstruction accuracy, spatial resolution, and related source localization accuracy.

[0035] The reconstruction accuracy is used to evaluate the reconstruction effect of the single-dipole source time series, and the correlation coefficient is used. And relative mean square error are used as quantitative indicators;

[0036] in, The Pearson correlation coefficient represents the relationship between the reconstructed time series and the original time series. The relative mean squared error is used to measure the normalized reconstruction error, and its calculation formula is as follows:

[0037]

[0038] in It is a normalized reconstructed time series. It is a normalized real time series. The larger the value, the smaller the RMSE, indicating higher source reconstruction accuracy;

[0039] The spatial resolution is used to evaluate the system's ability to distinguish between adjacent dipole sources in space, as measured by the Pearson correlation coefficient between the reconstructed time series of the two sources. As a quantitative indicator, the separability threshold is set as follows: This corresponds to a shared variance of less than 50% between the two reconstructed signals;

[0040] like If so, then the source pair is considered separable. The smaller the value, the higher the spatial resolution of the system;

[0041] The correlation source positioning accuracy is used to evaluate the accuracy of the correlation source's position estimation, with the local positioning error between the estimated position and the true position as the indicator. The calculation formula is as follows:

[0042]

[0043] in, Indicates the first An estimated Euclidean distance between a source and its nearest actual source. The set of source indexes that were successfully detected. For its quantity, the smaller the LE value, the higher the accuracy of the relevant source location.

[0044] Secondly, embodiments of the present invention provide a sensor array error assessment system for cardiac magnetic source imaging, comprising: a CT modeling module, which performs CT scans on the upper body of a subject with positioning markers attached, acquires CT images, performs tissue segmentation and three-dimensional reconstruction on the CT images, and generates a CT model;

[0045] The surface scanning and registration module acquires three-dimensional data of the subject's torso surface with a magnetocardiogram sensor array and positioning markers using a 3D scanner, establishes a 3D scanning model, and registers the 3D scanning model with the CT model.

[0046] The error modeling module, based on the registered magnetocardiogram sensor array information, sets the error types present in the magnetocardiogram sensor array information.

[0047] The magnetic heart signal simulation module is based on the registered heart-lung-trunk volume conductor model, combined with the cardiac equivalent current dipole source model, and sets the errors existing in the magnetic heart sensor array information. It uses the boundary element method to calculate the lead field matrix and simulate the generation of magnetic heart signals including the influence of errors.

[0048] The source imaging and performance evaluation module is based on the lead field matrix and simulated magnetic-cardiac signals that include the influence of errors. It uses an inverse problem solving algorithm to perform cardiac source imaging and evaluate the impact of errors on source imaging performance.

[0049] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the steps of the above-described sensor array error evaluation method for magnetocardiogram imaging.

[0050] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any of the steps of the above-described sensor array error evaluation method for magnetocardiogram imaging.

[0051] The beneficial effects of this invention are as follows: This invention provides a method for evaluating the error of a magnetic sensor array based on a real human multi-tissue model. It can systematically and comprehensively quantify the impact of crosstalk, gain error, sensitive axis angle error, and position error on the imaging performance of the magnetic sensor array. It also constructs a unified multi-dimensional evaluation system from three core dimensions: reconstruction accuracy, spatial resolution, and related source localization accuracy. This not only overcomes the lack of comprehensive evaluation standards in the existing technology, but also provides priority guidance for error calibration of the MCG system, significantly improving its imaging accuracy, clinical usability, and application value. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0053] Figure 1 This is a flowchart of a sensor array error evaluation method for magnetocardiography source imaging provided in an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of a sensor array error assessment method for magnetocardiography source imaging, provided as an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of the structure of a medium for a sensor array error assessment method for magnetocardiography provided in an embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram of a computing device for a sensor array error evaluation method for magnetocardiography source imaging, provided in an embodiment of the present invention. Detailed Implementation

[0057] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0058] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0060] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0061] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0062] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0063] Example

[0064] Reference Figures 1-4 This is the first embodiment of the present invention, which provides a sensor array error assessment method for magnetocardiography, comprising:

[0065] CT scans were performed on the upper body of subjects with positioning markers to obtain CT images. Tissue segmentation and three-dimensional reconstruction were performed on the CT images to generate CT models.

[0066] Three-dimensional data of the subject's torso surface with a magnetocardiogram sensor array and positioning markers were acquired using a 3D scanner, a 3D scanning model was established, and the 3D scanning model was registered with the CT model.

[0067] Based on the registered magnetocardiogram sensor array information, the error types present in the magnetocardiogram sensor array information are defined.

[0068] Based on the registered heart-lung-trunk volume conductor model, combined with the cardiac equivalent current dipole source model, and considering the errors present in the information of the magnetic sensor array, the boundary element method is used to calculate the lead field matrix and simulate the generation of magnetic signals containing the influence of errors.

[0069] Based on the lead field matrix and simulated magnetic-cardiographic signals that include the influence of errors, an inverse problem-solving algorithm is used to perform cardiac source imaging and evaluate the impact of errors on source imaging performance.

[0070] Step 1: Perform a CT scan on the upper body of the subject with the positioning markers to obtain CT images; perform tissue segmentation and three-dimensional reconstruction on the CT images to generate a personalized mesh model including the heart, lungs and trunk.

[0071] CT scan is a technology that uses X-rays to scan the human body from different angles and then uses computer processing to generate detailed images of the internal structure of the human body.

[0072] CT image segmentation and meshing refers to separating the heart, lungs, and trunk regions from CT images and converting them into three-dimensional mesh models. This step typically involves medical image processing techniques, such as image segmentation algorithms and 3D reconstruction techniques. During image segmentation, threshold-based methods or more advanced machine learning algorithms can be used to distinguish different tissue types. Meshing involves converting the segmented image into a 3D model composed of multiple small mesh units. The size and shape of these mesh units can be adjusted as needed to balance model accuracy and computational efficiency.

[0073] Locating markers are typically made of materials easily identifiable in CT images, such as metal balls or special marking tape. These markers need to be small enough to avoid causing discomfort to the subject or affecting the accuracy of the scan results. When setting the markers, it is necessary to ensure they are evenly distributed along the subject's lateral waist to provide sufficient reference information during subsequent model registration.

[0074] Step 2: Obtain three-dimensional data of the subject's torso surface with a magnetocardiogram sensor array and positioning markers using a 3D scanner, establish a 3D scanning model, and register this model with the CT model obtained in Step 1 to obtain a registered overall model integrating sensor array information.

[0075] 3D scanners are optical scanners, laser scanners, or other high-precision, high-speed scanners. These devices can quickly acquire the surface geometry information of the subject's torso and register it with the CT model to ensure the accuracy of the final model.

[0076] The registration process first establishes a three-dimensional coordinate system for the CT model. The subject's coordinates within this system are determined by positioning markers. Then, the coordinate information of the sensor array is obtained using the scanning model. Finally, the sensor coordinate system is transformed to the CT model coordinate system. This process can be achieved using methods such as feature matching or the Iterative Closest Point (ICP) algorithm.

[0077] Step 3: Based on the registered magnetocardiogram sensor array information in Step 2, set the possible error types and their levels or amplitudes in the array. Error types include crosstalk, gain error, sensitive axis angle error and position error.

[0078] The level or magnitude of various array errors are set as follows:

[0079] Crosstalk is used to simulate the magnetic field coupling effect between adjacent units when multiple sensors work synchronously. Its level is set to 1% to 5%, and it follows the following attenuation law: the sensor spacing is the smallest and bears the full crosstalk. The crosstalk effect decreases with the increase of the sensor spacing in an inverse cubic relationship.

[0080] Gain error is used to reflect the difference in response amplitude of each sensor channel. It is simulated by a uniformly distributed random scaling factor with a mean of zero and a maximum deviation of 1% to 5%.

[0081] Angular error is used to represent the orientation deviation of the sensor's sensitive axis relative to the ideal direction. A fixed angular offset of 1° to 5° is set for each sensor, and the offset direction is randomly generated in the tangential plane.

[0082] Position error is used to simulate the overall installation deviation of the sensor array, which is achieved by translating the entire array away from the body surface by 1 mm to 5 mm along the normal direction.

[0083] Step 4: Based on the registered heart-lung-trunk volume conductor model obtained in Step 2, combined with the cardiac equivalent current dipole source model and the array error set in Step 3, the boundary element method is used to calculate the lead field matrix and simulate the generation of the magnetic-cardiac signal containing the influence of the error.

[0084] The generation process of magnetic field signals includes the following sub-steps:

[0085] (4.1) Take the M grid vertices of the heart as the source space, and treat each vertex as an equivalent current dipole; select the active current dipole from the source space, and consider the following three cases respectively:

[0086] --For a single current dipole, a zero-mean Gaussian signal is used as its source activation time series;

[0087] --For paired current dipoles, the spacing is set to 4 mm, 6 mm and 8 mm respectively, and mutually orthogonal zero-mean Gaussian signals are used as activation sequences.

[0088] --For current dipoles with coupling relationships, after determining the number of sources, randomly select positions in the source space, and construct a source activation sequence with a set coupling strength through sinusoidal mixed signals;

[0089] (4.2) The boundary element method is used to calculate the lead field matrix corresponding to the current dipole; if there is an angle error or position error, the error parameter is included in the calculation process to obtain the lead field matrix with error.

[0090] (4.3) Based on the source activation sequence in step (4.1) and the lead field matrix obtained in step (4.2), a preliminary magnetocardiogram measurement signal is generated; then zero-mean Gaussian noise is added to simulate sensor noise; if there is crosstalk or gain error set in step 3, it is further applied to the noisy measurement signal.

[0091] (4.4) Output the magnetocardiogram measurement signal including array error and noise.

[0092] Step 5: Based on the lead field matrix and simulated magnetic field signal obtained in Step 4, cardiac source imaging is performed using an inverse problem solving algorithm. The impact of various types of errors on source imaging performance is systematically evaluated from three dimensions: reconstruction accuracy, spatial resolution, and related source localization accuracy.

[0093] Inverse problem solving algorithms include linearly constrained minimum variance beamformers and alternating projection algorithms;

[0094] A linearly constrained minimum variance beamformer is used for source time series reconstruction of single current dipoles and pairs of independent current dipoles. Its spatial filter weight vector is calculated by the following formula:

[0095] ,

[0096] in, Indicates position The lead field vector corresponding to the unit dipole. The source time series is reconstructed by passing the filter through the covariance matrix of the measured signal.

[0097] The alternating projection algorithm is used to locate the source of multiple current dipoles with coupling relationship. It estimates the source position by iterative optimization. The iteration terminates when the change in the Euclidean distance of all estimated source positions in two adjacent iterations is less than a set threshold, and the final source position estimation result is output.

[0098] Furthermore, the three dimensions are evaluated systematically using corresponding indicators:

[0099] (5.1) Reconstruction accuracy is used to evaluate the reconstruction effect of the single dipole source time series, and the correlation coefficient is used. And relative mean square error are used as quantitative indicators. Among them, The Pearson correlation coefficient represents the relationship between the reconstructed time series and the true time series. The relative mean squared error is used to measure the normalized reconstruction error. Calculate, where It is a normalized reconstructed time series. It is a normalized real time series. The larger the value, the smaller the RMSE, indicating higher source reconstruction accuracy;

[0100] (5.2) Spatial resolution is used to evaluate the system’s ability to distinguish between adjacent dipole sources in space, as measured by the Pearson correlation coefficient between the reconstructed time series of the two sources. As a quantitative indicator, the separability threshold is set as follows: This corresponds to a shared variance of less than 50% between the two reconstructed signals; if If so, then the source pair is considered separable. The smaller the value, the higher the spatial resolution of the system;

[0101] (5.3) Correlated source positioning accuracy is used to evaluate the accuracy of the location estimation of related sources. The local positioning error between the estimated position and the true position is used as the indicator, and its calculation formula is as follows: ,in Indicates the first An estimated Euclidean distance between a source and its nearest actual source. The set of source indexes that were successfully detected. The smaller the LE value, the higher the accuracy of the relevant source location.

[0102] In a preferred embodiment, a sensor array error assessment system for magnetocardiography (MCG) source imaging includes a surface scanning and registration module, which acquires three-dimensional data of the subject's torso surface with a MCG sensor array and positioning markers using a 3D scanner, establishes a 3D scanning model, and registers the 3D scanning model with a CT model; an error modeling module, which sets the error types present in the MCG sensor array information based on the registered information; a MCG signal simulation module, which calculates the lead field matrix using the boundary element method based on the registered heart-lung-torso volume conductor model, combined with the cardiac equivalent current dipole source model, and the set errors present in the MCG sensor array information, and simulates the generation of MCG signals including the influence of errors; and a source imaging and performance evaluation module, which performs cardiac source imaging using an inverse problem solving algorithm based on the lead field matrix and the simulated MCG signals including the influence of errors, and evaluates the impact of errors on source imaging performance.

[0103] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0104] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be an LCD screen or an e-ink display screen. The input device of the computer device may be a touch layer covering the display screen, or buttons, a trackball, or a touchpad located on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0105] In summary, this invention provides a method for evaluating the error of a magnetic sensor array based on a real human multi-tissue model. It can systematically and comprehensively quantify the impact of crosstalk, gain error, sensitive axis angle error, and position error on the imaging performance of the magnetic sensor array. Furthermore, it constructs a unified multi-dimensional evaluation system from three core dimensions: reconstruction accuracy, spatial resolution, and related source localization accuracy. This not only overcomes the lack of comprehensive evaluation standards in existing technologies but also provides priority guidance for error calibration of the MCG system, significantly improving its imaging accuracy, clinical usability, and application value.

[0106] After introducing the method and system of exemplary embodiments of the present invention, the following references are made. Figure 3 A computer-readable storage medium according to exemplary embodiments of the present invention will be described, please refer to... Figure 3 The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it implements the steps described in the above-described method implementation. For example, it performs a CT scan on the upper body of a subject with positioning markers to acquire CT images, performs tissue segmentation and three-dimensional reconstruction on the CT images to generate a CT model; it acquires three-dimensional data of the subject's torso surface with a magnetocardiogram sensor array and positioning markers using a 3D scanner to establish a 3D scanning model, and registers the 3D scanning model with the CT model; based on the registered magnetocardiogram sensor array information, it sets the error type present in the magnetocardiogram sensor array information; based on the registered heart-lung-torso volume conductor model, combined with the cardiac equivalent current dipole source model, and the set errors present in the magnetocardiogram sensor array information, it uses the boundary element method to calculate the lead field matrix and simulates the generation of magnetocardiogram signals including the influence of errors; based on the lead field matrix and the simulated magnetocardiogram signals including the influence of errors, it uses an inverse problem solving algorithm to perform cardiac source imaging and evaluate the impact of errors on source imaging performance. The specific implementation methods of each step will not be repeated here.

[0107] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0108] After introducing the methods and media of exemplary embodiments of the present invention, the following references are made. Figure 4 A computational device for adaptive recovery of low-voltage power grid self-healing control according to an exemplary embodiment of the present invention.

[0109] Figure 4 A block diagram is shown of an exemplary computing device 40 suitable for implementing embodiments of the present invention. The computing device 40 may be a computer system or a server. Figure 4 The computing device 40 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0110] like Figure 4As shown, the components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0111] The computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 40, including volatile and non-volatile media, and removable and non-removable media.

[0112] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 4 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 via one or more data media interfaces. System memory 402 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 the present invention.

[0113] A program / utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 4024 typically perform the functions and / or methods described in the embodiments of the present invention.

[0114] The computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 405. Furthermore, the computing device 40 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 406. Figure 4 As shown, network adapter 406 communicates with other modules of computing device 40 (such as processing unit 401) via bus 403. It should be understood that, although... Figure 4 As not shown, it can be used in conjunction with computing device 40 with other hardware and / or software modules.

[0115] The processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402. For example, it performs a CT scan on the upper body of a subject with positioning markers to acquire CT images, performs tissue segmentation and three-dimensional reconstruction on the CT images to generate a CT model; it acquires three-dimensional data of the subject's torso surface with a magnetocardiogram sensor array and positioning markers using a 3D scanner to establish a 3D scanning model, and registers the 3D scanning model with the CT model; based on the registered magnetocardiogram sensor array information, it sets the error types present in the magnetocardiogram sensor array information; based on the registered heart-lung-torso volume conductor model, combined with the cardiac equivalent current dipole source model, and the set errors present in the magnetocardiogram sensor array information, it uses the boundary element method to calculate the lead field matrix and simulates the generation of magnetocardiogram signals containing the influence of errors; based on the lead field matrix and the simulated magnetocardiogram signals containing the influence of errors, it uses an inverse problem solving algorithm to perform cardiac source imaging and evaluate the impact of errors on source imaging performance.

[0116] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0117] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0120] If the functionality is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0121] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0122] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for sensor array error evaluation for cardiac magnetic source imaging, characterized by: The application relates to a sensor array error evaluation method for magnetocardiography source imaging. CT scanning is performed on the upper body of a subject with positioning mark points, CT images are obtained, and CT models are generated through tissue segmentation and three-dimensional reconstruction of the CT images; Three-dimensional data of the torso surface of a subject with a magnetocardiogram sensor array and positioning mark points are obtained through a 3D scanner, and a 3D scanning model is established; the 3D scanning model is registered with the CT model; Based on the magnetocardiogram sensor array information obtained after registration, the types of errors existing in the magnetocardiogram sensor array information are set; Based on the registered heart-lung-torso volume conductor model, combined with the heart equivalent current dipole source model and the errors existing in the magnetocardiogram sensor array information, a boundary element method is used to calculate the lead field matrix and simulate the generation of magnetocardiogram signals containing error influences; Based on the lead field matrix and the simulated generation of magnetocardiogram signals containing error influences, an inverse problem solving algorithm is used for heart source imaging to evaluate the influence of errors on source imaging performance.

2. The method for sensor array error evaluation for cardiac magnetic source imaging of claim 1, wherein: The positioning mark points include being attached to the lateral waist of the subject, and the lateral waist mark points are used to provide a registration reference during CT scanning and 3D scanning model modeling, and are arranged to reduce the influence of abdominal fluctuation caused by respiration on model accuracy and are not interfered by the installation position of the magnetocardiogram acquisition board.

3. The method for sensor array error evaluation for cardiac magnetic source imaging of claim 1, wherein: The registration of the 3D scanning model with the CT model comprises an iterative closest point algorithm.

4. The method for sensor array error evaluation for cardiac magnetic source imaging of claim 1, wherein: The error types include crosstalk, gain error, sensitive axis angle error and position error; The setting of the errors existing in the magnetocardiogram sensor array information comprises the following steps: The crosstalk is used to simulate the magnetic field coupling effect between adjacent units when multiple sensors work synchronously, is horizontally set to 1% to 5%, follows the attenuation law, and is subjected to full-amplitude crosstalk at the minimum spacing between multiple sensors, and the crosstalk effect decays in inverse cubic relationship with the increase of the sensor spacing; The gain error is used to reflect the response amplitude difference of each sensor channel, and a uniform distribution random scaling factor with a mean value of zero and a maximum deviation of 1% to 5% is used to simulate the measurement signal; The sensitive axis angle error is used to represent the deviation of the sensitive axis of the multiple sensors relative to the orientation, and a fixed angle offset of 1° to 5° is set for each sensor, and the fixed angle offset direction is randomly generated in the tangential plane; The position error is used to simulate the installation deviation of the multiple sensor array, and is achieved by translating the multiple sensor array away from the body surface by 1 mm to 5 mm along the normal direction.

5. The sensor array error evaluation method for magnetocardiography source imaging according to claim 1, wherein: The generation process of the magnetocardiogram signal comprises the following steps: M grid vertices of the heart are regarded as a source space, each vertex is regarded as an equivalent current dipole, activated current dipoles are selected from the source space, and three cases of a single current dipole, a pair of current dipoles and a current dipole with a coupling relationship are considered respectively: The single current dipole comprises using a zero-mean Gaussian signal as a source activation time sequence; The pair of current dipoles comprises setting the spacing to be 4 mm, 6 mm and 8 mm respectively, and using mutually orthogonal zero-mean Gaussian signals as the activation sequence; The current dipole with coupling relationship includes randomly selecting a position in the source space after determining the number of sources, and constructing a source activation sequence through a sinusoidal mixed signal; The boundary element method is used to calculate the lead field matrix corresponding to the current dipole, and if there is an error type existing in the set magnetometer array information, the error parameter is included in the calculation process to obtain the lead field matrix containing errors; Based on the source activation sequence and the calculated lead field matrix, a preliminary magnetometer measurement signal is generated, and zero-mean Gaussian noise is added to simulate the actual sensor noise; If there is a set crosstalk or gain error, it acts on the noisy measurement signal; output the magnetometer measurement signal containing array error and noise effect.

6. The sensor array error evaluation method for magnetocardiographic source imaging according to claim 1, wherein: The inverse problem solving algorithm includes a linearly constrained minimum variance beamformer and an alternating projection algorithm; The linearly constrained minimum variance beamformer is used to reconstruct the source time sequence for a single current dipole and a pair of independent current dipoles, and the spatial filter weight vector calculation formula is: ， wherein representing a position the lead field vector corresponding to a unit dipole at the position, is the covariance matrix of the measurement signals, through which the reconstructed source time series is output by the filter; The alternating projection algorithm is used to locate the source for multiple current dipoles with coupling relationship, and the source position is estimated by iterative optimization; When the Euclidean distance of all estimated source positions in adjacent two iterations changes less than a set threshold, the iteration is terminated, and the final source position estimation result is output.

7. The method for sensor array error evaluation for cardiac magnetic source imaging of claim 1, wherein: The influence of the evaluation error on the source imaging performance includes system evaluation from the reconstruction accuracy, spatial resolution and related source positioning accuracy respectively: The reconstruction accuracy is used for evaluating the reconstruction effect of the single-dipole source time series, and correlation coefficient and relative mean square error are used as quantitative indexes. and relative mean square error as quantitative indexes. wherein, denotes the Pearson correlation coefficient between the reconstructed and the true time series, the relative mean square error is used to measure the normalized reconstruction error and is calculated as:

8. wherein is a normalized reconstruction time series, is a normalized real time series, The larger, the smaller the RMSE, indicating the higher the source reconstruction accuracy. The spatial resolution is used to assess the ability of the system to distinguish between adjacent dipole sources in space, in terms of the Pearson correlation coefficient between the two source time series after reconstruction As a quantitative indicator; set the separability threshold to be Corresponding to a shared variance between the two reconstructed signals of less than 50%. If then the source pair is considered separable, The smaller the value, the higher the spatial resolution of the system. The related source positioning accuracy is used to evaluate the position estimation accuracy of the related source, and the local positioning error between the estimated position and the true position is taken as an index, and the calculation formula is:

9. wherein, denotes the Euclidean distance between the estimation source and its nearest real source, is the set of successfully detected source indices, is the number of sources, the smaller the LE value, the higher the accuracy of the related source localization.

10. The sensor array error evaluation method for magnetocardiographic source imaging according to claim 1 or 5, wherein: The linearly constrained minimum variance beamformer and the indicators of reconstruction accuracy and spatial resolution are used to evaluate the reconstruction accuracy of a single source and the spatial resolution of the system, and the alternating projection algorithm and the indicator of related source positioning accuracy are used to evaluate the positioning accuracy of multiple related sources.

11. The method for sensor array error evaluation for cardiac magnetic source imaging of claim 4, wherein: The error type existing in the set magnetometer array information is used to act on the magnetometer signal and the lead field matrix at the same time to evaluate the sensor array error.

12. A sensor array error evaluation system for cardiac magnetic source imaging, based on the sensor array error evaluation method for cardiac magnetic source imaging according to any one of claims 1 to 9, characterized in that: It includes, A CT modeling module performs CT scanning on the upper body of a subject with positioning marker points attached, obtains CT images, performs tissue segmentation and three-dimensional reconstruction on the CT images, and generates a CT model; A surface scanning and registration module obtains three-dimensional data of the surface of the subject's torso with a magnetometer array and positioning marker points through a 3D scanner, establishes a 3D scanning model, and registers the 3D scanning model with the CT model; An error modeling module sets the error type existing in the magnetometer array information based on the magnetometer array information obtained after registration; The magnetocardiogram signal simulation module simulates and generates the magnetocardiogram signals containing error influence by using a boundary element method to calculate a lead field matrix based on the registered heart-double lung-trunk volume conductor model, a heart equivalent current dipole source model, and the error existing in the magnetocardiogram sensor array information; The source imaging and performance evaluation module performs heart source imaging by using an inverse problem solving algorithm and evaluates the influence of the error on the source imaging performance based on the lead field matrix and the magnetocardiogram signals simulated and generated containing the error influence.

Images