Magnetic source analysis phantom device and positioning method thereof

Abstract

The application discloses a magnetic source analysis mold device and a positioning method thereof, solves the technical problems of poor precision, low universality and low efficiency of a traditional mold, realizes full-attitude automatic positioning and precision evaluation of the magnetic source analysis mold, and provides a key verification tool support for development of a magnetic source analysis technology of a magnetic detection device.

Description

Technical Field

[0001] This application relates to the field of magnetic source analysis technology, specifically to a magnetic source analysis phantom device and its positioning method. Background Technology

[0002] Magnetic source analysis refers to inferring the source of an internal magnetic field based on the distribution of the external magnetic field. Typically, the "magnetic source" is modeled using an equivalent dipole, which can be an equivalent current dipole (a short current) or an equivalent magnetic dipole (a small magnet). Magnetic source analysis has important applications in medicine and industry. For example, modeling electromyography or neuronal discharges within a biological organism using equivalent current dipoles allows for the study of biological functions; similarly, modeling material impurities / defects using equivalent magnetic dipoles enables product quality control or repair.

[0003] The positioning accuracy of equivalent dipoles in magnetic source analysis is particularly important for its various applications. Therefore, the analysis system should be calibrated and its accuracy evaluated using a phantom before formal use. The phantom contains multiple equivalent dipoles whose spatial positions are known. The magnetic source analysis is performed on the magnetic field generated by the phantom using an analysis system (usually a magnetometer array). The analysis results are then compared with the prior positions to evaluate the positioning accuracy of the magnetic source analysis.

[0004] However, existing magnetic source analysis models and positioning methods have the following drawbacks: 1. Poor magnetic field equivalent accuracy: Traditional coils are hand-wound or simply welded. Current dipole coils often lack triangular tips, and magnetic dipole coils do not have closed-loop compensation wires. This results in a large deviation between the magnetic field generated by the coil and the ideal dipole magnetic field, which cannot meet the requirements of high-precision calibration.

[0005] 2. Low versatility and automation in positioning. Traditional phantoms are only compatible with a single device and have a fixed posture, making it impossible to achieve arbitrary placement and positioning; positioning relies on manual operation, which is time-consuming and has large errors, making it difficult to meet the needs of high-efficiency calibration in all scenarios.

[0006] Therefore, those skilled in the art urgently need to develop a magnetic source analysis phantom device and method that can achieve high-precision magnetic field equivalence and full-attitude automated positioning. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a magnetic source analysis phantom device and its positioning method. This invention solves the technical problems of poor accuracy, low versatility, and low efficiency of traditional phantoms, and realizes full-attitude automated positioning and accuracy evaluation of the magnetic source analysis phantom. It provides key verification tool support for the development of magnetic source analysis technology for magnetic detection equipment.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: The present invention provides a magnetic source analysis phantom device, including a phantom body, an optical marker array, and a positioning processing unit; The model body includes a model shell and an equivalent dipole coil module disposed within the model shell. The equivalent dipole coil module is used to generate a magnetic field that is highly fitted to an ideal dipole. The optical marker array is used to obtain a template point set by scanning during the pre-calibration stage and to obtain a measured marker point set by scanning after placing it in the magnetic detection array. The positioning processing unit is used to automatically match the measured set of marker points with the pre-calibrated set of template points. The positioning processing unit is configured to: perform coarse registration based on the local geometric feature descriptor of the marker points through a random sampling consensus algorithm to obtain an initial pose transformation; the initial pose transformation is then optimized by a fine registration algorithm to obtain the three-dimensional spatial pose of the phantom body, thereby realizing the automated positioning of the magnetic source analysis phantom.

[0009] Furthermore, the equivalent dipole coil module includes a first PCB board, a second PCB board, and an equivalent current dipole coil and an equivalent magnetic dipole coil respectively disposed on the first PCB board and the second PCB board; The equivalent current dipole coil adopts a triangular vertex completion structure; The equivalent magnetic dipole coil adopts a closed-loop structure with a compensating conductor passing through the origin.

[0010] Furthermore, both the equivalent current dipole coil and the equivalent magnetic dipole coil are fabricated using a composite process of PCB wiring and fine copper wire pulling.

[0011] Furthermore, the first PCB board is composed of two cross-shaped semi-circular PCB boards, integrating 32 equivalent current dipoles; the bottom of the first PCB board is vertically abutted against the second PCB board, and the two cross-shaped semi-circular PCB boards of the first PCB board separate the second PCB board to set 4 equivalent magnetic dipole coils; dipole reflective markers are set at the center of the spiral coil of the equivalent magnetic dipole coil and at the center of the base of the triangle of the triangle vertex completion structure, which together form a dipole array that covers the entire space inside the module body.

[0012] Furthermore, the triangle vertex completion structure is specifically as follows: the triangle vertex is the center of the semi-circular PCB board, and a pad with a preset safety distance is set on the waist of the triangle near the triangle vertex. Two thin copper wires are led out from the pads to the center of the semi-circular PCB board to form a complete triangle structure. Then, they are fixed at the center and led out. The led-out parts are twisted together to eliminate magnetic field interference. Finally, the twisted wires are extended and connected to the power supply.

[0013] Furthermore, the equivalent magnetic dipole coil adopts a closed-loop structure with a through-origin compensating wire. Specifically, the closed-loop structure with the through-origin compensating wire is as follows: the second PCB board includes an upper PCB board and a lower PCB board. The upper PCB board is provided with a spiral coil and an upper solder joint. The outer coil end of the spiral coil is connected to the upper solder joint. The lower PCB board is provided with a through-wire and a lower solder joint. One end of the through-wire is connected to the center of the spiral coil through a via, and the other end is connected to the lower solder joint. The upper solder joint and the lower solder joint are arranged opposite to each other. The power supply line led out from the upper solder joint and the power supply line led out from the lower solder joint are twisted together to suppress magnetic field interference of the power supply line.

[0014] Furthermore, the main body of the mold adopts a hemispherical structure, and the outer surface of the mold shell is provided with a groove for fixing reflective markers. The center of the reflective markers and the actual position of the internal equivalent dipole coil module form a fixed geometric relationship. The actual position of the equivalent dipole coil module is represented by the dipole array formed by the dipole reflective markers.

[0015] Furthermore, the phantom device also includes a reverse calibration unit, which is used to obtain the actual position of the equivalent dipole coil module and the center position of the reflective marker point through an optical scanning device, and to establish a pre-calibrated template point set.

[0016] Another aspect of the present invention provides a method for positioning a magnetic source analysis phantom, the method being applied to any of the magnetic source analysis phantom devices described above, specifically comprising the following steps: S1. Reflective markers are set on the surface of the model body, and an equivalent dipole coil module is installed. The actual position of the equivalent dipole coil module and the center position of the reflective markers are obtained by optical scanning equipment, and a pre-calibrated template point set is established. S2. Place the magnetic source analysis model in the magnetic detection array and acquire the measured set of marker points using an optical scanning device; S3. Calculate the local geometric feature descriptors of the marker points for the measured marker point set and template point set. Perform coarse registration based on the descriptor matching and random sampling consistency algorithm to obtain the initial pose transformation. S4. The initial pose transformation is optimized through a fine registration algorithm, and the final three-dimensional spatial pose of the phantom body is output, realizing the automated positioning of the magnetic source analysis phantom.

[0017] Furthermore, the local geometric feature descriptor includes distance and angle features from the target marker point to its nearest candidate points.

[0018] Furthermore, the coarse registration uses the RANSAC algorithm to randomly select three sets of matching point pairs, and estimates the rotation matrix and translation vector through SVD decomposition.

[0019] Furthermore, the precise registration employs the Iterative Nearest Neighbor (ICP) algorithm, which filters out abnormal matches using a distance threshold and optimizes the result using least squares to obtain the accurate transformation matrix.

[0020] Compared with the prior art, this application has the following advantages: High precision: The magnetic source analysis phantom device and its positioning method of this application can achieve low average error and high analysis precision in actual magnetic source analysis, which is far superior to traditional phantoms and meets the precision verification requirements of high-end magnetic detection equipment such as magnetoencephalography (MEG) and magnetocardiography (MCG).

[0021] High versatility: It supports the placement of the magnetic source analysis phantom in any orientation in three-dimensional space, and is compatible with various magnetic detection arrays (such as magnetoencephalography systems of different brands and different number of channels), enabling full-angle and full-scenario magnetic source analysis accuracy assessment.

[0022] High degree of automation: The entire positioning process of the magnetic source analysis model in this application does not require manual intervention and the positioning time is short, which greatly improves the verification efficiency compared with traditional manual positioning.

[0023] Scalability: The modular coil design and calibration scheme allows for rapid customization of dipole arrays (such as increasing the number of dipoles or adjusting the distribution density) to meet the diverse needs of fields such as industrial non-destructive testing (NDT). Attached Figure Description

[0024] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 This is a schematic diagram of the magnetic source analysis phantom device in the embodiments of this application; Figure 2 for Figure 1 A schematic diagram of the equivalent dipole coil module after removing the shell of the magnetic source analysis module; Figure 3This is a schematic diagram of the equivalent current dipole coil using a triangular vertex completion structure in an embodiment of this application; Figure 4 This is a schematic diagram of a closed-loop structure with a compensating wire penetrating the origin, used in an embodiment of this application for an equivalent magnetic dipole coil. Figure 5 This is a schematic diagram of the state structure in which the magnetic source analysis module is fixedly placed in the magnetic detection array; Figure 6 This is a flowchart of the magnetic source analysis phantom positioning method in the embodiments of this application.

[0025] Figure label: 1. Module body; 11. Module shell; 12. Equivalent dipole coil module; 13. Equivalent current dipole coil; 14. Pad; 15. Center of the semi-circular PCB board; 110. Groove for fixing reflective markers; 121. First PCB board; 122. Second PCB board; 123. Equivalent magnetic dipole coil; 124. Helical coil; 125. Upper solder joint; 126. Center of the helical coil; 127. Through wire; 130. Center of the base of the triangle; 1211. Semi-circular PCB board a; 1212. Semi-circular PCB board b; 1241. Outer coil end of the helical coil; 2. Grip; 20. Power cord; 3. Positioning processing unit. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the appendices in the embodiments of this disclosure will be described below. Figures 1-6 The technical solutions in the embodiments of this disclosure are clearly and completely described. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0027] The technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0028] Example 1 like Figure 1 , Figure 2As shown, this application provides a magnetic source analysis phantom device, including a phantom body 1, an optical marker array, a reverse calibration unit, and a positioning processing unit 3. The phantom body 1 adopts a hemispherical structure, including a phantom shell 11 and an equivalent dipole coil module 12 disposed within the phantom shell 11; the outer surface of the phantom shell 11 has a groove 110 for fixing the reflective markers, and the center of the reflective markers forms a fixed geometric relationship with the actual position of the internal equivalent dipole coil module 12.

[0029] The equivalent dipole coil module 12 includes a first PCB board 121, a second PCB board 122, and an equivalent current dipole coil 13 and an equivalent magnetic dipole coil 123 respectively disposed on the first PCB board 121 and the second PCB board 122, for generating a magnetic field that is highly fitted to an ideal dipole.

[0030] The optical marker array is used to obtain a template point set by scanning during the pre-calibration stage and to obtain a measured marker point set by scanning after placing it in the magnetic detection array.

[0031] The reverse calibration unit uses a structured light depth camera, a laser line scan camera, or an X-ray device to obtain the actual three-dimensional coordinates of the outer shell marker points and the internal coils of the mold, and to establish a set of template marker points.

[0032] The positioning processing unit 3 is used to automatically match the measured set of marker points with the pre-calibrated set of template marker points. Based on the local geometric feature descriptors of the marker points, the initial pose transformation is obtained by coarse registration through a random sampling consensus algorithm. The initial pose transformation is then optimized by a fine registration algorithm to obtain the three-dimensional spatial pose of the phantom body 1, thereby realizing the automated positioning of the magnetic source analysis phantom.

[0033] In coarse registration, local geometric feature descriptors of the marker points are calculated for the measured marker point set and the template marker point set. This is done based on descriptor matching and random sampling consensus algorithms to obtain the initial pose transformation. Specifically: First, calculate the local geometric feature descriptor: for each marker point, calculate the distance features to the k nearest neighbors and the paired angle features between the nearest neighbors to form a rigid body transformation invariant descriptor; Furthermore, descriptor matching: K-nearest neighbor search is used to obtain the first and second nearest neighbors, Lowe ratio test is used to filter fuzzy matches, and mutual nearest neighbor check is combined to retain reliable matching pairs; Finally, RANSAC coarse registration: randomly select a preset number of matching point pairs, preferably not limited to 3 groups, estimate the rotation matrix and translation vector through SVD decomposition, calculate the residuals of all point pairs, and filter inliers; iterate a preset number of times (e.g., 100 times), retain the model with the most inliers as the initial transformation, and dynamically adjust the number of iterations according to the proportion of inliers.

[0034] The rotation matrix and translation vector are estimated through SVD decomposition, the residuals of all point pairs are calculated, and interior points are selected. Details are as follows: For point sets and The estimated transformation makes:

[0035] Calculate the centroid:

[0036] Decentralization:

[0037] Calculate the covariance matrix:

[0038] SVD decomposition:

[0039] Calculate the rotation matrix:

[0040] if (Reflection), corrected to:

[0041] Calculate the translation vector: ; The transformation (R,t) is estimated from three point pairs using the SVD method.

[0042] Interior point evaluation: Calculate the residuals at all points.

[0043] in, This represents the residual of the i-th data point. Let represent the rotation matrix estimated at the k-th iteration. This represents the translation vector estimated at the k-th iteration. This represents the i-th point in the template marker set. This indicates that at the j-th point in the set of measured markers, the matching objective is to find an initial transformation (R,t) such that R× + t ≈ .

[0044] Set threshold Find after filtering The interior point of .

[0045] Model selection: Retain the model with the most interior points; Dynamic iteration count: The number of iterations is dynamically adjusted based on the proportion of interior points.

[0046] in It is the ratio of interior points.

[0047] The initial transformation of the random sampling consensus estimate is applied to all marked points:

[0048] in, This is the initial transformation matrix; Points after initial pose transformation : .

[0049] In the fine registration process, the initial pose transformation is optimized using the iterative nearest neighbor (ICP) algorithm, outputting the final three-dimensional spatial pose of the phantom body 1, including: The initial transformation is applied to the measured marker set, and the nearest neighbor of each measured marker is searched in the template marker set. Anomalies are filtered by distance threshold, and matching pairs with a distance of less than 3.0 mm are retained. The rotation matrix and translation vector are refined by least squares method and SVD decomposition using qualified matching pairs. The solution is iterated until the average error is less than 0.01 mm, and finally a 4×4 transformation matrix containing complete three-dimensional spatial pose information is output.

[0050] Specifically, nearest neighbor matching is performed for each transformed labeled point. Find the nearest point in the set of measured marker points:

[0051]

[0052] In the above nearest neighbor matching algorithm formula, for each transformed marker point... ,calculate With each Find the point with the smallest distance between them; its index is 1. ,Establish and The correspondence is filtered by a distance threshold, retaining only matching pairs whose distance is less than the threshold:

[0053] The default threshold is 3.0mm.

[0054] Least squares optimization, using the filtered matching pairs, re-estimates the accurate rigid body transformation using the SVD method:

[0055] Final transformation matrix:

[0056] Finally, the residuals are calculated to confirm the matching effect, and the registration residuals (RANSAC in-point pairs) are obtained:

[0057] Refined registration residuals (optimized matching pairs):

[0058] Based on the final transformation matrix, the pose of the magnetic probe array can be transformed to the phantom coordinate system. The relative positions of the dipole and the magnetic probe array are known, and then the accuracy of the magnetic source analysis can be evaluated.

[0059] Assuming a magnetic detection array The coordinates of the detectors are , It is a three-dimensional vector. The detectors are oriented as follows , If it is a three-dimensional unit vector, then Here are the coordinates of the magnetic detector in the phantom coordinate system. The orientation of the magnetic detector in the phantom coordinate system.

[0060] The three-dimensional attitude parameters of the phantom are calculated based on the final transformation matrix. The dipole coordinates in the template marker point set are transformed to the magnetic detection array coordinate system to complete the automated positioning of the magnetic source analysis phantom.

[0061] Example 2 Based on Embodiment 1, as a preferred implementation, the equivalent current dipole coil 13 in this embodiment adopts a triangular vertex-complete structure with a base length of 5mm and a height of 50mm. Finite element simulation verification shows that the average difference in magnetic field amplitude between the complete triangular coil and the ideal current dipole is 0.049%, and the difference in magnetic field angle is 0.024°. If the triangular apex is removed, the average amplitude difference reaches 17%, and the magnetic field angle difference reaches 11°. It is evident that the equivalent current dipole coil using a triangular vertex-complete structure can generate a magnetic field that closely matches that of an ideal dipole.

[0062] For the fabrication of equivalent current dipole coils, a composite process of PCB wiring and fine copper wire pulling is used. The specific steps are as follows: 1) The main body of the coil is made using a semi-circular PCB board with a copper thickness of 30μm, which enables high-precision printing of the base of the triangle and most of the waist, ensuring straight lines, sharp corners and minimal deformation; 2) The vertex of the triangle is located at the center 15 of the semi-circular PCB board. There is a wiring conflict near the vertex, so the line is cut off and pad 14 is set. A preset safe distance is maintained between the pads. 3) Use fine copper wire with a diameter of 0.1mm, solder one end to pad 14, and pull the other end straight to the center 15 of the semi-circular PCB board to form a complete triangular structure; 4) The fine copper wire is fixed at the center 15 and led out. The led-out parts are twisted together to eliminate magnetic field interference. The twisted wire is extended and connected to the power supply.

[0063] For the design of the equivalent magnetic dipole coil, the equivalent magnetic dipole coil 123 adopts a closed-loop structure with a compensating wire passing through the origin, with a coil diameter of 5mm and 9 turns. After adding the through wire, the average difference in magnetic field amplitude is 0.26%, and the angle difference is 0.036°; while removing the through wire to simulate common winding schemes, finite element simulation shows that the average difference in magnetic field amplitude is as high as 220%, and the angle difference is as high as 52°. It can be seen that the equivalent magnetic dipole coil 123, with a closed-loop structure with a compensating wire passing through the origin, can generate a magnetic field that closely matches that of an ideal dipole.

[0064] Furthermore, the equivalent magnetic dipole coil is fabricated using a double-layer PCB process, with the following specific steps: 1) The second PCB board 122 includes an upper PCB board and a lower PCB board. A spiral coil 124 and an upper solder joint 125 are provided on the upper PCB board. The outer coil end 1241 of the spiral coil 124 is connected to the upper solder joint 125. 2) A through conductor 127 and a lower-layer solder joint are provided on the lower PCB board. One end of the through conductor 127 is connected to the center 126 of the solenoid coil through a via, and the other end is connected to the lower-layer solder joint. 3) The upper solder joint 125 is set opposite to the lower solder joint, and the power supply line led out from the upper solder joint 125 is twisted together with the power supply line led out from the lower solder joint to suppress the magnetic field interference of the power supply line.

[0065] The specific assembly method of the equivalent dipole coil module is as follows: the first PCB board 121 is composed of two semi-circular PCB boards arranged in a cross shape, namely semi-circular PCB board a1211 and semi-circular PCB board b1212, preferably not limited to integrating 32 equivalent current dipole coils 13; the bottom of the first PCB board 121 is vertically attached to the second PCB board 122, and the second PCB board 122 is divided into 4 equivalent magnetic dipole coils 123; dipole reflective markers are set at the center 130 of the base of the triangle and the center 126 of the spiral coil, which together form a dipole array that covers the entire space inside the module body 1.

[0066] The reverse calibration of the mold is achieved as follows: Open the top cover of the mold body, and use optical scanning equipment or X-Ray equipment to simultaneously acquire the reflective marking points on the outer shell of the mold and the reflective marking points of the dipole on the PCB board; calculate the approximate position of the equivalent dipole through the reflective marking points of the dipole, unify all coordinates to the mold coordinate system, generate a pre-calibrated template marking point set, and eliminate processing and assembly errors.

[0067] Example 3 like Figure 6 As shown, this embodiment provides a method for locating a magnetic source analysis phantom, applied to the aforementioned positioning device, including the following steps: S1 Template Establishment: Reflective markers are arranged in the groove 110 on the surface of the model body 1, dipole reflective markers are arranged on the equivalent dipole coil module 12, the equivalent dipole coil module 12 is assembled, the actual position of the equivalent dipole coil module 12 and the center position of the reflective markers are obtained by optical scanning equipment, and a pre-calibrated template marker set is established.

[0068] S2 Measured Point Acquisition: The magnetic source analysis model is placed in the magnetic detection array, and the set of measured marker points under the current attitude is obtained through optical scanning equipment.

[0069] S3 coarse registration: Calculates local geometric feature descriptors for the measured and template marker point sets, based on descriptor matching and random sampling consensus algorithms, to obtain the initial pose transformation. Specifically, this includes: First, calculate the local geometric feature descriptor: for each marker point, calculate the distance features to the k nearest neighbors and the paired angle features between the nearest neighbors to form a rigid body transformation invariant descriptor; Furthermore, descriptor matching: K-nearest neighbor search is used to obtain the first and second nearest neighbors, Lowe ratio test is used to filter fuzzy matches, and mutual nearest neighbor check is combined to retain reliable matching pairs; Finally, RANSAC coarse registration: randomly select a preset number of matching point pairs, preferably not limited to 3 groups, estimate the rotation matrix and translation vector through SVD decomposition, calculate the residuals of all point pairs, and filter inliers; iterate a preset number of times (e.g., 100 times), retain the model with the most inliers as the initial transformation, and dynamically adjust the number of iterations according to the proportion of inliers.

[0070] The rotation matrix and translation vector are estimated through SVD decomposition, the residuals of all point pairs are calculated, and interior points are selected. Details are as follows: For point sets and The estimated transformation makes:

[0071] Calculate the centroid:

[0072] Decentralization:

[0073] Calculate the covariance matrix:

[0074] SVD decomposition:

[0075] Calculate the rotation matrix:

[0076] if (Reflection), corrected to:

[0077] Calculate the translation vector: ; The transformation (R,t) is estimated from three point pairs using the SVD method.

[0078] Interior point evaluation: Calculate the residuals at all points.

[0079] in, This represents the residual of the i-th data point. Let represent the rotation matrix estimated at the k-th iteration. This represents the translation vector estimated at the k-th iteration. This represents the i-th point in the template marker set. This indicates that at the j-th point in the set of measured markers, the matching objective is to find an initial transformation (R,t) such that R× + t ≈ .

[0080] Set threshold Find after filtering The interior point of .

[0081] Model selection: Retain the model with the most interior points; Dynamic iteration count: The number of iterations is dynamically adjusted based on the proportion of interior points.

[0082] in It is the ratio of interior points.

[0083] The initial transformation of the random sampling consensus estimate is applied to all marked points:

[0084] in, This is the initial transformation matrix; Points after initial pose transformation : .

[0085] S4 Fine Registration: The initial pose transformation is optimized through the Iterative Nearest Neighbor (ICP) algorithm to output the final 3D spatial pose of the phantom body 1, specifically including: The initial transformation is applied to the set of measured marker points to search for the nearest neighbor of each measured marker point; abnormal matches are filtered out using a distance threshold, and matching pairs with a distance of less than 3.0 mm are retained; the rotation matrix and translation vector are refined by using the least squares method and SVD decomposition of qualified matching pairs; the solution is iterated until the average error is less than 0.01 mm, and finally a 4×4 transformation matrix containing complete three-dimensional spatial pose information is output.

[0086] Specifically, nearest neighbor matching is performed for each transformed labeled point. Find the nearest point in the set of measured marker points:

[0087]

[0088] In the above nearest neighbor matching algorithm formula, the function of this formula is: for each transformed labeled point... ,calculate With each Find the point with the smallest distance between them, and its index is j. ,Establish and The correspondence is filtered by a distance threshold, retaining only matching pairs whose distance is less than the threshold:

[0089] The default threshold is 3.0mm.

[0090] Least squares optimization, using the filtered matching pairs, re-estimates the accurate rigid body transformation using the SVD method:

[0091] Final transformation matrix:

[0092] Finally, the residuals can be calculated to confirm the matching effect, and the registration residuals (RANSAC in-point pairs) are obtained:

[0093] Refined registration residuals (optimized matching pairs):

[0094] Based on the final transformation matrix, the pose of the magnetic probe array can be transformed to the phantom coordinate system. The relative positions of the dipole and the magnetic probe array are known, and then the accuracy of the magnetic source analysis can be evaluated.

[0095] Assuming a magnetic detection array The coordinates of the detectors are , It is a three-dimensional vector. The detectors are oriented as follows , If it is a three-dimensional unit vector, then Here are the coordinates of the magnetic detector in the phantom coordinate system. The orientation of the magnetic detector in the phantom coordinate system.

[0096] S5 Pose Output and Coordinate Transformation: Calculate the three-dimensional attitude parameters of the phantom based on the final transformation matrix, transform the dipole coordinates in the template marker point set to the magnetic detection array coordinate system, and complete the automated positioning of the magnetic source analysis phantom.

[0097] Combination Figure 5 The magnetic source analysis phantom shown is placed in a magnetic detector array, and the calculated results of its magnetic source analysis error are as follows: 1 1.579 2 0.826 3 0.939 4 0.888 5 0.212 6 0.550 7 1.308 8 2.429 9 1.181 10 1.363 11 1.777 12 2.158 13 1.591 14 1.562 15 1.623 16 1.942 17 1.635 18 1.774 19 1.688 20 1.570 21 1.934 22 2.157 23 2.270 24 2.344 25 0.718 26 0.666 27 0.219 28 0.641 29 1.603 30 1.573 31 1.108 32 0.781 Maximum value 2.429 average value 1.394

[0098] The magnetic source analysis phantom is used for the calibration of magnetoencephalography (MEG) systems. The measured average error of the magnetic source analysis is 1.394 mm, and the maximum error is 2.429 mm. The magnetic source analysis phantom can be placed in any orientation in six degrees of freedom space, and is compatible with various magnetic detection arrays. The theoretical error of the coil is low, and the assembly error is effectively corrected by reverse calibration. It has high positioning accuracy and strong robustness.

[0099] In summary, the magnetic source analysis phantom device and its positioning method of this application solve the technical problems of poor accuracy, low versatility and low efficiency of traditional phantoms, realize the full-attitude automated positioning and accuracy evaluation of the magnetic source analysis phantom, and provide key verification tool support for the development of magnetic source analysis technology for magnetic detection equipment.

[0100] The structures, processes, and algorithms not described in detail in the above embodiments are all implemented using conventional techniques in the field.

[0101] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications 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.

Claims

1. A magnetic source analysis phantom device, characterized in that, Includes the phantom body, optical marker array, and positioning processing unit; The model body includes a model shell and an equivalent dipole coil module disposed within the model shell. The equivalent dipole coil module is used to generate a magnetic field that closely matches an ideal dipole. The equivalent dipole coil module includes a first PCB board, a second PCB board, and equivalent current dipole coils and equivalent magnetic dipole coils respectively disposed on the first PCB board and the second PCB board. The first PCB board is composed of two semi-circular PCB boards arranged in a cross shape. The equivalent current dipole coil adopts a triangular vertex completion structure. The equivalent magnetic dipole coil adopts a closed-loop structure with a compensating wire passing through the origin. The triangle vertex completion structure is as follows: the triangle vertex is the center of the semi-circular PCB board. A pad with a preset safety distance is set on the waist of the triangle near the triangle vertex. Two thin copper wires are led out from the pads to the center of the semi-circular PCB board to form a complete triangle structure. Then, the wires are fixed at the center and led out. The led-out parts are twisted together to eliminate magnetic field interference. Finally, the twisted wires are extended and connected to the power supply. The closed-loop structure with a through-origin compensation conductor is specifically as follows: the second PCB board includes an upper PCB board and a lower PCB board. The upper PCB board is provided with a spiral coil and an upper solder joint. The outer coil end of the spiral coil is connected to the upper solder joint. The lower PCB board is provided with a through conductor and a lower solder joint. One end of the through conductor is connected to the center of the spiral coil through a via, and the other end is connected to the lower solder joint. The upper solder joint and the lower solder joint are arranged opposite to each other. The power supply line led out from the upper solder joint and the power supply line led out from the lower solder joint are twisted together to suppress magnetic field interference of the power supply line. The optical marker array is used to scan and obtain a template point set in the pre-calibration stage and to scan and obtain a measured marker point set after being placed in the magnetic detection array. The positioning processing unit is used to automatically match the measured set of marker points with the pre-calibrated set of template points. The positioning processing unit is configured to: perform coarse registration based on the local geometric feature descriptor of the marker points through a random sampling consensus algorithm to obtain an initial pose transformation; the initial pose transformation is then optimized by a fine registration algorithm to obtain the three-dimensional spatial pose of the phantom body, thereby realizing the automated positioning of the magnetic source analysis phantom.

2. The magnetic source analysis phantom device according to claim 1, characterized in that, Both the equivalent current dipole coil and the equivalent magnetic dipole coil are fabricated using a composite process of PCB wiring and fine copper wire pulling.

3. The magnetic source analysis phantom device according to claim 1, characterized in that, The bottom of the first PCB board is vertically abutted against the second PCB board. The two semi-circular PCB boards of the first PCB board are arranged in a cross shape to separate the second PCB board and set four equivalent magnetic dipole coils. Dipole reflective markers are set at the center of the spiral coil of the equivalent magnetic dipole coil and at the center of the base of the triangle of the triangle vertex completion structure, which together form a dipole array that covers the entire space inside the body of the model.

4. The magnetic source analysis phantom device according to claim 3, characterized in that, The outer surface of the mold shell is provided with a groove for fixing reflective markers. The center of the reflective markers forms a fixed geometric relationship with the actual position of the internal equivalent dipole coil module. The actual position of the equivalent dipole coil module is represented by the dipole array formed by the dipole reflective markers.

5. The magnetic source analysis phantom device according to claim 1, characterized in that, The phantom device also includes a reverse calibration unit, which is used to obtain the actual position of the equivalent dipole coil module and the center position of the reflective marker point through an optical scanning device, and to establish a pre-calibrated template point set.

6. A method for positioning a magnetic source analysis phantom, applied to the magnetic source analysis phantom device according to any one of claims 1-5, characterized in that, The steps include: S1, setting reflective markers on the surface of the phantom body and installing an equivalent dipole coil module, obtaining the actual position of the equivalent dipole coil module and the center position of the reflective markers through an optical scanning device, and establishing a pre-calibrated template point set; S2, placing the magnetic source analysis phantom in a magnetic detection array, and obtaining the measured marker point set through an optical scanning device. S3. Calculate the local geometric feature descriptors of the marker points for the measured marker point set and template point set. Perform coarse registration based on the descriptor matching and random sampling consistency algorithm to obtain the initial pose transformation. S4. Optimize the initial pose transformation through the fine registration algorithm and output the final three-dimensional spatial pose of the phantom body to realize the automated positioning of the magnetic source analysis phantom.

7. The method according to claim 6, characterized in that, The local geometric feature descriptor includes distance and angle features from the target marker point to its nearest candidate points.

8. The method according to claim 6, characterized in that, The coarse registration uses the RANSAC algorithm to randomly select three pairs of matching points, and estimates the rotation matrix and translation vector through SVD decomposition.

9. The method according to claim 6, characterized in that, The precise registration employs the Iterative Nearest Neighbor (ICP) algorithm, which filters out abnormal matches using a distance threshold and optimizes the result using least squares to obtain the accurate transformation matrix.

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