Registration method and device of magnetoencephalography, equipment, storage medium and program product

Abstract

The invention relates to a magnetoencephalography registration method, a magnetoencephalography registration device, magnetoencephalography registration equipment, a storage medium and a program product. Generating a helmet face model including the first facial feature of the testee and the first marker feature corresponding to the marker arranged on the brain magnetic helmet; according to the brain nuclear magnetic data of the testee, generating a scalp face model including a second facial feature of the testee; according to the design parameters of the brain magnetic helmet, acquiring a helmet device model comprising second marker features corresponding to the plurality of markers; registering the helmet face model with the scalp face model according to the first facial feature and the second facial feature to obtain a registered helmet face model including the registered first marker feature; and according to the registered first marker feature and the second marker feature, registering the helmet device model with the registered helmet face model to obtain a registered helmet device model.

Description

Technical Field

[0001] The present disclosure relates to the field of medical devices, and particularly to a registration method for a magnetoencephalograph, a registration device for a magnetoencephalograph, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Magnetoencephalography (MEG) is a non-invasive neuroactivity detection imaging technique that measures the magnetic field coupled by the collective current of brain neurons using highly sensitive magnetic field sensors distributed on the scalp surface. After years of research and development, MEG has been maturely applied in fields such as preoperative brain functional area localization, epilepsy focus localization, brain function impairment determination, neurological disease diagnosis, and basic research on human brain cognition. Whether it is a traditional superconducting quantum interference device magnetoencephalograph (SQUID MEG) system or a new generation of optically pumped magnetometers magnetoencephalograph (OPM-MEG) system, there is a very crucial registration process during use, that is, to determine the spatial coordinates and sensitive orientations of multi-channel sensors relative to the subject in its brain anatomical coordinate system, which is crucial for the solution of the inverse model of magnetic field detection.

[0003] However, the existing magnetoencephalograph registration methods completely rely on the position information of the complete sensor array obtained by a three-dimensional scanner, with a long scanning time and a cumbersome process. It is necessary to match features such as the boundary angle, normal angle, and center distance of each sensor, which is very likely to amplify the registration error. Therefore, there is an urgent need to provide an improved magnetoencephalograph registration method to shorten the scanning time, simplify the registration process, and improve the registration accuracy. Summary of the Invention

[0004] According to one aspect of the present disclosure, a registration method for a magnetoencephalograph is provided, including: generating a helmet surface model according to a head structure data set of a subject wearing a magnetoencephalograph helmet, wherein the helmet surface model includes a first facial feature of the subject and a first marker feature corresponding to a plurality of markers provided on the magnetoencephalograph helmet; generating a scalp surface model according to the nuclear magnetic data of the subject's brain, the scalp surface model including a second facial feature of the subject; obtaining a helmet device model according to the design parameters of the magnetoencephalograph helmet, the helmet device model including a second marker feature corresponding to the plurality of markers; registering the helmet surface model with the scalp surface model according to the first facial feature and the second facial feature to obtain a registered helmet surface model, wherein the registered helmet surface model includes the registered first marker feature; and registering the helmet device model with the registered helmet surface model according to the registered first marker feature and the second marker feature to obtain a registered helmet device model.

[0005] According to another aspect of the present disclosure, a registration device for a magnetoencephalograph is provided. The device includes: a first unit configured to generate a helmet surface model according to a head structure data set of a subject wearing a magnetoencephalograph helmet, wherein the helmet surface model includes a first facial feature of the subject and a first marker feature corresponding to a plurality of markers provided on the magnetoencephalograph helmet; a second unit configured to generate a scalp surface model according to the nuclear magnetic data of the subject's brain, the scalp surface model including a second facial feature of the subject; a third unit configured to obtain a helmet device model according to the design parameters of the magnetoencephalograph helmet, the helmet device model including a second marker feature corresponding to the plurality of markers; a fourth unit configured to register the helmet surface model with the scalp surface model according to the first facial feature and the second facial feature to obtain a registered helmet surface model, wherein the registered helmet surface model includes the registered first marker feature; and a fifth unit configured to register the helmet device model with the registered helmet surface model according to the registered first marker feature and the second marker feature to obtain a registered helmet device model.

[0006] According to another aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, and when the computer program is executed by the at least one processor, the above-mentioned method is implemented.

[0007] According to another aspect of the present disclosure, one or more computer-readable storage media are provided, on which instructions are stored, and when the instructions are executed by one or more processors, the one or more processors are caused to execute the above-mentioned method.

[0008] According to another aspect of the present disclosure, there is provided a computer program product including a computer program, wherein the computer program, when executed by a processor, implements the method described above.

[0009] The present disclosure provides a method, apparatus, and device for indirect registration of a magnetoencephalograph. The method, apparatus, and device can reconstruct a helmet surface model from a head structure data set collected by a three-dimensional scanner or a camera. The reconstructed helmet surface model can include only the facial features of the subject and the marker features corresponding to the markers of the magnetoencephalograph helmet. Furthermore, the reconstructed helmet surface model can indirectly align the helmet device model with the scalp surface model. The method, apparatus, and device of the present disclosure have the advantages of short scanning time and simple registration process.

[0010] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings exemplarily illustrate embodiments and form a part of the specification, and are used together with the written description of the specification to explain the exemplary embodiments of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0012] Embodiments of the present disclosure will be described in detail below with reference to the drawings, making the above and other features and advantages of the present disclosure clearer to those of ordinary skill in the art. In the drawings: Figure 1 shows a flowchart of a registration method of a magnetoencephalograph according to an embodiment of the present disclosure; Figure 2 shows a schematic diagram of a registration method of a magnetoencephalograph according to the related art; Figure 3A shows a front view schematic diagram of a marker provided on a magnetoencephalograph helmet according to an embodiment of the present disclosure; Figure 3B shows a left view schematic diagram of a marker provided on a magnetoencephalograph helmet according to an embodiment of the present disclosure; Figure 3C shows a top view schematic diagram of a marker provided on a magnetoencephalograph helmet according to an embodiment of the present disclosure; Figure 4 shows a flowchart of a partial process of a registration method of a magnetoencephalograph according to an embodiment of the present disclosure; Figure 5 shows a flowchart of a partial process of generating a helmet surface model according to an embodiment of the present disclosure; Figure 6The flowchart shows a partial process of generating a helmet surface model according to an embodiment of the present disclosure; Figure 7 The flowchart shows a process of extracting a helmet surface model based on a head rendering model according to an embodiment of the present disclosure; Figure 8 The schematic diagram shows a partial process of generating a scalp surface model according to an embodiment of the present disclosure; Figure 9 The schematic diagram shows a partial process of registering a helmet surface model with a scalp surface model according to an embodiment of the present disclosure; Figure 10 The schematic diagram shows a partial process of registering a helmet device model with a registered helmet surface model according to an embodiment of the present disclosure; Figure 11 The block diagram shows the structure of a registration device of a magnetoencephalograph according to an embodiment of the present disclosure. Detailed implementation manners

[0013] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted below.

[0014] In the present disclosure, unless otherwise specified, the terms "first", "second", etc. are used to describe various elements, and are not intended to limit the positional relationship, temporal relationship or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, and in certain cases, based on the context description, they may also refer to different instances.

[0015] In the description of various examples in the present disclosure, the terms used are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in the present disclosure covers any one of the listed items and all possible combinations.

[0016] Magnetoencephalography (MEG) technology is a brain function detection technology that non-invasively detects the electromagnetic physiological signals of the brain. It records, outside the brain through scalp sensors, the extremely weak bio-magnetic field signals formed by the currents generated by the postsynaptic potentials of neurons in the brain. In the actual measurement of a superconducting MEG instrument, before detection, the nasion, and the preauricular points on the left and right ears of the subject are usually marked as reference points to establish a three-dimensional coordinate system of the head. Then the patient enters the magnetic shielding room for MEG examination. After the collection of MEG signals is completed, the nasion and the preauricular points on the left and right ears of the patient are marked again as landmark points for image fusion, and then magnetic resonance imaging (MRI) examination is performed. After all measurements are completed, the model method is used to obtain the position and direction of the signal source in the MEG coordinate system, and registration is performed with the MRI image with the landmark points as the reference, so as to obtain a clear image of the position and direction of the current dipole in the brain structure.

[0017] However, the method of directly registering the MEG coordinate system with the MRI image as described above completely depends on a three-dimensional scanner to obtain the position information of the scalp sensor array, and has a long scanning time and a cumbersome process, making the registration process cumbersome and the registration accuracy low.

[0018] In view of this, the present disclosure provides an indirect registration method and device for a MEG instrument using markers provided on a MEG helmet and a helmet surface model. This method and device can simplify the process of obtaining a head structure data set by using the markers on the helmet, and indirectly register the scalp surface model obtained by MRI examination and the helmet device model representing the position distribution of the scalp sensors on the MEG helmet by using the generated helmet surface model, thereby obtaining an accurately registered helmet device model, which helps to subsequently determine the spatial coordinates and sensitive orientations of the scalp sensor array relative to the brain anatomical coordinate system of the subject, and further facilitates the solution of the inverse model of MEG signals. In addition, compared with the traditional positioning and registration methods using three-dimensional structured light or spatial marker points, the method and device of the present disclosure can significantly shorten the scanning time, simplify the registration process, and improve the registration accuracy.

[0019] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0020] First, in combination with Figure 1 、 Figures 4 - 10 the flowchart of and Figures 3A - 3C the three views of the setting of the markers on the MEG helmet of, the registration method for a MEG instrument according to an embodiment of the present disclosure will be described.

[0021] Figure 1 shows a flowchart of a registration method 100 for a MEG instrument according to an embodiment of the present disclosure. As Figure 1 shown, the method 100 may include: Step S110: Generate a helmet surface model based on the head structure dataset of the brain magnetic tomography (MEG) helmet worn by the subject. The helmet surface model includes the first facial features of the subject and the first marker features corresponding to the markers provided on the MEG helmet. Step S120: Generate a scalp surface model based on the nuclear magnetic resonance (MRI) data of the subject's brain. The scalp surface model includes the second facial features of the subject. Step S130: Obtain a helmet device model according to the design parameters of the MEG helmet. The helmet device model includes the second marker features corresponding to multiple markers. Step S140: Register the helmet surface model with the scalp surface model according to the first facial features and the second facial features to obtain the registered helmet surface model, where the registered helmet surface model includes the registered first marker features. Step S150: Register the helmet device model with the registered helmet surface model according to the registered first marker features and the second marker features to obtain the registered helmet device model.

[0022] In step S110, the core hardware for MEG detection may include an MEG instrument and an acquisition workstation located in a magnetic shielding room. The MEG instrument may further include an MEG helmet that at least partially wraps the subject's head. In practical applications, there are both rigid MEG helmets and flexible MEG helmets. On the MEG helmet, a plurality of sensor slots are provided for placing a sensor array for measuring MEG signals. It should be understood that in order to achieve the purpose of measuring the functions of specific brain regions by the MEG instrument, the number of scalp sensors is generally large. For example, the number of sensors may be 64 or 128, and the sensors are evenly distributed on the MEG helmet. However, due to cost considerations, rigid MEG helmets are not customized for each subject in terms of size and shape. Although flexible MEG helmets can adapt to the brain shapes of different subjects to a certain extent, the positions of the sensor arrays will also change with deformation. Therefore, regardless of the type of MEG helmet used, it is necessary to localize the magnetic field signal sources in the MEG data to three-dimensional space and spatially align them with the anatomical images of the human brain such as MRI images, that is, perform registration.

[0023] Figure 2 Shows a schematic diagram of a registration method for an MEG instrument according to the related art. As Figure 2 shown, one registration method in the related art is to manually mark marks on the subject's head by experience. For example, in Figure 2The nose, eyes or ears shown in [description], and then the corresponding markers of different devices (i.e., a magnetoencephalograph, and for example, a nuclear magnetic resonance imaging (MRI) device capable of acquiring brain anatomical images) are respectively placed at the marked positions as reference points before detection, and then a magnetoencephalogram image and an MRI image are obtained respectively. For the two images, the two images are registered by aligning the reference points. However, this registration method is extremely inaccurate, resulting in a large error between the magnetoencephalogram and the true head model of the subject, and it cannot meet the requirements of high-precision registration between the magnetoencephalogram and the true head model, thus restricting its development.

[0024] Based on this, in the embodiments of the present disclosure, the registration of the magnetoencephalograph is indirectly achieved by setting a plurality of markers on the helmet device. To ensure the registration accuracy, the number of the plurality of markers is not less than three.

[0025] Figures 3A - 3C Shows a front view schematic diagram, a left view schematic diagram, and a top view schematic diagram of the markers provided on the magnetoencephalogram helmet according to an embodiment of the present disclosure. As Figures 3A - 3C shown, the front view herein refers to a view that can completely see the entire scalp sensor array of the magnetoencephalogram helmet.

[0026] In some embodiments, the number of the plurality of markers can be 5, and the shapes of the plurality of markers are spherical. When the number of the markers is 5, at least three marker structures can be seen in the front view, top view, and left (right) view, and the markers can be symmetrically arranged on the helmet, which is beneficial to achieving a better registration effect. In a specific arrangement, the magnetoencephalogram helmet can include a helmet body 2 close to the brain surface of the subject, and a plurality of sensor slots 4 are provided on the helmet body for arranging the scalp sensor array. The magnetoencephalogram helmet further includes a helmet shell 3. For Figures 3A - 3C the situation shown, the helmet shell 3 is arranged around the outside of the helmet body 2. On the outer surface of the helmet shell 3, a plurality of (for example, 5) spherical markers are arranged as described above. It should be noted that although an example of a spherical marker is provided in the embodiments of the present disclosure, the shape of the marker is not limited, and the marker can also be cylindrical, cuboid, or other shapes that are easy to identify the marker characteristics.

[0027] Based on the arrangement of the markers, a head structure dataset of the subject wearing the magnetoencephalogram helmet can be obtained and a corresponding helmet surface model can be generated. In order for the helmet surface model to achieve indirect registration, the head structure dataset needs to contain both the characteristics of the subject's face, i.e., the first facial characteristics, and the characteristics of the markers placed on the outer surface of the helmet device, i.e., the first marker characteristics. How to achieve indirect registration through the first facial characteristics and the first marker characteristics will be described in detail below.

[0028] In some embodiments, the head structure data set is a three-dimensional point cloud obtained by scanning with a three-dimensional scanner. The three-dimensional point cloud includes a large amount of three-dimensional point information of the subject's facial area and multiple marker areas corresponding to the first facial feature and the first marker feature, and each three-dimensional point information includes X, Y, and Z coordinates. Optionally, each three-dimensional point information may also include color information, classification value, intensity value, time, etc. By performing surface reconstruction on the head structure data set in the form of a three-dimensional point cloud, a helmet face model can be obtained.

[0029] In some embodiments, the head structure data set is a plurality of head images obtained by camera shooting. The plurality of head images can be a plurality of pictures taken directly by the camera of the subject putting the head into the brain magnetic helmet device at multiple angles, or a video of the subject putting the head into the brain magnetic helmet device, and the video is frame-processed to extract a plurality of pictures. When collecting a plurality of head images according to the method of the embodiment of the present disclosure, each head image should be clear and free of artifacts.

[0030] Based on multiple head images captured by the camera, Figure 5 FIG. 4 is a flowchart showing a partial process of generating a helmet face model according to an embodiment of the present disclosure. Figure 5 As shown, step S110 further includes: Step S510: removing pixels outside the main area of ​​the multiple head images to obtain multiple post-processed head images, where the main area includes the area of ​​the subject's face and the areas of the multiple landmarks; Step S520: performing sparse feature extraction on the post-processed multiple head images to obtain multiple second head images; Step S530: sparsely reconstruct the multiple second head images to obtain a three-dimensional point cloud image of the helmet surface and camera pose parameters.

[0031] In step S510, the multiple head images are post-processed, and the post-processing mainly includes eliminating pixels in non-subject areas, wherein the subject area is the area of ​​the subject's face corresponding to the first facial feature and the first marker feature required in the indirect registration method of the embodiment of the present disclosure and the areas of each of the multiple markers.

[0032] In steps S520 and S530, for multiple post-processed head images, sparse feature extraction and matching are performed. Through sparse feature extraction, the multiple post-processed head images are compressed and dimension-reduced. Further, through sparse feature matching, sparse reconstruction can be achieved, thereby generating a three-dimensional point cloud map of the helmet surface and obtaining camera pose parameters. In sparse reconstruction, points with too large errors can be removed by minimizing the reprojection error. For sparse feature extraction, the file paths of the multiple post-processed head images can be input into the COLMAP (COLLISION-MAPping) software, and COLMAP completes the sparse feature extraction. COLMAP is a highly integrated three-dimensional reconstruction pipeline tool. It can support recovering the geometric structure (point cloud) of a three-dimensional scene and the camera pose corresponding to each image from an unordered or ordered set of two-dimensional images. In addition, other known software for extracting sparse features can also be applied to the embodiments of the present disclosure, such as Agisoft Metashape, RealityCapture, OpenMVG, VisualSFM, etc.

[0033] For the three-dimensional point cloud map of the helmet surface, dense reconstruction can be performed on it to generate a helmet surface model. Specifically, first, the helmet surface depth map and the helmet surface normal map need to be calculated and fused into the three-dimensional point cloud map of the helmet surface. For the fused three-dimensional point cloud map of the helmet surface, a dense surface is estimated using Poisson or triangulation reconstruction methods, and finally, a helmet surface model is obtained.

[0034] For the generated three-dimensional point cloud map of the helmet surface and the obtained camera pose parameters, a helmet surface model can also be generated using Neural Radiance Fields (NeRF). Specifically, Figure 6 A flowchart showing a part of the process of generating a helmet surface model according to an embodiment of the present disclosure is shown. As Figure 6 shown, step S110 further includes: Step S610: Input multiple head images and camera pose parameters into the neural radiance field network model; Step S620: Output a head rendering model after the neural radiance field network model converges; Step S630: Extract a helmet surface model according to the head rendering model.

[0035] In step S610, a neural radiance field is a computer vision technique used to generate high-quality 3D reconstruction models. It utilizes deep learning techniques to extract the geometric shape and texture information of an object from images taken from multiple viewpoints, and then uses this information to generate a continuous 3D radiance field, enabling the presentation of a highly realistic 3D model at any angle and distance. The advantages of the neural radiance field technique are that the generated 3D model has high quality and high fidelity, and can present the real object surface and texture details at any angle and distance. In addition, it can generate 3D models from any number of input images. For step S610, the obtained camera pose parameters and multiple head images need to be converted into a json format readable by NeRF, and the converted data is input into the neural radiance field network for training.

[0036] Next, in step S620, training is started and stopped until the model converges, obtaining a function representing the 3D scene of the head. Also, the 3D scene of the head after model convergence is converted into a visual image using volume rendering technology to obtain a head rendering model.

[0037] In step S630, a helmet surface model is extracted according to the head rendering model. Figure 7 A flowchart showing a partial process of extracting a helmet surface model according to a head rendering model according to an embodiment of the present disclosure is shown. As Figure 7 shown, step S630 further includes: Step S710: Remove patches outside the main body area in the head rendering model to obtain the post-processed head rendering model; Step S720: Generate a head point cloud map according to the post-processed head rendering model; Step S730: Extract the helmet surface model according to the head point cloud map.

[0038] In step S710, since a complete picture is used to input into the neural radiance field network for training in step S610, the obtained head rendering model is a 3D model that completely includes the subject's head and multiple markers. Therefore, it is first necessary to adjust the model domain space size, perform rotation and segmentation, remove patches outside the non-main body area, and grid it to obtain the post-processed head rendering model.

[0039] In steps S720 and S730, first, the post-processed head rendering model is point clouded, the head point cloud map is downsampled and filtered, and the surface is estimated from the point cloud using Poisson or triangulation reconstruction methods to finally obtain the helmet surface model.

[0040] Continue to refer to Figure 1, in step S120, it is necessary to generate a scalp surface model based on the brain MRI data of the subject, that is, the real head model of the subject. Figure 8 shows a flowchart of a part of the process of generating a scalp surface model according to an embodiment of the present disclosure. As Figure 8 shown, in some embodiments, S120 further includes: Step S810: Obtain the T1-weighted imaging in the brain MRI data; Step S820: Convert the T1-weighted imaging into a scalp point cloud map; Step S830: Perform surface reconstruction on the scalp point cloud map to generate a scalp surface model.

[0041] In step S810, the subject is subjected to a brain magnetic resonance examination, and the T1-weighted imaging is obtained. Among them, the magnetic resonance image includes T1-weighted imaging and T2-weighted imaging, and the T1-weighted imaging can better observe the anatomical structure, so it is used for the generation of the scalp surface model in the embodiment of the present disclosure.

[0042] In step S820, the T1-weighted imaging of the subject is converted into a scalp point cloud map, so that in the subsequent model reconstruction step S830, the scalp point cloud map is used to generate a scalp surface model.

[0043] In the embodiment of the present disclosure, the scalp point cloud map can be reconstructed by the method disclosed above, or the model can be reconstructed in a known manner. For the model reconstruction method, no specific limitation is made herein. In the generated scalp surface model, there are second facial features corresponding to the first facial features.

[0044] Continue to refer to Figure 1 , in step S130, according to the design parameters of the magnetoencephalography helmet, a helmet device model is obtained. The design parameters may include the size, shape of the helmet device, and the layout of multiple sensors, etc. In the obtained helmet device model, there are second marker features corresponding to the first marker features.

[0045] In step S140, first, the helmet surface model is registered with the scalp surface model as the real head model. Specifically, the registration is achieved by aligning the first facial features with the second facial features.

[0046] Figure 9 shows a schematic diagram of a part of the process of registering the helmet surface model and the scalp surface model according to an embodiment of the present disclosure. As Figure 9 shown, step S140 includes the following steps: Step S910: Perform rough registration on the helmet surface model based on the global matching of the first facial features and the second facial features; Step S920: Based on the local matching of at least one local feature point in the first facial feature and the second facial feature, perform fine registration on the roughly registered helmet surface model to obtain the registered helmet surface model.

[0047] In step S910, the roughly registered helmet surface model is obtained through the best fit of the first facial feature and the second facial feature.

[0048] In step S920, in the first facial feature and the second facial feature, select the corresponding at least one local feature point, such as the centers of the left and right eyes, the root of the nose, the tip of the nose, the philtrum, etc., and perform fine registration according to the at least one local feature point until the registration error is lower than the set registration accuracy requirement, and obtain the registered helmet surface model. Through the combined steps of first rough registration and then fine registration in the embodiments of the present disclosure, the registration accuracy can be effectively improved.

[0049] Continue to refer to Figure 1 , continuing step S140, in step S150, register the helmet device model with the registered helmet surface. Specifically, the registration is achieved by aligning the registered first marker feature with the second marker feature.

[0050] Figure 10 Shows a schematic diagram of a partial process of registering the helmet device model with the registered helmet surface model according to an embodiment of the present disclosure. As Figure 10 shown, step S150 includes the following steps: Step S1010: Extract a plurality of marker surface features from the registered first marker feature and the second marker feature; Step S1020: Register the helmet device model with the registered helmet surface model according to the plurality of marker surface features to obtain the registered helmet device model.

[0051] In step S1010, for the corresponding multiple groups of registered first marker features and second marker features, first extract the corresponding multiple marker surface features. The number of marker surface features may be the same as or different from the number of markers. And, the extracted marker surface features contain partial or complete surface structure information of the markers.

[0052] In step S1020, the final registration of the helmet device model is achieved through the best fit of the plurality of marker surface features. In some embodiments, for example, the least squares method, the vector least squares method, the least maximum method, and the vector least maximum method can be used for the best fit.

[0053] According to steps S110 - S150, the method 100 of the present disclosure embodiment generates a helmet surface model as a registration intermediate model through a head structure data set collected by a three - dimensional scanner or a camera. The first facial feature and the first marker feature in the helmet surface model are respectively used to align with the second facial feature of the scalp surface model and the second marker feature of the helmet device model, thereby achieving two registrations respectively. The method 100 of the present disclosure embodiment can significantly reduce the scanning time in the registration process and simplify the registration process, thereby promoting the clinical acceptance and commercial application of the magnetoencephalograph for patients.

[0054] Figure 4 The flowchart shows a partial process of the registration method of the magnetoencephalograph according to the embodiment of the present disclosure. After obtaining the registered helmet device model, the method 100 further includes: Step S410: Calibrate a plurality of sensors arranged on the magnetoencephalography helmet according to the helmet device model to determine the first position coordinates and the first orientation coordinates of the plurality of sensors relative to the space of the helmet device model; Step S420: Determine a space transformation matrix for transforming from the space of the helmet device model to the space of the scalp surface model according to the registered helmet device model; Step S430: Apply the space transformation matrix to the first position coordinates and the first orientation coordinates of the plurality of sensors to determine the second position coordinates and the second orientation coordinates of the plurality of sensors relative to the space of the scalp surface model.

[0055] In step S410, first, calibrate a plurality of sensors arranged on the magnetoencephalography helmet according to the helmet device model, so as to obtain the first position coordinates and the first orientation coordinates of the plurality of sensors relative to the space of the helmet device model, that is, obtain the position and orientation information of the sensors relative to the magnetoencephalography helmet itself before registration.

[0056] In step S420, according to the initially unregistered helmet device model and the registered helmet device model, the space transformation matrix for transforming from the space of the helmet device model to the space of the scalp surface model can be obtained.

[0057] Further, in step S430, apply this space transformation matrix, so as to convert both the first position coordinates and the first orientation coordinates into the second position coordinates and the second orientation coordinates that can correspond to the real head model of the subject.

[0058] Secondly, Figure 11 The block diagram shows the registration device 1100 of the magnetoencephalograph according to the embodiment of the present disclosure. The registration device 1100 includes: The first unit 1110 is configured to generate a helmet surface model based on multiple head image structure data sets of the brain magnetic helmet worn by a subject, where the helmet surface model includes the first facial features of the subject and the first marker features corresponding to the markers provided on the brain magnetic helmet; The second unit 1120 is configured to generate a scalp surface model based on the nuclear magnetic data of the subject's brain, and the scalp surface model includes the second facial features of the subject; The third unit 1130 is configured to obtain a helmet device model based on the design parameters of the brain magnetic helmet, and the helmet device model includes the second marker features corresponding to multiple markers; The fourth unit 1140 is configured to register the helmet surface model with the scalp surface model according to the first facial features and the second facial features to obtain the registered helmet surface model, where the registered helmet surface model includes the registered first marker features; The fifth unit 1150 is configured to register the helmet device model with the registered helmet surface model according to the registered first marker features and the second marker features to obtain the registered helmet device model.

[0059] It should be understood that Figure 11 each unit of the registration device 1100 shown in Figure 1 can correspond to each step in the method 100 described in the reference

[0060] According to another aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; where the memory stores a computer program, and the computer program, when executed by the at least one processor, implements the above-described method.

[0061] According to another aspect of the present disclosure, one or more computer-readable storage media are provided, on which instructions are stored, and the instructions, when executed by one or more processors, cause the one or more processors to execute the above-described method.

[0062] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, where the computer program, when executed by a processor, implements the above-described method.

[0063] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0064] The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0065] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0066] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0067] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0068] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0069] Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above methods, systems, and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only defined by the authorized claims and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, the steps can be executed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. Importantly, with the evolution of technology, many of the elements described herein can be replaced by equivalent elements that emerge after this disclosure.

[0070] The above are only the embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A registration method for a magnetoencephalograph, comprising: Generating a helmet surface model based on a head structure dataset of a subject wearing the magnetoencephalograph's magnetic helmet, wherein the helmet surface model includes the first facial features of the subject and first marker features corresponding to a plurality of markers provided on the magnetic helmet; Generating a scalp surface model based on the brain MRI data of the subject, the scalp surface model including the second facial features of the subject; Obtaining a helmet device model according to the design parameters of the magnetic helmet, the helmet device model including second marker features corresponding to the plurality of markers; Registering the helmet surface model with the scalp surface model according to the first facial features and the second facial features to obtain a registered helmet surface model, wherein the registered helmet surface model includes the registered first marker features; Registering the helmet device model with the registered helmet surface model according to the registered first marker features and the second marker features to obtain a registered helmet device model.

2. The method according to claim 1, wherein, After obtaining the registered helmet device model, it further includes: Calibrating a plurality of sensors arranged on the magnetic helmet according to the helmet device model to determine the first position coordinates and the first orientation coordinates of the plurality of sensors relative to the space of the helmet device model; Determining a spatial transformation matrix for transforming from the space of the helmet device model to the space of the scalp surface model according to the registered helmet device model; Applying the spatial transformation matrix to the first position coordinates and the first orientation coordinates to determine the second position coordinates and the second orientation coordinates of the plurality of sensors relative to the space of the scalp surface model.

3. The method according to claim 1, wherein, The head structure dataset is a three-dimensional point cloud obtained by scanning with a three-dimensional scanner.

4. The method according to claim 1, wherein The head structure dataset is a plurality of head images obtained by photographing with a camera.

5. The method according to claim 4, wherein Generating a helmet surface model based on a head structure dataset of a subject wearing the magnetic helmet of the magnetoencephalograph includes: Removing pixels outside the main body area in the plurality of head images to obtain the post-processed plurality of head images, the main body area including the area of the subject's face and the area of the plurality of markers; Performing sparse feature extraction on the post-processed plurality of head images to obtain a plurality of second head images; Performing sparse reconstruction on the plurality of second head images to obtain a helmet surface three-dimensional point cloud map and camera pose parameters.

6. The method according to claim 5, wherein, Generating a helmet surface model based on a head structure dataset of a subject wearing the magnetic helmet of the magnetoencephalograph further includes: Performing dense reconstruction on the helmet surface three-dimensional point cloud map to generate the helmet surface model.

7. The method according to claim 5, wherein Generating a helmet surface model based on a head structure dataset of a subject wearing the magnetic helmet of the magnetoencephalograph further includes: Inputting the plurality of head images and the camera pose parameters into a neural radiance field network model; Outputting a head rendering model after the neural radiance field network model converges; Extracting the helmet surface model according to the head rendering model.

8. The method according to claim 7, wherein Extracting the helmet surface model according to the head rendering model includes: Remove the patches other than the main area in the head rendering model to obtain the post-processed head rendering model; Generate a head point cloud map based on the post-processed head rendering model; Extract the helmet surface model based on the head point cloud map.

9. The method according to any one of claims 1 to 8, wherein; the number of the plurality of markers is not less than three.

10. The method according to any one of claims 1 to 8, wherein, Generating a scalp surface model based on the brain MRI data of the subject includes: Obtain the T1-weighted imaging in the brain MRI data; Convert the T1-weighted imaging into a scalp point cloud map; Perform surface reconstruction on the scalp point cloud map to generate the scalp surface model.

11. The method according to any one of claims 1 to 8, wherein, Align the helmet surface model with the scalp surface model according to the first facial feature and the second facial feature to obtain the aligned helmet surface model, including: Perform rough alignment on the helmet surface model based on the global matching of the first facial feature and the second facial feature; Perform fine alignment on the roughly aligned helmet surface model based on the local matching of at least one local feature point in the first facial feature and the second facial feature to obtain the aligned helmet surface model.

12. The method according to any one of claims 1 to 8, wherein, Align the helmet device model with the aligned helmet surface model according to the aligned first marker feature and the second marker feature to obtain the aligned helmet device model, including: Extract a plurality of marker surface features from the aligned first marker feature and the second marker feature; Align the helmet device model with the aligned helmet surface model according to the plurality of marker surface features to obtain the aligned helmet device model.

13. A registration device for a magnetoencephalograph, wherein, The device includes: A first unit configured to generate a helmet surface model according to a plurality of head structure data sets of a brain magnetic helmet worn by a subject, wherein the helmet surface model includes a first facial feature of the subject and a first marker feature corresponding to a plurality of markers provided on the brain magnetic helmet; A second unit configured to generate a scalp surface model according to the brain MRI data of the subject, the scalp surface model including a second facial feature of the subject; A third unit configured to obtain a helmet device model according to the design parameters of the brain magnetic helmet, the helmet device model including a second marker feature corresponding to the plurality of markers; A fourth unit configured to align the helmet surface model with the scalp surface model according to the first facial feature and the second facial feature to obtain the aligned helmet surface model, wherein the aligned helmet surface model includes the aligned first marker feature; A fifth unit configured to align the helmet device model with the aligned helmet surface model according to the aligned first marker feature and the second marker feature to obtain the aligned helmet device model.

14. An electronic device, the electronic device includes: At least one processor; And A memory communicatively connected to the at least one processor; Wherein The memory stores a computer program, and when the computer program is executed by the at least one processor, it implements the method according to any one of claims 1-12.

15. One or more computer-readable storage media having instructions stored thereon that, in response to execution by one or more processors, cause the one or more processors to perform the method according to any one of claims 1-12.

16. A computer program product, comprising a computer program, wherein, The computer program, when executed by a processor, implements the method according to any one of claims 1-12.

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