Method and apparatus for finding hand movement hotspots based on a brain network group atlas

Abstract

The present invention provides a method and device for finding hand movement hotspots based on a brain network group atlas. Among them, the method determines the hand movement area by registering the brain network group atlas to the medical image data of the subject; plans an N*N positioning target matrix on the scalp of the subject according to the hand movement area; positions the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target of the positioning target matrix one by one; obtains the maximum peak-to-peak value at each positioning target; and determines the hand movement hotspots according to the position of each positioning target and the corresponding maximum peak-to-peak value. The present invention combines the medical data of the subject, the brain network group atlas, the visual sensor, the robotic arm, the transcranial magnetic stimulator, etc. to realize the function of automatically finding hand movement hotspots, thereby saving manpower and operation time and improving the treatment efficiency.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular, to a method and device for finding hand movement hotspots based on a brain network group atlas. Background Art

[0002] Transcranial Magnetic Stimulation (TMS) is a non-invasive neuromodulation technique used in the study of human neurophysiology and the treatment of nervous system diseases. The Hand Motor Hotspot (hMHS) refers to the cerebral cortex where motor evoked potentials (MEPs) are most easily elicited under TMS and is used to determine the resting or active motor threshold (MT) of an individual subject. The stimulation intensity for inducing MEP is often used as a reference value for determining the individual-specific stimulation intensity. Therefore, determining the hand movement hotspot is a common operation in TMS treatment, and the positioning accuracy directly affects the stimulation parameters and thus the efficacy of TMS. In existing clinical practices of transcranial magnetic stimulation, finding hMHS is generally done manually. Medical staff manually adjust the coil position according to the navigation system, constantly trial and error, and judge whether they have found it based on the subject's response to the stimulation; and the coil has a certain weight, resulting in a time-consuming and laborious process, reducing the work efficiency of medical staff and increasing the discomfort of the subject. Summary of the Invention

[0003] The present invention provides a method and device for finding hand movement hotspots based on a brain network group atlas to solve the defects of low detection efficiency and increased discomfort of the subject in the prior art, and to achieve saving manpower and operation time and improving the treatment efficiency.

[0004] In a first aspect, the present invention provides a method for finding hand movement hotspots based on a brain network group atlas, including:

[0005] Registering the brain network group atlas to the medical image data of the subject to determine the hand movement area;

[0006] Planning an N*N positioning target matrix on the scalp of the subject according to the hand movement area;

[0007] Positioning the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target of the positioning target matrix;

[0008] Obtaining the maximum peak-to-peak value at each positioning target;

[0009] Determine the hand movement hotspots according to the positions of each of the positioning target points and the corresponding maximum peak-to-peak values.

[0010] Further, according to the method for finding hand movement hotspots based on a brain network group atlas provided by the present invention, wherein the step of registering the brain network group atlas to the medical image data of the subject to determine the hand movement area specifically includes:

[0011] Map the upper limb movement area of the brain network group atlas to the medical image data of the subject, and take the area formed by the projection points of the center of the brain region to the cortical surface on the medical image of the subject as the hand movement area.

[0012] Further, according to the method for finding hand movement hotspots based on a brain network group atlas provided by the present invention, wherein the step of planning an N*N positioning target point matrix on the scalp of the subject according to the hand movement area specifically includes:

[0013] Take the central position of the manual movement area as the initial target point;

[0014] Construct an initial path according to the initial target point and the direction of the point normal vector of the initial target point;

[0015] Take the intersection point of the initial path and the scalp three-dimensional model as the central position;

[0016] Construct an N*N planar target point matrix based on the central position;

[0017] Construct an initial target point matrix according to the N*N planar target point matrix;

[0018] Determine the nearest point of each target point on the scalp surface according to the initial target point matrix;

[0019] Determine the N*N positioning target point matrix according to the nearest points.

[0020] Further, according to the method for finding hand movement hotspots based on a brain network group atlas provided by the present invention, wherein the step of positioning the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target point of the positioning target point matrix specifically includes:

[0021] Construct a spatial mapping relationship between the subject and the robotic arm;

[0022] Obtain the relative movement result of the subject's head monitored by the visual sensor in real time;

[0023] Modify the spatial movement relationship according to the relative movement result to obtain the final spatial transformation relationship;

[0024] Position the coil of the transcranial magnetic stimulator provided on the robotic arm at each positioning target point of the positioning target matrix according to the spatial conversion relationship.

[0025] Furthermore, according to the method for finding the hand movement hot spot based on the brain network group atlas provided by the present invention, wherein determining the hand movement hot spot according to the position of each positioning target point and the corresponding maximum peak-to-peak value specifically includes:

[0026] Obtain the first three-dimensional coordinates of each positioning target point in the initial target matrix;

[0027] Convert the first three-dimensional coordinates into spherical coordinates;

[0028] Perform three-dimensional surface fitting according to the two-dimensional coordinates composed of the zenith angle and the azimuth angle in the spherical coordinates and the maximum peak-to-peak value corresponding to each positioning target point to obtain a three-dimensional surface;

[0029] Determine the first two-dimensional coordinates corresponding to the point with the maximum peak value on the three-dimensional surface;

[0030] Convert the first two-dimensional coordinates into second three-dimensional coordinates;

[0031] Determine the point on the subject's scalp closest to the second three-dimensional coordinates as the hand movement hot spot.

[0032] Furthermore, according to the method for finding the hand movement hot spot based on the brain network group atlas provided by the present invention, wherein constructing the spatial mapping relationship between the subject and the robotic arm specifically includes:

[0033] Obtain the first coordinates of the probe with tracking and positioning markers at multiple facial feature points of the subject through a visual sensor;

[0034] Collect the second coordinates of the facial feature points in the subject space;

[0035] Determine a first transformation matrix according to the first coordinates and the second coordinates;

[0036] Obtain the third coordinates of the tracking and positioning markers on the robotic arm at multiple position points near the subject's head in the visual sensor and the fourth coordinates in the robotic arm space;

[0037] Determine a second transformation matrix according to the third coordinates and the fourth coordinates;

[0038] Determine the spatial mapping relationship between the subject space and the robotic arm space according to the first transformation matrix and the second transformation matrix.

[0039] Second aspect, the present invention provides a device for finding hand movement hotspots based on a brain network group atlas, including:

[0040] A manual movement area determination module, configured to register the brain network group atlas to the medical image data of the subject to determine the hand movement area;

[0041] A positioning target matrix determination module, configured to plan an N*N positioning target matrix on the scalp of the subject according to the hand movement area;

[0042] A fixing module, configured to position the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target of the positioning target matrix one by one;

[0043] A peak value acquisition module, configured to acquire the maximum peak-to-peak value at each of the positioning targets;

[0044] A hand movement hotspot acquisition module, configured to determine the hand movement hotspot according to the position of each of the positioning targets and the corresponding maximum peak-to-peak value.

[0045] Third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method for finding hand movement hotspots based on a brain network group atlas as described in any one of the above are implemented.

[0046] Fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for finding hand movement hotspots based on a brain network group atlas as described in any one of the above are implemented.

[0047] Fifth aspect, the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method for finding hand movement hotspots based on a brain network group atlas as described in any one of the above are implemented.

[0048] The method and device for finding hand movement hotspots based on a brain network group atlas provided by the present invention determine the hand movement area by registering the medical image data of the subject to the brain network group atlas; plan an N*N positioning target matrix on the scalp of the subject according to the hand movement area; position the coil of the transcranial magnetic stimulator arranged on the robotic arm to each target of the positioning target matrix; acquire the maximum peak-to-peak value at each of the positioning targets; and determine the hand movement hotspot according to the position of each of the positioning targets and the corresponding maximum peak-to-peak value. The present invention combines the medical data of the subject, the brain network group atlas, the visual sensor, the robotic arm, the transcranial magnetic stimulator, etc. to realize the function of automatically finding hand movement hotspots, thereby saving manpower and operation time and improving the treatment efficiency. Brief Description of the Drawings

[0049] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 is a schematic flowchart of a method for finding hand movement hotspots based on a brain network group atlas provided by the present invention;

[0051] Figure 2 is a schematic diagram of a method for determining hand movement hotspots according to the positions of each of the positioning target points and the corresponding maximum peak-to-peak values provided by the present invention;

[0052] Figure 3 is a schematic structural diagram of a device for finding hand movement hotspots based on a brain network group atlas provided by the present invention;

[0053] Figure 4 is a schematic structural diagram of an electronic device provided by the present invention. Detailed Embodiments

[0054] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0055] The following will describe in conjunction with Figure 1 a method for finding hand movement hotspots based on a brain network group atlas of the present invention, including:

[0056] Step 100: Register the brain network group atlas to the medical image data of the subject to determine the hand movement area;

[0057] Specifically, register the medical image data of the subject to the brain network group atlas to determine the movement area and the position of the hand knot.

[0058] Step 200: Plan an N*N positioning target point matrix on the scalp of the subject according to the hand movement area;

[0059] Specifically, plan an N*N positioning target point matrix on the scalp of the subject according to the hand movement area for collecting electromyography signals on each target point matrix.

[0060] Step 300: Position the coils of the transcranial magnetic stimulator arranged on the robotic arm one by one at each positioning target point of the positioning target matrix;

[0061] Specifically, fix the coil of the transcranial magnetic stimulator on the bracket at the end of the robotic arm with a tracking and positioning marker, and the robotic arm automatically positions to the target point of the target matrix. Each time a target point is positioned, control the transcranial magnetic stimulator to automatically stimulate.

[0062] Step 400: Obtain the maximum peak-to-peak value at each of the positioning target points;

[0063] Specifically, control the transcranial magnetic stimulator to automatically give single-pulse stimulation, and at the same time collect the myoelectric signals of the subject at this target point, and calculate the maximum peak-to-peak value of the myoelectric signals.

[0064] Step 500: Determine the hand movement hot spot according to the position of each of the positioning target points and the corresponding maximum peak-to-peak value.

[0065] Specifically, after all the target points of the target matrix are positioned, stimulated, and the peak-to-peak values of the myoelectric signals are calculated, perform surface fitting to calculate the point with the maximum peak-to-peak value in the area covered by the target matrix, and this point is the found hand movement hot spot.

[0066] A method for finding a hand movement hot spot based on a brain network group atlas provided by the present invention determines the hand movement area by registering the medical image data of the subject to the brain network group atlas; plans an N*N positioning target matrix on the scalp of the subject according to the hand movement area; positions the coils of the transcranial magnetic stimulator arranged on the robotic arm at each target point of the positioning target matrix; obtains the maximum peak-to-peak value at each of the target points; determines the hand movement hot spot according to the position of each of the target points and the corresponding maximum peak-to-peak value. The present invention combines the medical data of the subject, the brain network group atlas, the visual sensor, the robotic arm, the transcranial magnetic stimulator, etc. to realize the function of automatically finding the hand movement hot spot, thereby saving manpower and operation time and improving the treatment efficiency.

[0067] Further, according to the method for finding a hand movement hot spot based on a brain network group atlas provided by the present invention, wherein the registering the brain network group atlas to the medical image data of the subject to determine the hand movement area specifically includes:

[0068] Map the upper limb movement area of the brain network group atlas to the medical image data of the subject, and take the area formed by the projection points of the center of the brain region to the cortical surface in the medical image of the subject as the hand movement area.

[0069] Specifically, the brain network group atlas is registered onto the medical image data of the subject, and the hand knot position is determined. It is characterized in that the motor areas of the brain network group atlas (numbered 57 on the left and 58 on the right) are mapped onto the individual image, and the projection point from the center of the brain region to the cortical surface is taken as the central target, that is, the hand motor area.

[0070] Furthermore, according to the method for finding hand movement hotspots based on the brain network group atlas provided by the present invention, wherein, planning an N*N positioning target matrix on the scalp of the subject according to the hand motor area specifically includes:

[0071] Taking the central position of the manual motor area as the initial target;

[0072] Constructing an initial path according to the initial target and the direction of the point normal vector of the initial target;

[0073] Taking the intersection point of the initial path and the scalp three-dimensional model as the central position;

[0074] Constructing an N*N planar target matrix based on the central position;

[0075] Constructing an initial target matrix according to the N*N planar target matrix;

[0076] Determining the nearest point of each target on the scalp surface according to the initial target matrix;

[0077] Determining the N*N positioning target matrix according to the nearest points.

[0078] Specifically, plan the initial path. On the three-dimensional model of the subject's gray matter, take the central position of the hand motor area as the initial target to plan a path, and the path direction is the direction of the point normal vector of this target. Take this path as the initial path. Since the three-dimensional model is a triangular mesh model, when the initial target is a vertex on the triangular mesh model, its point normal vector is the average value of the normal vectors of all triangular patches sharing this vertex; when the initial target is on a triangular patch, its point normal vector is the normal vector of the triangular patch where this point is located.

[0079] Plan the initial target matrix. Take the intersection points of the initial path and the three-dimensional scalp model as the central positions of the target matrix. Along the direction of the normal vector of the central position of the matrix pointing towards the inner side of the scalp, set the point 500 mm away from the central position of the matrix as the origin of the spherical coordinate system. The direction of the normal vector of the point at the center of the matrix is the Z-axis direction of the spherical coordinate system. At this time, the central position of the matrix is on the spherical surface. Set the row pitch and column pitch of the target matrix to be d mm, set the number of rows and columns of the target matrix to be N, and the plane where the matrix is located is perpendicular to the normal vector of the central position of the matrix. According to the positions of the targets in the matrix and the conversion relationship between the spherical coordinate system and the rectangular coordinate system, project the target matrix onto the spherical surface so that N*N targets are evenly distributed on the spherical surface. Keep the central position of the matrix unchanged and slightly adjust the radius of the sphere where the target matrix is located and the directions of the target matrix as a whole on the X, Y, and Z axes with the center of the matrix as the rotation center, where the X-axis direction is the coil handle direction, so that the targets on the target matrix roughly fall on the scalp surface. The target matrix at this time is the initial target matrix.

[0080] Plan the positioning target matrix. Calculate the points on the scalp surface that are closest to the targets on the target matrix respectively, and the normal vectors of the closest points on the scalp. At this time, the closest points on the scalp are the targets for final positioning, and the direction of their normal vectors is the direction perpendicular to the coil surface when reaching these targets, obtaining an N*N positioning target matrix.

[0081] Furthermore, according to the method for finding hand movement hotspots based on the brain network group atlas provided by the present invention, wherein positioning the coil of the transcranial magnetic stimulator provided on the robotic arm to each target of the positioning target matrix specifically includes:

[0082] Construct the spatial mapping relationship between the subject and the robotic arm;

[0083] Obtain the relative movement results of the subject's head monitored by the visual sensor in real time;

[0084] Correct the spatial movement relationship according to the relative movement results to obtain the final spatial conversion relationship;

[0085] Position the coil of the transcranial magnetic stimulator provided on the robotic arm to each positioning target of the positioning target matrix one by one according to the spatial conversion relationship.

[0086] Specifically, constructing the spatial mapping relationship between the subject and the robotic arm means mutually transforming the subject's space and the robotic arm space to determine the path from the robotic arm to the subject. Subject space registration. Using a probe with tracking and positioning markers, the coordinates of the tip of the probe can be obtained through a vision sensor. Place the tip of the probe at multiple facial feature points of the subject (the number of points ≥ 4), where any 4 points are not coplanar, and collect the coordinates of the facial feature points under the vision sensor. In the medical image space, collect the coordinates of the corresponding facial feature points of the subject in the subject space, and register the coordinates of the two sets of facial feature points through the SVD algorithm to obtain the transformation matrix between the subject space and the vision sensor space

[0087] Robotic arm space registration. Drag the end of the robotic arm to place the tracking and positioning marker on the end tool of the robotic arm within the field of view of the vision sensor, and move it to multiple positions near the subject's head (the number of positions ≥ 4), where any 4 points are not coplanar. Each time it moves to a position, simultaneously collect the coordinates of the tracking and positioning marker in the vision sensor space and the robotic arm space, and register the two sets of coordinates through the SVD algorithm to obtain the transformation matrix between the robotic arm space and the subject space

[0088] According to the results of the transformation matrix between the subject space and the vision sensor space and the transformation matrix between the robotic arm space and the subject space, obtain the transformation relationship between the subject space and the robotic arm space

[0089] During the coil positioning process and after the target point is located, the vision sensor monitors the movement status of the subject's head in real time and feeds it back to the robotic arm. The robotic arm performs real-time pose compensation according to the feedback. After the coil is positioned at the target point, it always adheres closely to the scalp surface to prevent missing the target. The specific steps are as follows: Fix a headband with a tracking and positioning marker on the subject's head to ensure that during the treatment process, the positioning marker on the headband does not move relative to the subject's head. Obtain the pose of the tracking and positioning marker on the headband in the vision sensor space in real time through the vision sensor Calculate the transformation matrix between the headband positioning marker space and the subject space Obtain the pose of the tracking and positioning marker on the headband after movement in the vision sensor space Calculate the change in the pose of the tracking and positioning marker on the headband before and after movement Calculate the change in the pose of the subject before and after movement Calculate the transformation matrix between the subject space and the vision sensor space after movement Calculate the transformation matrix between the robotic arm space and the subject space after movement Calculate the pose of the target point in the space of the robotic arm after movement Thus, the target pose after the robotic arm performs motion compensation at this target point is obtained.

[0090] Furthermore, according to the compensated target pose, determine the motion target of the robotic arm.

[0091] Further, as shown in A-C of Figure 2 , according to the method for finding the hand movement hot spot based on the brain network group atlas provided by the present invention, wherein, determining the hand movement hot spot according to the position of each of the positioning target points and the corresponding maximum peak-to-peak value specifically includes:

[0092] Obtain the first three-dimensional coordinates of each positioning target point in the initial target point matrix;

[0093] Convert the first three-dimensional coordinates into spherical coordinates;

[0094] Perform three-dimensional surface fitting according to the two-dimensional coordinates composed of the zenith angle and the azimuth angle in the spherical coordinates and the maximum peak-to-peak value corresponding to each positioning target point to obtain a three-dimensional surface;

[0095] Determine the first two-dimensional coordinates corresponding to the point with the largest peak value on the three-dimensional surface;

[0096] Convert the first two-dimensional coordinates into second three-dimensional coordinates;

[0097] Determine the point on the scalp of the subject that is closest to the second three-dimensional coordinates as the hand movement hot spot.

[0098] Specifically, for the target points on the sphere in the initial target point matrix, according to the conversion relationship between the three-dimensional rectangular coordinate system and the spherical coordinate system, convert their three-dimensional rectangular coordinates into spherical coordinates. Since on the initial target point matrix, the radius r of the target points in the spherical coordinate system is the same, so the zenith angle θ and the azimuth angle of the spherical coordinates of the target points are used as the two-dimensional coordinates of the target points. Use the converted two-dimensional coordinates of the target points as the x and y coordinates, and combine the maximum peak-to-peak value p of the electromyogram signal at this target point as the z coordinate, and use thin plate spline interpolation method for three-dimensional surface fitting. Find the point with the largest peak-to-peak value on the surface, and use the two-dimensional coordinates of this point combined with the radius r of the spherical coordinates to convert this point into the three-dimensional space. Find the point on the scalp surface that is closest to the above point, and this point is the found hand movement hot spot

[0099] Further, according to the method for finding the hand movement hot spot based on the brain network group atlas provided by the present invention, wherein, constructing the spatial mapping relationship between the subject and the robotic arm specifically includes:

[0100] Obtain the first coordinates of a probe with tracking and positioning markers at multiple facial feature points of the subject through a visual sensor;

[0101] Collect the second coordinates of the facial feature points in the subject space;

[0102] Determine the first transformation matrix according to the first coordinates and the second coordinates;

[0103] Obtain the third coordinates of multiple position points of the tracking and positioning marker on the robotic arm near the subject's head in the visual sensor and the fourth coordinates in the robotic arm space;

[0104] Determine the second transformation matrix according to the third coordinates and the fourth coordinates;

[0105] Determine the spatial mapping relationship between the subject space and the robotic arm space according to the first transformation matrix and the second transformation matrix.

[0106] Specifically, subject space registration. Using a probe with tracking and positioning markers, the coordinates of the tip of the probe can be obtained through a visual sensor. Place the tip of the probe at multiple facial feature points of the subject (the number of points ≥ 4), where any 4 points are not coplanar, and collect the coordinates of the facial feature points under the visual sensor. In the medical image space, collect the coordinates of the corresponding facial feature points of the subject in the subject space, and register the coordinates of the two sets of facial feature points through the SVD algorithm to obtain the transformation matrix between the subject space and the visual sensor space

[0107] Robotic arm space registration. Drag the end of the robotic arm to place the tracking and positioning marker on the tool at the end of the robotic arm within the field of view of the visual sensor, and move it to multiple positions near the subject's head (the number of positions ≥ 4), where any 4 points are not coplanar. Each time it is moved to a position, collect the coordinates of the tracking and positioning marker in the visual sensor space and the robotic arm space at the same time, and register the two sets of coordinates through the SVD algorithm to obtain the transformation matrix between the robotic arm space and the subject space

[0108] According to the results of the transformation matrix between the subject space and the visual sensor space and the transformation matrix between the robotic arm space and the subject space, obtain the transformation relationship between the subject space and the robotic arm space

[0109] As Figure 3 shown, the present invention provides a device for finding hand movement hotspots based on a brain network group atlas, including:

[0110] A manual movement area determination module 31 for registering the brain network group atlas onto the medical image data of the subject to determine the hand movement area;

[0111] A positioning target point matrix determination module 32, configured to plan an N*N positioning target point matrix on the scalp of the subject according to the hand motor area;

[0112] A fixing module 33, configured to position the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target point of the positioning target point matrix one by one;

[0113] A peak value acquisition module 34, configured to acquire the maximum peak-to-peak value at each of the positioning target points;

[0114] A hand movement hot spot acquisition module 35, configured to determine the hand movement hot spot according to the position of each of the positioning target points and the corresponding maximum peak-to-peak value.

[0115] Since the device provided by the embodiment of the present invention can be used to execute the method described in the above embodiment, and its working principle and beneficial effects are similar, details are not described herein again. For specific content, reference can be made to the introduction of the above embodiment.

[0116] A device for finding hand movement hot spots based on a brain network group atlas provided by the present invention determines the hand motor area by registering the medical image data of the subject to the brain network group atlas; plans an N*N positioning target point matrix on the scalp of the subject according to the hand motor area; positions the coil of the transcranial magnetic stimulator arranged on the robotic arm to each target point of the positioning target point matrix; acquires the maximum peak-to-peak value at each of the target points; and determines the hand movement hot spot according to the position of each of the target points and the corresponding maximum peak-to-peak value. The present invention combines the medical data of the subject, the brain network group atlas, the visual sensor, the robotic arm, the transcranial magnetic stimulator, etc., to realize the function of automatically finding the hand movement hot spot, thereby saving manpower and operation time and improving the treatment efficiency.

[0117] Further, for the device for finding hand movement hot spots based on a brain network group atlas provided by the present invention, the hand movement area determination module 31 is specifically configured to:

[0118] Map the upper limb motor area of the brain network group atlas to the medical image data of the subject, and take the area formed by the projection points of the center of the brain region to the cortical surface on the medical image of the subject as the hand motor area.

[0119] Further, for the device for finding hand movement hot spots based on a brain network group atlas provided by the present invention, the positioning target point matrix determination module 32 is specifically configured to:

[0120] Take the central position of the hand movement area as the initial target point;

[0121] Construct an initial path according to the initial target point and the direction of the point normal vector of the initial target point;

[0122] Use the intersection point of the initial path and the three-dimensional scalp model as the central position;

[0123] Construct an N*N planar target matrix based on the central position;

[0124] Construct an initial target matrix according to the N*N planar target matrix;

[0125] Determine the closest point on the scalp surface for each target according to the initial target matrix;

[0126] Determine an N*N positioning target matrix according to the closest points;

[0127] Furthermore, according to the device for finding hand movement hotspots based on the brain network group atlas provided by the present invention, wherein the fixing module 33 is specifically configured to:

[0128] Construct a spatial mapping relationship between the subject and the robotic arm;

[0129] Obtain the relative head movement result of the subject monitored by the visual sensor in real time;

[0130] Modify the spatial movement relationship according to the relative movement result to obtain the final spatial conversion relationship;

[0131] Position the coils of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target of the positioning target matrix one by one according to the spatial conversion relationship.

[0132] Furthermore, according to the device for finding hand movement hotspots based on the brain network group atlas provided by the present invention, wherein the hand movement hotspot acquisition module 35 is specifically configured to:

[0133] Obtain the first three-dimensional coordinates of each positioning target in the initial target matrix;

[0134] Convert the first three-dimensional coordinates into spherical coordinates;

[0135] Perform three-dimensional surface fitting according to the two-dimensional coordinates composed of the zenith angle and the azimuth angle in the spherical coordinates and the maximum peak-to-peak value corresponding to each positioning target to obtain a three-dimensional surface;

[0136] Determine the first two-dimensional coordinates corresponding to the point with the maximum peak value on the three-dimensional surface;

[0137] Convert the first two-dimensional coordinates into second three-dimensional coordinates;

[0138] Determine the point on the subject's scalp closest to the second three-dimensional coordinates as the hand movement hotspot.

[0139] Further, for the apparatus for finding hand movement hotspots based on the brain network group atlas provided by the present invention, the fixing module 33 is further specifically configured to:

[0140] Obtain, through a visual sensor, a first coordinate of a probe with a tracking and positioning marker at multiple facial feature points of a subject;

[0141] Collect a second coordinate of the facial feature points in the space of the subject;

[0142] Determine a first transformation matrix according to the first coordinate and the second coordinate;

[0143] Obtain a third coordinate of a tracking and positioning marker on the robotic arm at multiple position points near the head of the subject in the visual sensor and a fourth coordinate in the space of the robotic arm;

[0144] Determine a second transformation matrix according to the third coordinate and the fourth coordinate;

[0145] Determine a spatial mapping relationship between the space of the subject and the space of the robotic arm according to the first transformation matrix and the second transformation matrix.

[0146] Figure 4 Illustrates a schematic physical structure diagram of an electronic device, as Figure 4 shown. The electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call logical instructions in the memory 430 to execute a method for finding hand movement hotspots based on the brain network group atlas. The method includes: registering medical image data of a subject onto the brain network group atlas to determine a hand movement area; planning an N*N positioning target matrix on the scalp of the subject according to the hand movement area; positioning the coil of a transcranial magnetic stimulator arranged on the robotic arm to each target of the positioning target matrix; obtaining the maximum peak-to-peak value at each target; and determining hand movement hotspots according to the position of each target and the corresponding maximum peak-to-peak value.

[0147] In addition, when the logical instructions in the above-mentioned memory 430 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0148] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a method for finding hand movement hotspots based on a brain network group atlas provided by the above-mentioned various methods. The method includes: registering the medical image data of the subject to the brain network group atlas to determine the hand movement area; planning an N*N positioning target point matrix on the scalp of the subject according to the hand movement area; positioning the coil of the transcranial magnetic stimulator provided on the robotic arm to each target point of the positioning target point matrix; obtaining the maximum peak-to-peak value at each target point; and determining the hand movement hotspots according to the position of each target point and the corresponding maximum peak-to-peak value.

[0149] In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute a method for finding hand movement hotspots based on a brain network group atlas provided by the above-mentioned various methods. The method includes: registering the medical image data of the subject to the brain network group atlas to determine the hand movement area; planning an N*N positioning target point matrix on the scalp of the subject according to the hand movement area; positioning the coil of the transcranial magnetic stimulator provided on the robotic arm to each target point of the positioning target point matrix; obtaining the maximum peak-to-peak value at each target point; and determining the hand movement hotspots according to the position of each target point and the corresponding maximum peak-to-peak value.

[0150] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.

[0151] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements 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 method for finding hand movement hotspots based on a brain network group atlas, characterized in that, Including: Registering the brain network group atlas to the medical image data of the subject to determine the hand motor area; Planning an N*N positioning target matrix on the scalp of the subject according to the hand motor area; Positioning the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target of the positioning target matrix one by one; Obtaining the maximum peak-to-peak value at each positioning target; Determining the hand motor hot spot according to the position of each positioning target and the corresponding maximum peak-to-peak value; The step of planning an N*N positioning target matrix on the scalp of the subject according to the hand motor area specifically includes: Taking the central position of the hand motor area as the initial target; Constructing an initial path according to the initial target and the direction of the point normal vector of the initial target; Taking the intersection point of the initial path and the scalp three-dimensional model as the central position; Constructing an N*N planar target matrix based on the central position; Constructing an initial target matrix according to the N*N planar target matrix; Determining the nearest point of each target on the scalp surface according to the initial target matrix; Determining the N*N positioning target matrix according to the nearest point; The step of determining the hand motor hot spot according to the position of each positioning target and the corresponding maximum peak-to-peak value specifically includes: Obtaining the first three-dimensional coordinates of each positioning target in the initial target matrix; Converting the first three-dimensional coordinates into spherical coordinates; Performing three-dimensional surface fitting according to the two-dimensional coordinates composed of the zenith angle and the azimuth angle in the spherical coordinates and the maximum peak-to-peak value corresponding to each positioning target to obtain a three-dimensional surface; Determining the first two-dimensional coordinates corresponding to the point with the maximum peak value on the three-dimensional surface; Converting the first two-dimensional coordinates into second three-dimensional coordinates; Determining the point on the scalp of the subject closest to the second three-dimensional coordinates as the hand motor hot spot.

2. The method for finding hand movement hotspots based on the brain network group atlas according to claim 1, wherein The step of registering the brain network group atlas to the medical image data of the subject to determine the hand motor area specifically includes: Mapping the upper limb motor area of the brain network group atlas to the medical image data of the subject, and taking the area formed by the projection points of the center of the brain area to the cortical surface in the medical image of the subject as the hand motor area.

3. The method for finding hand movement hotspots based on the brain network group atlas according to claim 1, wherein, The step of positioning the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target of the positioning target matrix one by one specifically includes: Constructing the spatial mapping relationship between the subject and the robotic arm; Obtaining the relative movement result of the subject's head monitored by the visual sensor in real time; Correcting the spatial movement relationship according to the relative movement result to obtain the final spatial conversion relationship; Positioning the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target of the positioning target matrix according to the spatial conversion relationship.

4. The method for finding hand movement hotspots based on the brain network group atlas according to claim 3, wherein The step of constructing the spatial mapping relationship between the subject and the robotic arm specifically includes: Obtaining the first coordinates of the probe with the tracking and positioning markers at multiple facial feature points of the subject through the visual sensor; Collecting the second coordinates of the facial feature points in the space of the subject; Determining the first conversion matrix according to the first coordinates and the second coordinates; Obtain the third coordinates of the tracking and positioning markers on the robotic arm at multiple position points near the subject's head in the visual sensor and the fourth coordinates in the robotic arm space; Determine the second transformation matrix according to the third coordinates and the fourth coordinates; Determine the spatial mapping relationship between the subject space and the robotic arm space according to the first transformation matrix and the second transformation matrix.

5. An apparatus for finding hand movement hotspots based on a brain network group atlas, characterized in that, It includes: A manual motor area determination module, configured to register the brain network group atlas to the medical image data of the subject to determine the hand motor area; A positioning target matrix determination module, configured to plan an N*N positioning target matrix on the scalp of the subject according to the hand motor area; A fixing module, configured to sequentially position the coil of the transcranial magnetic stimulator arranged on the robotic arm to each positioning target of the positioning target matrix; A peak value acquisition module, configured to acquire the maximum peak-to-peak value at each positioning target; A hand movement hot spot acquisition module, configured to determine the hand movement hot spot according to the position of each positioning target and the corresponding maximum peak-to-peak value; The step of planning an N*N positioning target matrix on the scalp of the subject according to the hand motor area specifically includes: Taking the central position of the hand motor area as the initial target; Constructing an initial path according to the initial target and the direction of the point normal vector of the initial target; Taking the intersection point of the initial path and the scalp three-dimensional model as the central position; Constructing an N*N planar target matrix based on the central position; Constructing an initial target matrix according to the N*N planar target matrix; Determining the closest point on the scalp surface of each target according to the initial target matrix; Determining the N*N positioning target matrix according to the closest point; The step of determining the hand movement hot spot according to the position of each positioning target and the corresponding maximum peak-to-peak value specifically includes: Obtaining the first three-dimensional coordinates of each positioning target in the initial target matrix; Converting the first three-dimensional coordinates into spherical coordinates; Performing three-dimensional surface fitting according to the two-dimensional coordinates composed of the zenith angle and the azimuth angle in the spherical coordinates and the maximum peak-to-peak value corresponding to each positioning target to obtain a three-dimensional surface; Determining the first two-dimensional coordinates corresponding to the point with the maximum peak value on the three-dimensional surface; Converting the first two-dimensional coordinates into second three-dimensional coordinates; Determining the point on the scalp of the subject closest to the second three-dimensional coordinates as the hand movement hot spot.

6. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for finding the hand movement hot spot based on the brain network group atlas according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for finding the hand movement hot spot based on the brain network group atlas according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for finding the hand movement hot spot based on the brain network group atlas according to any one of claims 1 to 4.

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