Individualized TMS target positioning method and system for spastic cerebral palsy children, equipment and medium

Abstract

The invention provides an individualized TMS target positioning method and system for spastic cerebral palsy children, and aims to realize TMS precise nerve regulation and control for different types of SCP children. According to the method, the structural features of the healthy child are obtained, and after image processing, accurate division of the SCP child brain region is achieved, the defects of a current brain injury child brain region spectrogram are overcome, and a basis is provided for seed point selection of different SCP symptoms; multi-modal image parameter features are further scanned, whole-brain voxel correlation analysis is performed based on seed points to construct functional connection diagrams, the crossed and overlapped functional connection diagrams are searched to serve as a liability focus network of different symptoms, a voxel set close to the scalp surface serves as a TMS target, and SCP child treatment targets of different clinical symptoms are accurately positioned. Based on a focus network mapping technology, functional connection characteristics related to different symptoms or symptom complexes are better positioned, a specific brain network is identified, and an accurate intervention target is provided for TMS treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of target positioning, and in particular to an individualized TMS target positioning method, system, equipment and medium for children with spastic cerebral palsy. Background Art

[0002] Spastic cerebral palsy (SCP) is the most common movement disorder in children, characterized by abnormal movements and postures and limited limb movement, which seriously affects the quality of life of children. SCP still lacks effective prevention strategies and cure methods, and rehabilitation therapy is still an important means to improve the clinical symptoms of children. Children are in a critical period of remodeling after brain injury, and early diagnosis, efficacy evaluation and intervention treatment are crucial for rehabilitation. How to accurately locate the pathogenic brain areas of SCP children and conduct individualized intervention treatment is the key to improving the motor function of children and improving the quality of prognosis.

[0003] Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulatory technique that enhances neural plasticity and improves local blood circulation by regulating the brain's neural electrical and metabolic activities. This technique can effectively promote neural remodeling and improve motor function in children with SCP, and is expected to be used for diagnosis and intervention in the early life of infants to maximize the developmental outcomes of children with SCP. Traditional TMS intervention programs usually select the primary motor cortex (M1 area) as the stimulation target. Compared with the previous "8" coil positioning, the robot-assisted TMS system can improve the accuracy of treatment through target navigation positioning. However, no matter which TMS treatment program is chosen, only some SCP children benefit. This heterogeneity is attributed to the fact that the motor dysfunction of SCP children includes upper limb and lower limb dysfunction, gait abnormality and muscle spasm. A single M1 area stimulation cannot cover complex clinical symptoms, nor can it be individually positioned according to the brain structure and development of each child. Therefore, how to find refined targets for different clinical symptoms of SCP children is a key issue that needs to be solved urgently.

[0004] In the related art, for example, the Chinese invention patent with application number CN201911340696.1 discloses a task-state functional magnetic resonance individualized target positioning method (hereinafter referred to as prior art solution 1); and the Chinese invention patent with application number CN202410931124.5 discloses a rTMS target positioning method and system based on multimodal images (hereinafter referred to as prior art solution 2). The above two prior arts are based on magnetic resonance imaging for TMS target positioning. Prior art solution 1 obtains the degree of amygdala activation and functional connection strength from functional magnetic resonance data, and prior art solution 2 fuses structural images, task-state images and resting-state images to multi-dimensionally characterize brain network connection characteristics and target heterogeneity. Prior art solution 1 mainly involves functional magnetic resonance imaging of amygdala lesions in depression, and a single imaging unilaterally reflects brain network characteristics. Different from the single clear depression lesion in prior art solution 1, the present invention makes a self-made brain map based on image processing and fiber tracking and determines multiple lesion areas of different symptoms in SCP children. Although multimodal image analysis is used in the prior art solution 2, the traditional T1 and T2 sequences are used to describe the structural features, while the present invention uses three modalities, 3DT1, DTI and fMRI, which can not only extract brain network features, but also quantify the damage lesions and white matter features, thereby identifying the relevant responsible lesions. Different from the traditional functional connection methods in the above two technical solutions, the present invention is mainly based on lesion network mapping technology, functional connections are made for seed points, and based on multiple functional connection networks, cross-overlapping functional networks are determined and analyzed, mapped to the lesion responsibility network, and the lesion network mapping relationship of different symptoms of SCP children is generated. Summary of the invention

[0005] In order to achieve the above-mentioned purpose and other advantages of the present invention, the first purpose of the present invention is to provide a method for individualized TMS target location for children with spastic cerebral palsy, comprising the following steps:

[0006] TI, DTI, and fMRI images were obtained from children with spastic CP and age-matched healthy children;

[0007] Extract multimodal image data of healthy children, divide the functions of children's brain regions, and construct maps;

[0008] Extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyze characteristic data of lesions and determine seed points;

[0009] Extract BOLD time series and generate lesion network maps;

[0010] Based on the lesion network mapping relationship and referring to the spatial distribution characteristics of brain functional connections, a set of voxels close to the scalp surface was found as TMS targets.

[0011] Furthermore, the steps of extracting multimodal image data of healthy children, dividing the functions of children's brain regions, and constructing a map include:

[0012] All T1 images were preprocessed;

[0013] Brain tissue segmentation was performed on gray matter, white matter, and cerebrospinal fluid in all images using a joint registration-segmentation method;

[0014] The brain is divided into multiple regions of interest through brain segmentation;

[0015] A feature-based group registration algorithm was employed to align the subjects of each age group into their respective age-specific common space.

[0016] Furthermore, the step of preprocessing all T1 images includes:

[0017] Non-brain tissue was dissected using BET;

[0018] Use ITK-SNAP editing to ensure that the skull is removed accurately;

[0019] Nonparametric nonuniform intensity normalization bias correction was applied to all images;

[0020] T2 images were resampled to a resolution of 1 × 1 × 1 mm3.

[0021] Furthermore, the step of performing brain tissue segmentation on the gray matter, white matter and cerebrospinal fluid of all images by using the joint registration-segmentation method includes:

[0022] Adaptive fuzzy C-means algorithm was used to segment brain tissue images of children of a preset age and align them with images at earlier time points;

[0023] Multiple Gaussian distributions were used to model the grayscale distribution of each brain tissue, refine the segmentation results of children in each age group, and correlate the longitudinal deformation field;

[0024] Remove artifacts or errors that may be introduced by automated segmentation algorithms.

[0025] Furthermore, the step of dividing the brain into a plurality of regions of interest by brain region segmentation includes:

[0026] Register the Colin27 brain image to the brain image of each child of a preset age in the group;

[0027] The registered partition maps were propagated to their corresponding earlier images along the time axis to obtain a set of images for each subject in each age group, including intensity images, TPMs, and anatomical partition maps.

[0028] Furthermore, the step of propagating the registered partition map to its corresponding early image along the time axis includes:

[0029] Using a hierarchical nonlinear deformable registration algorithm, the AAL atlas is registered to each preset age image using the obtained deformation field, propagating to its corresponding earlier image along the time axis.

[0030] Furthermore, the step of using a feature-based group registration algorithm to align the subjects of each age group to their respective age-specific common space includes:

[0031] Group registration using affine transformation;

[0032] The registration was further performed using a feature-based group registration algorithm, which computed the image features of each voxel, including geometric moment invariants of image intensity, edge type, and tissue type;

[0033] Based on the correspondences identified by anatomically unique voxels, the deformation of other non-driven voxels is guided by the deformation of these driven voxels;

[0034] Atlases and TPMs were obtained by averaging the aligned images and obtaining anatomical parcellation maps by majority voting of the aligned parcellation maps.

[0035] Furthermore, the steps of extracting 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyzing characteristic data of lesions and determining seed points include:

[0036] T test was used to evaluate the differences in white matter DTI parameters and gray matter morphology between children with spastic cerebral palsy and healthy children;

[0037] Convert the original DICOM format DTI data into NII format;

[0038] Use the bet command to remove individual non-brain tissues and perform image optimization;

[0039] The white matter fiber bundles were automatically tracked for each voxel in the gray matter volume of the individual subject. When the anisotropy score or the minimum direction angle reached the corresponding threshold, the fiber bundles were automatically stopped from being tracked.

[0040] In the MNI standard space, the fiber start and end ROIs were defined for the average DTI images of each group, and the similarity was compared with the standard fiber bundle probability map, and the fibers with high probability scores were retained;

[0041] Diffusion parameters, including FA and mean diffusivity, were estimated at multiple equidistant points of each fiber tract to generate a white matter fiber tract map;

[0042] Quantitative analysis of lesion space and white matter fiber bundle characteristics provides a basis for seed point selection.

[0043] Furthermore, the step of extracting the BOLD time series and generating a lesion network map includes:

[0044] Extract the individual BOLD signal, calculate the functional connection strength of each node based on the seed point, and construct the functional connection matrix;

[0045] The overlapping parts of the functional network connections with each clinical classification symptom of spastic cerebral palsy were taken as the relevant lesion network to generate a lesion network map.

[0046] The second object of the present invention is to provide an individualized TMS target positioning system for children with spastic cerebral palsy, which implements the above method, including an image acquisition module, a brain region function division and map construction module, a lesion quantitative analysis module, a lesion network mapping generation module, and a TMS target positioning module; wherein,

[0047] The image acquisition module is used to acquire TI, DTI and fMRI images of children with spastic cerebral palsy and healthy children of the same age;

[0048] The brain region function division and atlas construction module is used to extract multimodal image data of healthy children, divide the brain region functions of children, and construct an atlas;

[0049] The lesion quantitative analysis module is used to extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyze characteristic data of lesions and determine seed points;

[0050] The lesion network map generation module is used to extract the BOLD time series and generate a lesion network map;

[0051] The TMS target location module is used to find a voxel set close to the scalp surface as a TMS target based on the lesion network mapping relationship and with reference to the spatial distribution characteristics of brain functional connections.

[0052] A third object of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0053] A fourth object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention proposes a method and system for individualized TMS target localization in SCP children based on lesion network mapping of multimodal magnetic resonance imaging, so as to achieve precise TMS neural regulation for SCP children of different types.

[0056] Since the child's brain is a dynamic development process, there is currently insufficient research on the brain maps of SCP children. The present invention obtains the structural features of healthy children, and after image processing, it can achieve accurate division of the brain areas of SCP children, making up for the shortcomings of the current brain area spectra of brain-damaged children, and providing a basis for the selection of seed points for different symptoms of SCP. On the basis of current traditional research, multimodal imaging parameter features such as 3DT1, DTI and fMRI are further scanned, and a functional connection map is constructed based on the seed point for whole-brain voxel correlation analysis, and cross-overlapping functional connection maps are found as the responsible lesion network for different symptoms. The voxel set close to the scalp surface is used as the TMS target, so as to accurately locate the treatment targets of SCP children with different clinical symptoms. Compared with traditional lesion localization methods, the present invention is based on lesion network mapping technology, which can better locate the functional connection characteristics related to different symptoms or symptom complexes, identify specific brain networks, and provide precise intervention targets for TMS treatment.

[0057] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0059] Figure 1 The process of individualized TMS target location method for children with spastic cerebral palsy in Example 1 Figure 1 ;

[0060] Figure 2 The process of individualized TMS target location method for children with spastic cerebral palsy in Example 1 Figure 2 ;

[0061] Figure 3 A flowchart of brain region function division and map construction in Example 1;

[0062] Figure 4 This is a flow chart of lesion quantitative analysis in Example 1;

[0063] Figure 5 A flow chart for generating a lesion network map of Example 1;

[0064] Figure 6 This is a schematic diagram of the individualized TMS target positioning system for children with spastic cerebral palsy in Example 2;

[0065] Figure 7 This is a schematic diagram of a computer device in Example 3;

[0066] Figure 8 This is a schematic diagram of a computer-readable storage medium of Example 4. DETAILED DESCRIPTION

[0067] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. It should be noted that, under the premise of no conflict, the embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0068] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0069] The figure numbers in this application are only used to distinguish the various steps in the scheme and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0071] The present invention provides a method and system for individualized TMS target positioning for children with spastic cerebral palsy based on multimodal magnetic resonance lesion network mapping. Magnetic resonance diffusion tensor imaging (DTI) combined with 3D-T1WI cortical segmentation technology can quantitatively detect the size and spatial position of intracranial lesions in children with SCP, evaluate the degree of damage and analyze its structural network changes. fMRI can detect changes in brain functional networks in children with SCP. The above-mentioned structural and functional brain maps constructed in combination with multimodality can define regions of interest (ROI) and help formulate personalized treatment plans. Specifically, by combining brain connection features and image registration, new classifiers can be developed to divide children's brain functional partitions. Furthermore, lesion network mapping technology helps to explore damaged brain networks that affect clinical symptoms, providing an important theoretical basis and technical support for accurate target positioning of TMS. This use of neural network analysis methods to guide TMS target selection improves the efficiency and accuracy of treatment, and is expected to improve the personalized treatment effect of SCP children.

[0072] Example 1

[0073] A method for individualized TMS target localization in children with spastic cerebral palsy, such as Figure 1 , Figure 2 As shown, the following steps are included:

[0074] S1. Obtain TI, DTI and fMRI images of children with spastic cerebral palsy and age-matched healthy children;

[0075] S2, extracting multimodal image data of healthy children, dividing the functions of children’s brain regions, and constructing maps;

[0076] In some embodiments, Figure 3 As shown, the steps of extracting multimodal image data of healthy children, dividing the functions of children's brain regions, and constructing a map include:

[0077] S21, preprocessing all T1 images;

[0078] Furthermore, the step of preprocessing all T1 images includes:

[0079] Use BET to strip non-brain tissues such as skull and dura mater;

[0080] Edit using ITK-SNAP to ensure that the skull is removed accurately; for example, manually edit using ITK-SNAP to ensure that the skull is removed accurately.

[0081] Nonparametric nonuniform intensity normalization (N4) bias correction was applied to all images;

[0082] T2 images were resampled to a resolution of 1 × 1 × 1 mm3.

[0083] S22, using the joint registration-segmentation method to perform brain tissue segmentation on the gray matter, white matter and cerebrospinal fluid of all images;

[0084] Furthermore, the step of performing brain tissue segmentation on the gray matter, white matter and cerebrospinal fluid of all images by using the joint registration-segmentation method includes:

[0085] Use the adaptive fuzzy C-means algorithm to segment brain tissue images of children of a preset age (e.g., 12-year-old children) and align them with images at earlier time points;

[0086] Multiple Gaussian distributions were used to model the grayscale distribution of each brain tissue, refine the segmentation results of children in each age group, and correlate the longitudinal deformation field;

[0087] Remove artifacts or errors that may be introduced by automated segmentation algorithms.

[0088] S23, dividing the brain into multiple regions of interest by brain segmentation;

[0089] Furthermore, the step of dividing the brain into a plurality of regions of interest by brain region segmentation includes:

[0090] Colin27 brain images were registered to the brain images of each 12-year-old child in the group;

[0091] The registered zonation maps were propagated to their corresponding earlier images along the time axis. The registration method used a hierarchical nonlinear deformable registration algorithm, and the resulting deformation field was used to register the AAL atlas to each 12-year-old image and propagate it to its corresponding earlier image along the time axis. Finally, a set of images for each subject in each age group was obtained, including intensity images, TPMs, and anatomical zonation maps.

[0092] S24. A feature-based group registration algorithm is used to align the subjects of each age group to their respective age-specific common space.

[0093] Furthermore, the step of using a feature-based group registration algorithm to align the subjects of each age group to their respective age-specific common space includes:

[0094] Group registration using affine transformation;

[0095] The registration was further performed using a feature-based group registration algorithm, which computed the image features of each voxel, including geometric moment invariants of image intensity, edge type, and tissue type;

[0096] Based on the correspondences identified by anatomically unique voxels, the deformation of other non-driven voxels is guided by the deformation of these driven voxels;

[0097] Atlases and TPMs were obtained by averaging the aligned images and obtaining anatomical parcellation maps by majority voting of the aligned parcellation maps.

[0098] S3, extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyze characteristic data of lesions and determine seed points;

[0099] In some embodiments, Figure 4 As shown, the steps of extracting 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyzing feature data of lesions and determining seed points include:

[0100] S31. T-test was used to evaluate the differences in white matter DTI parameters and gray matter morphology between children with spastic cerebral palsy and healthy children;

[0101] S32, converting the DTI data in the original DICOM format into the NII format;

[0102] S33, use the bet command to remove individual non-brain tissue and perform image optimization;

[0103] S34. Automatically track the white matter fiber bundles for each voxel in the gray matter volume of the individual subject. When the anisotropy fraction value or the minimum direction angle reaches the corresponding threshold, the fiber bundle automatically stops tracking; for example, use Automated Fiber Quantification (AFQ; https: / / github.com / jyeatman / AFQ) software to automatically track the white matter fiber bundles for each voxel in the gray matter volume of the individual subject. When the anisotropy fraction (FA) value is less than 0.2 or the minimum direction angle is greater than 30°, the fiber bundle automatically stops tracking.

[0104] S35. In the MNI standard space, define the ROI of the fiber start and end points of the average DTI image of each group, compare the similarity with the standard fiber bundle probability map, and retain the fibers with high probability scores;

[0105] S36, estimating diffusion parameters of multiple equidistant points (e.g., 100 equidistant points) of each fiber bundle, including FA and mean diffusivity (MD), to generate a white matter fiber bundle curve map;

[0106] S37. Quantitative analysis of lesion space and white matter fiber bundle characteristics provides a basis for seed point selection.

[0107] S4, extract BOLD time series and generate lesion network mapping;

[0108] In some embodiments, Figure 5 As shown, the steps of extracting BOLD time series and generating lesion network mapping diagram include:

[0109] S41, extract the individual BOLD signal, calculate the functional connectivity strength (FC) of each node based on the seed point, and construct the functional connectivity matrix;

[0110] S42. The overlapping part of the functional network connection with each clinical classification symptom of spastic cerebral palsy is taken as the relevant lesion network, thereby generating a lesion network map.

[0111] S5. Based on the lesion network mapping relationship and referring to the spatial distribution characteristics of brain functional connections, find the voxel set close to the scalp surface as the TMS target.

[0112] On the other hand, this embodiment also provides a personalized TMS target map for SCP children based on lesion network mapping technology.

[0113] This embodiment provides an individualized TMS target positioning method based on multimodal magnetic resonance lesion network mapping technology, which uses multimodal magnetic resonance technology to achieve accurate positioning of TMS treatment targets for different types of SCP children. Specifically, the functions of children's brain regions are accurately divided based on the self-made T1 and DTI longitudinal brain templates of children in the normal control group, and the brain lesions and white matter fibers are quantitatively evaluated based on multimodal imaging data and automatic fiber bundle tracking. The lesion network mapping relationship is obtained based on the BOLD time series and the whole brain functional connection. Based on the mapping relationship map, the spatial distribution characteristics of brain functional connections are used as a reference to obtain the TMS target positioning results of the subject's brain functional area. The lesion network mapping technology in this embodiment locates related lesions and searches for key imaging markers by analyzing the connection patterns between multiple regions. The core of this technology is that it not only focuses on a single brain injury area, but maps different brain area lesions that cause the same symptoms to a common brain network, which is crucial for revealing neural network mechanisms and identifying neural regulation targets.

[0114] Example 2

[0115] Based on the same concept, this embodiment also provides an individualized TMS target positioning system for children with spastic cerebral palsy, and applies an individualized TMS target positioning method for children with spastic cerebral palsy provided in Example 1. For a detailed description of the individualized TMS target positioning method for children with spastic cerebral palsy provided in Example 1, please refer to the corresponding description of Example 1, which will not be repeated here.

[0116] It is understandable that the individualized TMS target positioning system for children with spastic cerebral palsy provided in this embodiment includes hardware structures and / or software modules corresponding to the execution of each function in order to realize the above functions. In combination with the units and algorithm steps of each example disclosed in this embodiment, this embodiment can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of this embodiment.

[0117] An individualized TMS target localization system for children with spastic cerebral palsy6, such as Figure 6 As shown, it includes an image acquisition module 600, a brain region function division and atlas construction module 610, a lesion quantitative analysis module 620, a lesion network mapping generation module 630, and a TMS target positioning module 640; wherein,

[0118] The image acquisition module is used to acquire TI, DTI and fMRI images of children with spastic cerebral palsy and healthy children of the same age;

[0119] The brain region function division and atlas construction module is used to extract multimodal image data of healthy children, divide the brain region functions of children, and construct an atlas;

[0120] The lesion quantitative analysis module is used to extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyze characteristic data of lesions and determine seed points;

[0121] The lesion network map generation module is used to extract the BOLD time series and generate a lesion network map;

[0122] The TMS target location module is used to find a voxel set close to the scalp surface as a TMS target based on the lesion network mapping relationship and with reference to the spatial distribution characteristics of brain functional connections.

[0123] Based on the technical solution of the above embodiment, optionally, the steps of extracting multimodal image data of healthy children, dividing the functions of children's brain regions, and constructing a map include:

[0124] All T1 images were preprocessed;

[0125] Brain tissue segmentation was performed on gray matter, white matter, and cerebrospinal fluid in all images using a joint registration-segmentation method;

[0126] The brain is divided into multiple regions of interest through brain segmentation;

[0127] A feature-based group registration algorithm was employed to align the subjects of each age group into their respective age-specific common space.

[0128] Based on the technical solution of the above embodiment, optionally, the step of preprocessing all T1 images includes:

[0129] Non-brain tissue was dissected using BET;

[0130] Use ITK-SNAP editing to ensure that the skull is removed accurately;

[0131] Nonparametric nonuniform intensity normalization bias correction was applied to all images;

[0132] T2 images were resampled to a resolution of 1 × 1 × 1 mm3.

[0133] Based on the technical solution of the above embodiment, optionally, the step of performing brain tissue segmentation on the gray matter, white matter and cerebrospinal fluid of all images by using the joint registration-segmentation method includes:

[0134] Adaptive fuzzy C-means algorithm was used to segment brain tissue images of children of a preset age and align them with images at earlier time points;

[0135] Multiple Gaussian distributions were used to model the grayscale distribution of each brain tissue, refine the segmentation results of children in each age group, and correlate the longitudinal deformation field;

[0136] Remove artifacts or errors that may be introduced by automated segmentation algorithms.

[0137] Based on the technical solution of the above embodiment, optionally, the step of dividing the brain into multiple regions of interest by brain region segmentation includes:

[0138] Register the Colin27 brain image to the brain image of each child of a preset age in the group;

[0139] The registered partition maps were propagated to their corresponding earlier images along the time axis to obtain a set of images for each subject in each age group, including intensity images, TPMs, and anatomical partition maps.

[0140] Based on the technical solution of the above embodiment, optionally, the step of propagating the registered partition map to its corresponding early image along the time axis includes:

[0141] Using a hierarchical nonlinear deformable registration algorithm, the AAL atlas is registered to each preset age image using the obtained deformation field, propagating to its corresponding earlier image along the time axis.

[0142] Based on the technical solution of the above embodiment, optionally, the step of using a feature-based group registration algorithm to align the subjects of each age group to their respective specific age common space includes:

[0143] Group registration using affine transformation;

[0144] The registration was further performed using a feature-based group registration algorithm, which computed the image features of each voxel, including geometric moment invariants of image intensity, edge type, and tissue type;

[0145] Based on the correspondences identified by anatomically unique voxels, the deformation of other non-driven voxels is guided by the deformation of these driven voxels;

[0146] Atlases and TPMs were obtained by averaging the aligned images and obtaining anatomical parcellation maps by majority voting of the aligned parcellation maps.

[0147] Based on the technical solution of the above embodiment, optionally, the step of extracting 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyzing feature data of lesions and determining seed points includes:

[0148] T test was used to evaluate the differences in white matter DTI parameters and gray matter morphology between children with spastic cerebral palsy and healthy children;

[0149] Convert the original DICOM format DTI data into NII format;

[0150] Use the bet command to remove individual non-brain tissues and perform image optimization;

[0151] The white matter fiber bundles were automatically tracked for each voxel in the gray matter volume of the individual subject. When the anisotropy score or the minimum direction angle reached the corresponding threshold, the fiber bundles were automatically stopped from being tracked.

[0152] In the MNI standard space, the fiber start and end ROIs were defined for the average DTI images of each group, and the similarity was compared with the standard fiber bundle probability map, and the fibers with high probability scores were retained;

[0153] Diffusion parameters, including FA and mean diffusivity, were estimated at multiple equidistant points of each fiber tract to generate a white matter fiber tract map;

[0154] Quantitative analysis of lesion space and white matter fiber bundle characteristics provides a basis for seed point selection.

[0155] Based on the technical solution of the above embodiment, optionally, the step of extracting the BOLD time series and generating a lesion network map includes:

[0156] Extract the individual BOLD signal, calculate the functional connection strength of each node based on the seed point, and construct the functional connection matrix;

[0157] The overlapping parts of the functional network connections with each clinical classification symptom of spastic cerebral palsy were taken as the relevant lesion network to generate a lesion network map.

[0158] This embodiment provides an individualized TMS target positioning system based on multimodal magnetic resonance lesion network mapping technology, which uses multimodal magnetic resonance technology to achieve accurate positioning of TMS treatment targets for different types of SCP children. Specifically, the functions of children's brain regions are accurately divided based on the self-made T1 and DTI longitudinal brain templates of children in the normal control group, and the brain lesions and white matter fibers are quantitatively evaluated based on multimodal imaging data and automatic fiber bundle tracking. The lesion network mapping relationship is obtained based on the BOLD time series and the whole brain functional connection. Based on the mapping relationship map, the spatial distribution characteristics of brain functional connections are used as a reference to obtain the TMS target positioning results of the subject's brain functional area. The lesion network mapping technology in this embodiment locates related lesions and searches for key imaging markers by analyzing the connection patterns between multiple regions. The core of this technology is that it not only focuses on a single brain injury area, but maps different brain area lesions that cause the same symptoms to a common brain network, which is crucial for revealing neural network mechanisms and identifying neural regulation targets.

[0159] Example 3

[0160] A computer device 700, such as Figure 7 As shown, it includes a memory 710, a processor 720, and a computer program 730 stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for individualized TMS target location for children with spastic cerebral palsy are implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0161] Example 4

[0162] A computer readable storage medium such as Figure 8 As shown, a computer program is stored thereon, and when the computer program is executed by the processor, the steps of a method for individualized TMS target location for children with spastic cerebral palsy are implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, and no further description is given here.

[0163] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0164] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

[0165] The apparatus, computer device, non-volatile computer storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, computer device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, computer device and non-volatile computer storage medium will not be repeated here.

[0166] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software units for implementing the method and structures within the hardware component.

[0167] The systems, devices or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions in various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or more software and / or hardware.

[0168] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0169] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0172] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0173] The specification may be described in the general context of computer-executable instructions executed by a computer, such as program units. Generally, program units include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program units may be located in local and remote computer storage media, including storage devices.

[0174] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0175] The above description is only an embodiment of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification.

Claims

1. A method for individualized TMS target positioning for children with spastic cerebral palsy, characterized in that: The following steps are involved: TI, DTI, and fMRI images were obtained from children with spastic CP and age-matched healthy children; Extract multimodal image data of healthy children, divide the functions of children's brain regions, and construct maps; Extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyze characteristic data of lesions and determine seed points; Extract BOLD time series and generate lesion network maps; Based on the lesion network mapping relationship and referring to the spatial distribution characteristics of brain functional connections, a set of voxels close to the scalp surface was found as TMS targets.

2. The method for individualized TMS target positioning for children with spastic cerebral palsy as claimed in claim 1, characterized in that: The steps of extracting multimodal image data of healthy children, dividing the functions of children's brain regions, and constructing a map include: All T1 images were preprocessed; Brain tissue segmentation was performed on gray matter, white matter, and cerebrospinal fluid in all images using a joint registration-segmentation method; The brain is divided into multiple regions of interest through brain segmentation; A feature-based group registration algorithm was employed to align the subjects of each age group into their respective age-specific common space.

3. The method for individualized TMS target positioning for children with spastic cerebral palsy as claimed in claim 2, characterized in that: The step of preprocessing all T1 images comprises: Non-brain tissue was dissected using BET; Use ITK-SNAP editing to ensure that the skull is removed accurately; Nonparametric nonuniform intensity normalization bias correction was applied to all images; T2 images were resampled to a resolution of 1 × 1 × 1 mm3.

4. The method for individualized TMS target positioning for children with spastic cerebral palsy as claimed in claim 2, characterized in that: The step of performing brain tissue segmentation on the gray matter, white matter and cerebrospinal fluid of all images by using the joint registration-segmentation method includes: Adaptive fuzzy C-means algorithm was used to segment brain tissue images of children of a preset age and align them with images at earlier time points; Multiple Gaussian distributions were used to model the grayscale distribution of each brain tissue, refine the segmentation results of children in each age group, and correlate the longitudinal deformation field; Remove artifacts or errors that may be introduced by automated segmentation algorithms.

5. The method for individualized TMS target positioning for children with spastic cerebral palsy as claimed in claim 2, characterized in that: The step of dividing the brain into a plurality of regions of interest by brain region segmentation comprises: Register the Colin27 brain image to the brain image of each child of a preset age in the group; The registered partition maps were propagated to their corresponding earlier images along the time axis to obtain a set of images for each subject in each age group, including intensity images, TPMs, and anatomical partition maps.

6. The method for individualized TMS target positioning for children with spastic cerebral palsy as claimed in claim 5, characterized in that: The step of propagating the registered partition map to its corresponding early image along the time axis includes: Using a hierarchical nonlinear deformable registration algorithm, the AAL atlas is registered to each preset age image using the obtained deformation field, propagating to its corresponding earlier image along the time axis.

7. The method for individualized TMS target positioning for children with spastic cerebral palsy as claimed in claim 2, characterized in that: The steps of using a feature-based group registration algorithm to align subjects of each age group to their respective age-specific common space include: Group registration using affine transformation; The registration was further performed using a feature-based group registration algorithm, which computed the image features of each voxel, including geometric moment invariants of image intensity, edge type, and tissue type; Based on the correspondences identified by anatomically unique voxels, the deformation of other non-driven voxels is guided by the deformation of these driven voxels; Atlases and TPMs were obtained by averaging the aligned images and obtaining anatomical parcellation maps by majority voting of the aligned parcellation maps.

8. The method for individualized TMS target positioning for children with spastic cerebral palsy as claimed in claim 1, characterized in that: The steps of extracting 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyzing characteristic data of lesions and determining seed points include: T test was used to evaluate the differences in white matter DTI parameters and gray matter morphology between children with spastic cerebral palsy and healthy children; Convert the original DICOM format DTI data into NII format; Use the bet command to remove individual non-brain tissues and perform image optimization; The white matter fiber bundles were automatically tracked for each voxel in the gray matter volume of the individual subject. When the anisotropy score or the minimum direction angle reached the corresponding threshold, the fiber bundles were automatically stopped from being tracked. In the MNI standard space, the fiber start and end ROIs were defined for the average DTI images of each group, and the similarity was compared with the standard fiber bundle probability map, and the fibers with high probability scores were retained; Diffusion parameters, including FA and mean diffusivity, were estimated at multiple equidistant points of each fiber tract to generate a white matter fiber tract map; Quantitative analysis of lesion space and white matter fiber bundle characteristics provides a basis for seed point selection.

9. The method for individualized TMS target positioning for children with spastic cerebral palsy as claimed in claim 1, characterized in that: The step of extracting the BOLD time series and generating a lesion network map comprises: Extract individual BOLD signals, calculate the functional connection strength of each node based on the seed point, and construct a functional connection matrix; The overlapping parts of the functional network connections with each clinical classification symptom of spastic cerebral palsy were taken as the relevant lesion network to generate a lesion network map.

10. An individualized TMS target positioning system for children with spastic cerebral palsy, implementing the method according to any one of claims 1 to 9, characterized in that: It includes image acquisition module, brain region function division and atlas construction module, lesion quantitative analysis module, lesion network mapping module, and TMS target positioning module; among them, The image acquisition module is used to acquire TI, DTI and fMRI images of children with spastic cerebral palsy and healthy children of the same age; The brain region function division and atlas construction module is used to extract multimodal image data of healthy children, divide the brain region functions of children, and construct an atlas; The lesion quantitative analysis module is used to extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantitatively analyze characteristic data of lesions and determine seed points; The lesion network map generation module is used to extract the BOLD time series and generate a lesion network map; The TMS target location module is used to find a voxel set close to the scalp surface as a TMS target based on the lesion network mapping relationship and with reference to the spatial distribution characteristics of brain functional connections.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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