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

Abstract

The present invention provides a method and system for individualized TMS target positioning for children with spastic cerebral palsy, so as to achieve precise neuromodulation of TMS for children with SCP of different types. The present invention obtains the structural characteristics of healthy children, and after image processing, achieves accurate division of the brain areas of SCP children, which makes up for the shortcomings of the current brain area spectra of children with brain damage and provides a basis for the selection of seed points for different symptoms of SCP; further scans the multimodal imaging parameter characteristics, and constructs a functional connection map based on the whole brain voxel correlation analysis of the seed points, and searches for cross-overlapping functional connection maps as the responsible lesion network for different symptoms, and uses the voxel set close to the scalp surface as the TMS target to accurately locate the treatment target for SCP children with different clinical symptoms. Based on the lesion network mapping technology, the present invention better locates the functional connection characteristics related to different symptoms or symptom complexes, identifies specific brain networks, and provides precise intervention targets for TMS treatment.

Description

Technical Field

[0001] The present invention relates to the field of target positioning technology, and in particular to a method, system, equipment and medium for individualized TMS target positioning in 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 mobility, which seriously affects the quality of life of children with SCP. SCP still lacks effective prevention strategies and cures, and rehabilitation therapy remains an important means of improving clinical symptoms in children. Children are in a critical period of remodeling after brain injury, and early diagnosis, efficacy evaluation, and intervention are crucial for rehabilitation. Accurately locating the diseased brain areas in children with SCP and implementing individualized interventions are key to improving children's motor function and enhancing their quality of life.

[0003] Transcranial magnetic stimulation (TMS) is a noninvasive neuromodulatory technique that modulates brain electrical and metabolic activity to enhance neuroplasticity and improve regional blood circulation. This technique can effectively promote neural remodeling and improve motor function in children with SCP. It has the potential to be used for diagnosis and intervention early in life, maximizing developmental outcomes. Traditional TMS interventions typically target the primary motor cortex (M1 area). Compared to conventional figure-of-eight coil positioning, robotic-assisted TMS systems improve treatment accuracy through target navigation. However, regardless of the chosen TMS treatment approach, only a subset of children with SCP benefit. This heterogeneity is attributed to the fact that motor dysfunction in children with SCP encompasses a range of upper and lower limb dysfunction, gait abnormalities, and muscle spasticity. Single M1 stimulation cannot encompass the complex clinical landscape, nor can it be tailored to each child's brain structure and developmental profile. Therefore, identifying targeted treatments tailored to the diverse clinical symptoms of children with SCP remains a critical issue.

[0004] In the related art, for example, the Chinese invention patent with application number CN201911340696.1 discloses a task-state functional magnetic resonance imaging individualized target positioning method (hereinafter referred to as prior art solution 1); and the Chinese invention patent with application number CN202410931124.5 discloses an rTMS target positioning method and system based on multimodal images (hereinafter referred to as prior art solution 2). Both of the above 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 imaging 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 one-sidedly reflects the characteristics of the brain network. Unlike the single clear depression lesion in prior art solution 1, the present invention is based on image processing and fiber tracking to make a self-made brain map and determine multiple lesion areas with different symptoms in SCP children. While prior art solution 2 utilizes multimodal image analysis, it uses traditional T1 and T2 sequences to describe structural features. The present invention, however, utilizes three modalities: 3DT1, DTI, and fMRI. This not only extracts brain network features but also quantifies lesion and white matter features, thereby identifying the responsible lesions. Unlike the traditional functional connectivity methods used in the aforementioned two solutions, the present invention primarily relies on lesion network mapping technology. Functional connectivity is performed on seed points. Based on multiple functional connectivity networks, overlapping functional networks are identified and analyzed, mapped onto the responsible lesion network, and generated lesion network mapping relationships for different symptoms in children with SCP. Summary of the Invention

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

[0006] TI, DTI, and fMRI images were obtained from children with spastic cerebral palsy 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 the characteristic data of lesions and determine the 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 connectivity, a set of voxels close to the scalp surface was found as the TMS target.

[0011] Furthermore, the steps of extracting multimodal image data of healthy children, dividing the functions of children's brain regions, and constructing an atlas 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 accurately removed;

[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 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 multiple regions of interest by brain region segmentation includes:

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

[0027] The registered partition maps were propagated along the time axis to their corresponding earlier images, and a set of images for each subject in each age group was obtained, 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 and propagated to its corresponding earlier image along the time axis.

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

[0031] Group registration using affine transformation;

[0032] The feature-based group registration algorithm further aligns and calculates the image features of each voxel, including image intensity, edge type, and geometric moment invariants of 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 feature 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] Automatic white matter fiber tract tracking is performed for each voxel within the gray matter volume of an individual subject. When the anisotropy score or the minimum direction angle reaches the corresponding threshold, the fiber tract tracking is automatically stopped.

[0040] In the MNI standard space, the fiber starting and ending 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 along each fiber bundle to generate a white matter fiber tractogram.

[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 individual BOLD signals, calculate the functional connectivity strength of each node based on the seed point, and construct a functional connectivity matrix;

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

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

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

[0049] The lesion quantification analysis module is used to extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantify the lesion feature data and determine the seed point;

[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 children with SCP based on lesion network mapping using multimodal magnetic resonance imaging, so as to achieve precise TMS neuromodulation for children with SCP of different types.

[0056] Since the child's brain is a dynamic development process, there is currently insufficient research on the brain maps of children with SCP. The present invention obtains the structural characteristics of healthy children, and after image processing, it can achieve accurate division of the brain areas of children with SCP, making up for the shortcomings of the current brain area spectra of children with brain damage, and providing a basis for the selection of seed points for different symptoms of SCP. On the basis of current traditional research, further scanning of multimodal imaging parameter features such as 3DT1, DTI and fMRI, and whole-brain voxel correlation analysis based on seed points are used to construct functional connection maps, 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 target 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 to implement it according to the contents of the description, the following preferred embodiments of the present invention are described in detail with reference to the accompanying drawings. The specific implementation methods of the present invention are given in detail by the following embodiments and the 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 positioning method for children with spastic cerebral palsy in Example 1 Figure 1 ;

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

[0061] Figure 3 A flowchart of brain region functional 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 the lesion network map of Example 1;

[0064] Figure 6 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 the computer equipment of Example 3;

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

[0067] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. It should be noted that, without conflict, the embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0068] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this 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 this specification of the present invention 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 personalized 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 location of intracranial lesions in children with SCP, assess the degree of damage and analyze its structural network changes. fMRI can detect changes in the functional brain networks of children with SCP. The above-mentioned structural and functional brain maps constructed by combining multimodality can define regions of interest (ROIs) 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 regions. Furthermore, lesion network mapping technology helps to explore damaged brain networks that affect clinical symptoms, providing an important theoretical basis and technical support for the precise 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 enhance the personalized treatment effect of children with SCP.

[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. Extract multimodal image data of healthy children, divide the functions of children's brain regions, and construct an atlas;

[0076] In some embodiments, as Figure 3 As shown, the steps of extracting multimodal image data of healthy children, dividing the functions of children's brain regions, and constructing an atlas 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] Use ITK-SNAP to edit and ensure that the skull is removed accurately; for example, use ITK-SNAP to manually edit and 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. Use 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 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 through brain segmentation;

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

[0090] Colin27's brain image was registered to the brain image of each 12-year-old child in the group;

[0091] The registered zonation maps were propagated temporally to their corresponding earlier images. The registration method employed a hierarchical nonlinear deformable registration algorithm, utilizing the resulting deformation field to register the AAL atlas to each 12-year-old image and then propagating it temporally to its corresponding earlier image. Finally, a set of images was obtained for each subject at each age group, 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 aligning the subjects of each age group to their respective age-specific common spaces using a feature-based group registration algorithm includes:

[0094] Group registration using affine transformation;

[0095] The feature-based group registration algorithm further aligns and calculates the image features of each voxel, including image intensity, edge type, and geometric moment invariants of 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 the characteristic data of the lesions and determine the seed points;

[0099] In some embodiments, as 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 original DICOM format DTI data into NII format;

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

[0103] S34. Automatically track white matter fiber tracts for each voxel within the gray matter volume of the individual subject. Fiber tract tracking automatically stops when the fractional anisotropy value or minimum orientation angle reaches a corresponding threshold. For example, use Automated Fiber Quantification (AFQ; https: / / github.com / jyeatman / AFQ) software to automatically track white matter fiber tracts for each voxel within the gray matter volume of the individual subject. Fiber tract tracking automatically stops when the fractional anisotropy (FA) value is <0.2 or the minimum orientation angle is >30°.

[0104] S35. In the MNI standard space, define the fiber starting and ending ROIs for the average DTI images 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, including FA and mean diffusivity (MD), at multiple equidistant points (e.g., 100 equidistant points) of each fiber bundle, 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 map;

[0108] In some embodiments, as Figure 5 As shown, the steps of extracting the BOLD time series and generating the lesion network map 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 used 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 connectivity, a set of voxels close to the scalp surface is found as the TMS target.

[0112] On the other hand, this embodiment also provides a SCP children's personalized TMS target map 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 precise positioning of TMS treatment targets for different types of SCP children. Specifically, the functions of children's brain regions are accurately divided based on homemade T1 and DTI longitudinal brain templates of children in the normal control group, and 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 BOLD time series and whole-brain functional connectivity. Based on the mapping relationship map, the spatial distribution characteristics of brain functional connectivity 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 damage area, but maps lesions in different brain regions that cause the same symptoms to a common brain network, which is crucial for revealing neural network mechanisms and identifying neural regulatory 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 the 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 and 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 can 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] A personalized TMS target positioning 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 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 age-matched healthy children;

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

[0120] The lesion quantification analysis module is used to extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantify the lesion feature data and determine the seed point;

[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 an atlas 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 accurately removed;

[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 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 images of children of each preset age in the group;

[0139] The registered partition maps were propagated along the time axis to their corresponding earlier images, and a set of images for each subject in each age group was obtained, 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 and propagated 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 age-specific common space includes:

[0143] Group registration using affine transformation;

[0144] The feature-based group registration algorithm further aligns and calculates the image features of each voxel, including image intensity, edge type, and geometric moment invariants of 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 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:

[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] Automatic white matter fiber tract tracking is performed for each voxel within the gray matter volume of an individual subject. When the anisotropy score or the minimum direction angle reaches the corresponding threshold, the fiber tract tracking is automatically stopped.

[0152] In the MNI standard space, the fiber starting and ending 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 along each fiber bundle to generate a white matter fiber tractogram.

[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 the lesion network map includes:

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

[0157] The overlapping parts of the functional network connections with each clinical classification symptom of spastic cerebral palsy were used 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 precise positioning of TMS treatment targets for different types of SCP children. Specifically, the functions of children's brain regions are accurately divided based on homemade T1 and DTI longitudinal brain templates of children in the normal control group, and 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 BOLD time series and whole-brain functional connectivity. Based on the mapping relationship map, the spatial distribution characteristics of brain functional connectivity 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 damage area, but maps lesions in different brain regions that cause the same symptoms to a common brain network, which is crucial for revealing neural network mechanisms and identifying neural regulatory targets.

[0159] Example 3

[0160] A computer device 700, such as Figure 7 As shown, the present invention 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 personalized TMS target localization for children with spastic cerebral palsy are implemented. For a detailed description of the method, please refer to the corresponding description in the above method embodiment and 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 a 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 can be made to the corresponding description in the above method embodiment, and no further details will be given here.

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

[0164] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. 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 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. 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 will also appreciate that, in addition to implementing the controller in pure computer-readable program code, it is entirely possible to implement the same functionality by programming the method steps logically, such as through logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered structures within the hardware component. Alternatively, the devices for implementing various functions can be considered both software units 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 physical devices, or by products with certain functions. For ease of description, the above devices are described separately by function, with each unit described separately. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware components.

[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 take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing 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 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 that can direct a computer or other programmable data processing device to work 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 The 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 operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0172] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0173] This 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, and the like 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] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0175] The foregoing is merely an example of the present invention and is not intended to limit the present invention to one or more embodiments. It will be apparent to those skilled in the art that various modifications and variations may be made to the present invention to one or more embodiments. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention to one or more embodiments shall be included within the scope of the claims of the present invention to one or more embodiments.

Claims

1. A method for individualized TMS target localization for children with spastic cerebral palsy, characterized by: The following steps are involved: TI, DTI, and fMRI images were obtained from children with spastic cerebral palsy 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 the characteristic data of lesions and determine the 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 connectivity, a set of voxels close to the scalp surface was found as the TMS target; 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: 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 NI I format; Use the bet command to remove individual non-brain tissues and perform image optimization; Automatic white matter fiber tract tracking is performed for each voxel within the gray matter volume of an individual subject. When the anisotropy score or the minimum direction angle reaches the corresponding threshold, the fiber tract tracking is automatically stopped. In the MNI standard space, the fiber starting and ending 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 along each fiber bundle to generate a white matter fiber tractogram. Quantitative analysis of lesion space and white matter fiber bundle characteristics provides a basis for seed point selection.

2. The method for individualized TMS target positioning for children with spastic cerebral palsy according to 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 an atlas 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 according to claim 2, characterized in that: The steps of preprocessing all T1 images include: Non-brain tissue was dissected using BET; Use ITK-SNAP editing to ensure that the skull is accurately removed; Nonparametric nonuniform intensity normalization bias correction was applied to all images; Resample the T2 image to 1 × 1 × 1 mm 3 resolution.

4. The method for individualized TMS target positioning for children with spastic cerebral palsy according to claim 2, characterized in that: The steps of performing brain tissue segmentation on the gray matter, white matter and cerebrospinal fluid of all images using the joint registration-segmentation method include: 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 according to claim 2, characterized in that: The step of dividing the brain into multiple regions of interest by brain region segmentation includes: Register the Colin27 brain image to the brain images of children of each preset age in the group; The registered partition maps were propagated along the time axis to their corresponding earlier images, and a set of images for each subject in each age group was obtained, including intensity images, TPMs, and anatomical partition maps.

6. The method for individualized TMS target positioning for children with spastic cerebral palsy according to 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, and then propagated to its corresponding earlier image along the time axis.

7. The method for individualized TMS target positioning for children with spastic cerebral palsy according to claim 2, characterized in that: The steps of aligning the subjects of each age group to their respective age-specific common space using a feature-based group registration algorithm include: Group registration using affine transformation; The feature-based group registration algorithm further aligns and calculates the image features of each voxel, including image intensity, edge type, and geometric moment invariants of 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 according to 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 connectivity strength of each node based on the seed point, and construct a functional connectivity matrix; The overlapping parts of the functional network connections with each clinical classification symptom of spastic cerebral palsy were used as the relevant lesion network to generate a lesion network map.

9. A personalized TMS target positioning system for children with spastic cerebral palsy, implementing the method according to any one of claims 1 to 8, characterized in that: It includes image acquisition module, brain area 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 age-matched healthy children; The brain region function division and atlas construction module is used to extract multimodal image data of healthy children, divide the children's brain region functions, and construct an atlas; The lesion quantification analysis module is used to extract 3DT1 and DTI image data of children with spastic cerebral palsy, quantify the lesion feature data and determine the seed point; 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 connectivity.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. 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 8 are implemented.

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