85 results about "Brain imag" patented technology

The invention relates to the technical field of brain age prediction and image data processing, in particular to a brain age prediction method, system and device based on multi-modal information fusion and a medium. Multi-scale enhancement processing is carried out on a preprocessed image, multi-scale features and space attention information are combined, a brain image feature map is obtained, and a brain age prediction result is obtained; different feature extraction modes are respectively used for the brain region node feature matrix and the adjacent feature matrix, a plurality of different image convolutions are used for carrying out parallel convolution processing to extract brain region node features, an edge convolution processing and row convolution column convolution processing combined mode is used for extracting adjacent features, a brain image omics feature map is obtained after splicing, and the brain region node features are extracted. And further performing feature splicing and cross-modal feature fusion processing on the brain image feature map and the brain radiomics feature map to realize accurate prediction of the brain age.

The invention relates to the technical field of image processing, in particular to a craniocerebral disease area identification and detection method and system based on an MRI (Magnetic Resonance Imaging) image, and the method comprises the steps: obtaining a plurality of sub-images with different scales according to a gray level image of the craniocerebral MRI image; performing multi-scale analysis on the gradient value of any pixel point according to each sub-image to obtain a multi-scale gradient coefficient of any pixel point; obtaining a multi-scale local anomaly degree according to the gray values of the pixel points in different local ranges of any pixel point and the distribution in the gradient direction; optimizing the gradient value of any pixel point according to the multi-scale gradient coefficient and the multi-scale local anomaly degree to obtain a self-adaptive gradient value, and performing image enhancement on the grayscale image by using an anisotropic diffusion filtering algorithm according to the self-adaptive gradient value of each pixel point so as to identify a craniocerebral disease region. And the effect of performing image enhancement on the MRI image by using the anisotropic diffusion filtering algorithm is improved.

A method for recognizing a motor imagery electroencephalography (MI-EEG) signal based on a capsule network (CapsNet) is provided, and relates to the technical field of deep learning and brain-computer interfaces (BCIs). An electroencephalography (EEG) time series is mapped into a three-dimensional (3D) array form based on a spatial electrode distribution. By combining 3D convolution, a CapsNet constructs a three-dimensional capsule network (3D-CapsNet) for recognizing an MI-EEG signal. A 3D convolution module performs feature extraction from both a temporal dimension and an inter-channel spatial dimension through a plurality of layers of 3D convolution to obtain a low-level feature. The low-level feature output by the 3D convolution module is integrated through the CapsNet to obtain a high-level spatial vector containing an inter-feature relationship. A primary capsule and a motor capsule are connected through dynamic routing, and finally a CapsNet module outputs a classification result through a nonlinear activation function squash.

The invention discloses an SIND network construction method and system based on KD-Tree multi-source feature fusion, and the method comprises the steps: obtaining individual QSM data, mapping the individual QSM data to a preset standardized template space to obtain a standardized brain image, and segmenting the brain image into different brain regions; encoding the QSM data of each brain region into multi-dimensional vector data, and constructing a KD tree of each brain region based on a variance-sensitive dynamic division strategy and median segmentation; calculating KL divergence of any two brain regions based on a KD tree, and determining an SIND value between the two brain regions according to the KL divergence; and carrying out dimension reduction analysis on the SIND value by adopting diffusion mapping, and determining the magnetic sensitive gradient characteristics of the brain region. According to the method, efficient data processing, multi-source feature fusion, KL divergence measurement and diffusion mapping analysis of the KD Tree are combined, the magnetic sensitive gradient features of the brain regions can be accurately analyzed, the structure and function relation between the brain regions is disclosed, and a powerful tool is provided for brain science research.

Systems and methods for reliably returning a neuromodulation system to a predetermined position relative to a user's head is disclosed. The system includes a neuromodulation device and a stimulation control computing environment. The stimulation control computing environment can be configured with data processing functions to focus ultrasound emission to a target brain region. The system identifies an initial position of one or more ultrasound-emitting elements with respect to the head of a user, uses brain images to identify the target brain region, and performs acoustic simulations to focus ultrasound emissions from the initial position to the target brain region. The distance between an inner surface of the device and a user's head can be measured, and one or more spacing elements can be placed on the inner surfaces based on the measured distances, which enables the reliable return of the neuromodulation device to the initial position with minimal displacement.

The embodiment of the invention discloses a brain image registration method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring an individual brain image, a template brain image and a pre-trained registration model; inputting the individual brain image and the template brain image into a registration model, and registering the template brain image to the individual brain image through the registration model based on the following steps: carrying out affine registration associated with the individual brain image and the template brain image by using an affine registration network to obtain an affine transformation matrix, and carrying out a first spatial transformation layer to obtain a second spatial transformation layer; based on the affine transformation matrix, performing spatial transformation of the template brain image to obtain an intermediate brain image; and performing deformation registration on the intermediate brain image and the individual brain image by using a deformation registration network to obtain a deformation field, and performing spatial transformation of the intermediate brain image based on the deformation field by using a second spatial transformation layer to obtain a target brain image. The problem that the brain image registration process is tedious is solved.

The invention provides an automatic segmentation method for a focus of brain injury of a premature infant. The method comprises the following steps: preprocessing a data set; the data set is a brain MRI image of the premature brain injury patient; dividing the preprocessed data set into a training set, a test set and a verification set according to a preset proportion; building an automatic lesion segmentation network model of the brain injury of the premature infant; the network model comprises a network architecture composed of a PAFM module group, a CMSC module group and a 3D-UXNET; inputting the training set into the network model for training, and adjusting hyper-parameters based on the verification set; and inputting the test set into the trained network model to obtain an automatic lesion segmentation result of the brain injury of the premature infant corresponding to the test set. According to the method, features are extracted through multi-scale convolution of the CMSC module, and the capability of segmenting small focuses is enhanced. According to the method, multiple modal data are utilized at the same time, and modal information is fused in parallel through a hierarchical fusion strategy and a PAFM module formed by inter-modal attention and cross-modal attention, so that the segmentation performance of the model is effectively improved.

The invention relates to the technical field of biomedical engineering, in particular to an atlas making and registering method and system for a mouse brain in a special development period, and the method comprises the following steps: carrying out the data preprocessing of a mouse brain image, and obtaining a reconstructed image; averaging the plurality of reconstructed mouse brains by adopting an iterative registration mode, making an average template, then labeling the average template to make a brain region template, and then generating a structure information file of a mouse brain map; and performing registration on the individual mouse brain image and the mouse brain map to realize accurate alignment of the brain region. According to the method provided by the invention, the appropriate atlas can be specially drawn for the mouse brain in the special development period and registration is carried out under the condition that no existing atlas exists, the characteristics of the VISoR imaging technology are fully considered, good adaptation with the imaging technology is realized, the accuracy of image registration of the mouse brain in the special development period is improved, and the accuracy of image registration of the mouse brain in the special development period is improved. A more accurate tool is provided for neuroscience research, and deep research on the brain structure and function of the mouse in the special development period is promoted.

A method of predicting an effect of a chemotherapy treatment on a cancer patient's cognitive status using the patient's predicted age difference (PAD) comprises acquiring at least one medical brain image of a patient's brain before a chemotherapy treatment; processing the medical brain image to obtain at least one feature of the image; generating a PAD value of the individual based on the at least one feature of the image; and predicting an effect of the chemotherapy treatment on a cancer patient's cognitive status using the PAD value.

The invention discloses a PET-based epilepsy postoperative prognosis information generation system and method, and the system comprises a preprocessing module, a target training data generation module, a training module and an application module which are sequentially connected. Through processing the whole brain health PET image, the preoperative whole brain PET image and the postoperative whole brain magnetic resonance imaging, the obtained target training data focuses on the metabolic connection characteristics of the focus area and the whole brain, a new view angle is provided for the postoperative prognosis prediction, so that the target training data is adopted to train the deep learning model, and the prognosis prediction accuracy is improved. A deep learning model focusing on the metabolic connection characteristics of the focus area and the whole brain can be obtained, so that the postoperative whole brain magnetic resonance imaging is accurately processed, and prognosis information is obtained.

Methods for treating a neuropsychiatric disorder by administering an iboga alkaloid and a cardioprotective agent in conjunction with analysis of brain image data is described. Also described are methods to improve brain health and to slow or reverse brain aging by disorder by administering an iboga alkaloid and a cardioprotective agent, where analysis of brain image data is used to monitor and / or evaluate treatment effectiveness.

The invention discloses a light field reconstruction brain image brain region boundary extraction method based on convex hull fitting. The method comprises the steps that the p percentile of a brain image is calculated, and image cutting, contrast stretching and Gaussian filtering smooth denoising are carried out; carrying out Canny edge detection on the filtered image to obtain a binary edge image; boundary enhancement and connection are carried out through expansion and closed operation, all connected regions are extracted, and the contour with the maximum area is selected as the contour of the brain region boundary; a datum point is determined based on the contour of the brain region boundary, the polar angle and distance of each point except the datum point are calculated and sorted, and convex hull fitting is carried out through Graham Scan scanning based on the datum point and the sorted point set; converting the convex hull into a closed boundary region, and obtaining a mask image of the brain image after light field reconstruction through a mask function; according to the method, the special boundary form of the light field reconstruction image can be accurately adapted, deep learning and data training are not needed, and the generated mask has high geometric consistency.

The present invention relates to a method for segmenting a brain image by using virtual data and, more specifically, to a method for constructing a general-purpose modality-agnostic artificial intelligence model by using virtual data and a method for segmenting a brain image by using the constructed artificial intelligence model. To this end, provided is a method for constructing a general-purpose modality-agnostic artificial intelligence model by using virtual data comprising: a step (S100) for inputting a label map (100) of a medical image including one or more regions related to a disease of a human body; a step (S120) in which an image generation model (120) generates a plurality of deformed images by deforming the label map (100); a step (S140) for training the general-purpose modality-agnostic artificial intelligence model (200) on the basis of at least one of the medical image, the label map (100), or the plurality of deformed images; and a step (S160) for determining that the general-purpose modality-agnostic artificial intelligence model (200) has been constructed when an error between an expected label map (220) output by the general-purpose modality-agnostic artificial intelligence model (200) and the label map (100) is within a prescribed range.

The application discloses a brain injury identification method and an identification system. The identification system identifies the injury of a patient according to the identification method. The identification method comprises the following steps: firstly, the electrical impedance tomography (EIT) of the brain of the patient is performed to obtain the EIT image of the brain of the patient; then, the region with abnormal resistivity distribution in the EIT image of the brain is taken as a reference for scanning of a near-infrared spectrometer, and a scanning path of the near-infrared spectrometer is planned; finally, the near-infrared spectrometer is scanned along the scanning path, and the lesion range and the lesion type are determined according to the obtained near-infrared spectral data. The electrodes need not be arranged on the whole head of the patient, and the automatic and rapid identification of the lesion range and the nature of the brain injury is realized.

The application relates to the technical field of brain image analysis, and discloses a fine-grained brain age prediction method, a system, a terminal and a storage medium.The method comprises the following steps: a plurality of brain tissue structures are segmented from a brain structure nuclear magnetic resonance image of a target object after preprocessing; a plurality of feature maps are generated according to the brain tissue structures and are mapped to a plurality of positive faces; a four-stage network model is used to process the mapped samples; and dynamic adaptive lateral attention is added after each stage to predict the brain age of each brain region.The application predicts the brain age by using the fine-grained brain cortex region level, and introduces a dynamic adaptive lateral attention mechanism to simulate the real brain structure lateral relationship, so that the topological structure and local features of the cortex can be effectively expressed, and the error caused by the brain morphology difference is reduced.

The invention discloses a perfusion curve estimation method based on contrast enhanced imaging, which comprises the following steps: S1, according to a series of T2 * weighted brain images, calculating the time when a contrast agent passes through blood capillaries and a pixel value corresponding to each time point in a region of interest (ROI) to obtain a signal-time curve with signal decline; s2, the signal-time curve is converted into a contrast agent concentration-time curve, and a gamma function is adopted as an ideal model concentration-time curve for modeling; s3, defining an area enclosed by a concentration-time curve under the region of interest according to a central volume principle and a normalized first moment to describe physiological parameters to be calculated; and S4, under the condition that the model is definite and parameters are unknown, selecting a maximum likelihood estimation method to estimate the established model, and forming a concentration-time curve simulation diagram. The method still shows satisfactory performance fitting ability under the condition of small samples.

A time sequence brain image calibration method based on multi-task adversarial learning belongs to the technical field of medical images, and comprises the following steps: pre-processing a pre-acquired time sequence brain image to remove irrelevant information; a brain image phenotype calibration network is divided into two sub-tasks (local detail reconstruction and global structure transformation), learning is carried out in different modes, and a discriminator in an adversarial learning strategy is adopted to restrain and generate a better calibration brain image. The method is very valuable for improving the brain image and gene association performance, and the state of the human brain can be effectively observed and understood.

The application relates to the technical field of image processing, in particular to a brain image classification method, a brain image classification device, equipment and a storage medium. A self-attention network is applied to a plurality of features of a detected brain image to calculate the correlation degree between any two features; the plurality of features are corrected according to the correlation degree to obtain a plurality of features after correction; a graph convolution network is applied to the plurality of features after correction to obtain a classification result of the detected brain image output by the graph convolution network. The application inputs the plurality of features into the self-attention network, the self-attention network outputs the correlation degree between the plurality of features, then the plurality of features are corrected according to the correlation degree between the plurality of features, finally the plurality of features after correction are input into the graph convolution network, and the graph convolution network outputs the classification result. The application fully considers the correlation degree between the features, so that the classification result of the application for the brain image is relatively accurate.

The application provides a child neuropsychological development monitoring and management system. The system uses brain physiological parameters, brain MRI images, development scale characteristics and corresponding child neuropsychological development suffering grades to construct a child neuropsychological development evaluation model. The constructed child neuropsychological development evaluation model generates a current child neuropsychological development suffering grade of a user according to real-time brain physiological parameters collected by a system terminal, real-time brain MRI images monitored by a hospital and corresponding real-time development scale characteristics, provides a basis for whether the corresponding child needs further monitoring according to the current child neuropsychological development suffering grade, and improves the probability of finding potential abnormal development children. The application uses three kinds of fusion parameters related to child brain nerves, such as brain physiological parameters, brain image characteristics and subjective measurement table characteristics, to construct a high-precision real-time processing model, realizes intelligent monitoring of the morbidity of child neuropsychological development disorders by doctors, and reduces the burden on families.

An apparatus for diagnosing an autism spectrum disorder (ASD) based on a graph neural network includes a preprocessor configured to acquire brain image data, designate a region of interest (ROI) of the brain, and generate preprocessed data for neural network input, a spatial feature extractor configured to extract spatial features from the preprocessed data, a temporal feature extractor configured to analyze changes in brain activity over time and extract attention-based temporal features, a spatial and temporal convergence feature unit configured to analyze spatiotemporal correlations by combining the spatial features and the temporal features, a graph generator configured to convert connectivity between regions of interest into a graph structure and implement the same as nodes and edges, and a graph classifier configured to analyze a spatiotemporal pattern of the connectivity through the graph structure and classify whether or not there is an ASD.

We describe a computer-implemented method for determining a patient's brain age and optionally stratifying patients into dementia risk groups based on the determined brain age. The methods comprise extracting at least one volumetric feature from an image of a brain by: obtaining at least one volume value for at least part of the patient's brain, and normalising the at least one obtained volume value to obtain the at least one volumetric feature. Brain age is predicted by inputting the at least one extracted volumetric feature into a pre-trained brain age model, wherein the brain age model is a linear regression model. Bias of the linear regression model may also be corrected. A classification model, such as a logistic regression binary classifier, may be used to stratify patients into dementia risk groups.

The application provides a method for imaging-based diagnosis of neuropsychiatric lupus based on machine learning. The method comprises: obtaining a brain MRI image of a patient; segmenting the intracerebral region of interest of the preprocessed brain MRI image; extracting radiomics features in the region of interest; screening features related to the diagnosis and prediction of neuropsychiatric lupus; dividing the screened features into a first training set and a first test set; building N machine learning-based radiomics prediction models; saving the trained N machine learning-based radiomics prediction models; using multiple linear regression to save the radiomics prediction model corresponding to the optimal AUC brain region; obtaining the serological indicators of the NPSLE patient; dividing the serological indicators into a second training set and a second test set; building a joint prediction model combining radiomics features and serological indicators; and inputting the second test set into the joint prediction model to obtain the prediction classification result of the patient's neuropsychiatric lupus. The application fills the gap of magnetic resonance imaging in the diagnosis of NPSLE, and provides practical guidance for clinicians in assisting the diagnosis of NPSLE.

A method for deriving dementia-related information using volume predicted from brain CT includes: a step of an analysis apparatus receiving a brain CT (Computed Tomography) image of a subject; a step of the analysis apparatus inputting the brain CT image into a pre-trained segmentation model to extract regions of interest; a step of the analysis apparatus inputting pixel information of the regions of interest into a pre-trained first learning model to predict the volume of at least one region among the regions of interest; and a step of the analysis apparatus inputting the volume of the at least one region into a pre-trained second learning model to derive dementia-related information of the subject.

The invention relates to the field of brain image processing, in particular to an image analysis processing system and method based on thought control, and the system comprises an electroencephalogram signal collection system which is used for collecting and preprocessing electroencephalogram signals of a user; the thought analysis system is used for extracting features from the preprocessed electroencephalogram signals and then carrying out thought classification and instruction mapping; the image acquisition and processing system is used for acquiring image information according to the mind control instruction of the user and performing image processing; the VR display system is used for converting the processed image into a format suitable for VR equipment display and providing interactive feedback; and the control and communication system is used for converting the mind state into a control instruction and realizing data transmission and communication among the systems. The EEG signals and the fNIRS signals are acquired through the multi-mode signals, the defect of a single signal in the aspect of reflecting the brain activity is overcome, and therefore brain activity information corresponding to the thought is more accurately interpreted.

The embodiment of the invention provides a craniocerebral image cutting method, a craniocerebral image segmentation model training method and related equipment. The craniocerebral image cutting method comprises the following steps: acquiring a to-be-processed craniocerebral image; a to-be-processed craniocerebral image is input into the craniocerebral image segmentation model, a segmentation result is obtained, and the segmentation result comprises a craniocerebral mask; cutting the craniocerebral image based on the segmentation result to obtain a cutting result; wherein the craniocerebral image segmentation model comprises an encoder and a decoder; inputting the to-be-processed craniocerebral image into the craniocerebral image segmentation model to obtain a segmentation result, specifically comprising: inputting the to-be-processed craniocerebral image into an encoder to carry out predetermined times of downsampling and feature extraction to obtain a target feature image; and inputting the target feature image into a decoder, performing up-sampling for a predetermined number of times, and then performing feature fusion to obtain a segmentation result. According to the technical scheme provided by the embodiment of the invention, the cutting duration can be shortened from several minutes to a second level, and the segmentation accuracy of data in different scanning ranges is ensured.

The present invention provides a method for automatic segmentation of lesions of premature brain damage. The method comprises: preprocessing a data set; the data set is a brain MRI image of a patient with premature brain damage; dividing the preprocessed data set into a training set, a test set and a validation set according to a preset ratio; building a network model for automatic segmentation of lesions of premature brain damage; the network model comprises: a network architecture composed of a PAFM module group, a CMSC module group and a 3D-UXNET; inputting the training set into the network model for training, and adjusting the hyperparameters based on the validation set; inputting the test set into the trained network model to obtain the automatic segmentation result of lesions of premature brain damage corresponding to the test set. The present invention enhances the segmentation capability of small lesions by extracting features through the multi-scale convolution of the CMSC module. The method simultaneously utilizes multiple modal data, and fuses modal information in parallel through a PAFM module composed of a hierarchical fusion strategy and inter-modal attention and cross-modal attention, thereby effectively improving the segmentation performance of the model.

The invention relates to a method for constructing a growth model of a brain in a life cycle, and the method comprises the steps: obtaining brain MRI images of individuals of different age groups, and dividing the cerebral cortex into n regions; constructing a morphological feature network for sMRI of the brain MRI image of the individual, and performing morphological feature similarity degree analysis to obtain a first phenotype; constructing a functional network for the fMRI of the brain MRI image of the individual; constructing a structure-function coupling matrix for the morphological feature network and the function network, and calculating to obtain a second phenotype; selecting the first phenotype and the second phenotype of each individual as dependent variables and the age of each individual as a smooth item to carry out GAMLSS modeling, and selecting JSU distribution to respectively fit the change of the first phenotype and the second phenotype along with the age to generate a reference growth curve. The method can be used for studying the growth mode of the brain network in the whole life cycle, studying life cycle association between morphological characteristics and functional characteristics of different areas and the like.

The invention relates to a new biomarker and combination of biomarkers and to their use in screening of traumatic brain injury in paediatric patients and selecting those patients in need of a brain image technique and / or a subsequent treatment intervention. The method allows to rule out the performance of a brain imaging technique if it is determined that the patient does not have any intracranial lesion which would be detectable by means of said brain imaging technique. Particular kits and means for carrying out the methods are also disclosed.

The invention discloses a child brain metabolism template establishing method, a processing device, a system, equipment and a medium. The method comprises the following steps: dividing a PET image of an original child brain by body weight and injection dosage to obtain a standardized uptake value image; performing spatial standardization processing on the standardized shooting value image; based on the age interval of the brain of the child, extracting a region of interest from the standardized target image, and calculating a standardized average shooting value of the region of interest; and establishing a brain metabolism template containing each brain region of each age group. When the children brain metabolism template is established, brain metabolism templates of different ages are established according to age groups, and the standardized uptake mean value of each brain region is extracted. The finally obtained children brain metabolism template can be more accurately used for analyzing children brain PET images of corresponding age groups, change trends of different age groups and brain regions are obtained, and a metabolism rule of children brain development is obtained.

A method and a system are provided for determining a location of a brain target for neuromodulation therapy without requiring MRI imaging of the head. The system includes a sensor or a plurality of sensors configured to acquire data describing a geometry of a head of a subject. A processor is coupled to the sensor and is configured to receive the data describing a geometry of the head of the subject gathered by the sensor, determine 3D coordinates of head-surface contour points from the acquired data, apply a predictive model to the determined 3D coordinates of head-surface contour points, receive 3D coordinates of a located brain target as output from the model, and output 3D coordinates of the located brain target for subsequent delivery of neuromodulatory brain therapy. The model is pre-trained on a dataset of head and brain images and configured to associate head-surface coordinates with 3D coordinates of at least one deep brain target.