23 results about "Diffusivity tensor" patented technology

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

The invention discloses a brain data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-modal image data of the brain of a target object, the multi-modal image data at least comprising resting state functional magnetic resonance imaging data and diffusion tensor imaging data at a plurality of collection moments; for each brain region of the brain, a brain region dynamic model of the brain region is constructed according to the diffusion tensor imaging data and the resting state functional magnetic resonance imaging data at the multiple acquisition moments, and the brain region dynamic model comprises disturbance parameters; by adjusting disturbance parameters of a brain region kinetic model of the brain region, simulation time sequences of the brain region under the multiple disturbance parameters are obtained, critical indexes of the brain region are determined according to the multiple simulation time sequences, and a critical toughness coefficient of the brain region is determined according to the critical indexes under the multiple disturbance parameters; constructing a critical toughness map of the brain according to the critical toughness coefficients of the plurality of brain regions, and displaying the critical toughness map; therefore, the brain health state is quantitatively evaluated.

The present invention discloses a diffusion magnetic resonance (MR) method for measuring arterial pulsation dependence of perivascular cerebrospinal fluid flow in glymphatic system: DTI acquisition: Brain MRI images were acquired using dynamic diffusion tensor imaging (DTI); Cardiac signal synchronization: Simultaneously collect heart rate fluctuation time-series signals; Peak coordinate determination: Identify the peak timing of cardiac pulsation signals from the time-series data; Image realignment: Reorganize brain MR images according to their temporal positions within the cardiac cycle; Temporal interpolation: Perform uniform time-sampling reconstruction to generate equidistant diffusion MRI datasets across the cardiac cycle; Parameter calculation: Compute axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) at each voxel level; Mask-based analysis: Generate characteristic curves of AD / RD / MD and spin density(S) dynamics using region-specific masks in individual space. This method enables non-invasive measurement of: (1) Cerebrospinal fluid (CSF) flow velocity / direction in large perivascular spaces during heartbeats; (2) Microvascular perivascular CSF dynamics through diffusion parameter analysis.

This invention provides a method and apparatus for mapping tissue microstructures based on diffusion tensor distribution using deep learning. The method includes the following steps: preprocessing diffusion magnetic resonance imaging (DMRI) data; determining target voxels in the DMRI data using an FSL tool; dividing the DMRI data into target data blocks based on the target voxels; unfolding the three-dimensional target data blocks into a two-dimensional matrix; processing the two-dimensional matrix into embedded feature vectors; inputting the embedded feature vectors into a Transformer encoder to obtain encoded feature vectors; inputting the encoded feature vectors into a decoder, the decoder including a sparse dictionary mapping branch and a prediction branch, the sparse dictionary mapping branch outputting brain component distribution vectors, and the prediction branch outputting regression calculation parameters; calculating multiple brain component mapping pixels at the target voxel locations based on the brain component distribution vectors and regression calculation parameters, and constructing microstructure parameter maps corresponding to various brain components.

This method uses magnetic resonance (MR) data to estimate a diffusion tensor distribution (DTD). The method includes inverting a Fredholm integral of the first kind, specifically performing an nD Inverse Laplace Transform subject to several constraints. First, a positive definiteness constraint is used, which zeros out a subset of diffusion tensor components that do not lie on a manifold of symmetric positive definite matrices. Second, marginal distribution constraints are used, which further partition the manifold of symmetric positive definite matrices into even smaller domains to improve DTD estimates in each MR voxel.

Calculating a brain age predicts progression from cognitively normal status to cognitive impairment. Software acquires diffusion magnetic resonance images (dMRI) and T1 weighted magnetic resonance images (T1w MRI) of the brain and calculate a pair of diffusion tensor imaging (DTI) scalar maps. The scalar maps may be a fractional anisotropy (FA) scalar map and / or a mean diffusivity (MD) scalar map. Concatenating the FA scalar map and the MD scalar map form a frame of scalar map data. The method includes a rigid transformation from scalar map data to the T1w images to store a rigid transformed image; applying an affine transformation from the rigid transformed image to a brain template; storing a rigid plus affine transformed image; and applying a non-rigid transformation to warp the rigid plus affine transformed image to the selected brain template to form a non-rigid white matter age input to a brain age calculation model.

The application belongs to the technical field of image recognition, and discloses a kind of individualized brain function area boundary positioning method and system;Obtain structural MRI data, functional MRI data and diffusion tensor imaging data under the same voxel space, generate functional activation map under resting state condition, and determine coarse functional area mask in target anatomical region according to functional activation map;Coarse functional area mask is expanded in space and edge band is extracted, so as to demarcate a candidate boundary band voxel set around coarse functional area mask;The boundary feature vector corresponding to each voxel is constructed;Boundary feature vector is input into pre-trained boundary discriminant network, and the boundary probability value of each voxel is obtained, and the boundary probability graph of individualized brain function area is generated;Based on boundary probability graph, construct boundary energy function, combine energy minimization segmentation mechanism, divide candidate boundary band voxel set, three-dimensional reconstruction is carried out in individual structure space, and the boundary surface of individualized brain function area is obtained.

This invention discloses a magnetic resonance imaging (MRI) method for evaluating the therapeutic effect of cerebral palsy rehabilitation in children, relating to the field of medical image processing technology. The method includes: acquiring structural imaging data (T1-weighted imaging and diffusion tensor imaging) and resting-state imaging data for each child patient; extracting brain and tissue boundaries from T1-weighted imaging using a 3D-Unet segmentation model; preprocessing the resting-state and structural imaging data and aligning them to brain boundaries, then registering these data to a standard brain template for children; acquiring and normalizing multiple clinical indicators based on the standard brain template, splicing them into multi-index vector data, inputting them into a pre-trained support vector machine model, and outputting differences in clinical indicators; determining abnormal brain regions for each child based on the differences, and finally generating a multimodal fusion index analysis report to observe changes in brain function and white matter integrity before and after rehabilitation.

The invention relates to the technical field of biomedical signal processing and neural image analysis, in particular to an epileptic seizure prediction method combining electroencephalogram and image data, which comprises the following steps: acquiring diffusion tensor imaging data of a target object, calculating the diffusion tensor of whole brain voxels, and calculating the image data of the target object; an anisotropic transmission resistance tensor field representing the conduction performance of the white matter fiber bundle of the brain is constructed based on inverse transformation of the diffusion tensor; and based on the anisotropic transmission resistance tensor field, calculating an anisotropic geodesic distance between any two points in the cerebral cortex space, and constructing a Riemannian manifold geometric substrate containing anatomical conduction constraint. According to the method, by constructing the anisotropic transmission resistance field and the Riemannian manifold substrate, the minimum biophysical cost constrained by anatomy is calculated, so that the problems that correlation analysis lacking physical constraint is mostly adopted in the traditional technology, and due to the neglect of the white matter fiber conduction rule, the analysis accuracy is low are solved; therefore, the false alarm problem caused by false synchronization caused by the fact that the volume conductor effect cannot be eliminated is solved.

The invention belongs to the technical field of medical image processing, and relates to an MRI (Magnetic Resonance Imaging) medical image data processing method, which comprises the following steps of: 1, respectively scanning a scanned object through a plurality of medical image sequences to obtain a plurality of medical image data; 2, correcting the original amplitude image and the original phase image to obtain a corrected amplitude image and a corrected phase image; 3, removing a background phase in the corrected phase image by adopting variable high-pass filtering to obtain a local phase image; the local phase image refers to an image related to pathology; 4, performing quantitative magnetization intensity mapping on the local phase image to obtain an initial magnetic susceptibility diagram; 5, performing iterative optimization on the initial magnetic susceptibility diagram to generate an optimized magnetic susceptibility diagram; step 6, fusing the optimized magnetic susceptibility map, the diffusion tensor data and the cerebral blood flow to obtain a fused damage map; and the definition of brain imaging is improved, so that a basis is provided for diagnosis and evaluation of diseases such as traumatic brain injury and the like.

This method uses magnetic resonance (MR) data to estimate a diffusion tensor distribution (DTD). The method includes inverting a Fredholm integral of the first kind, specifically performing an nD Inverse Laplace Transform subject to several constraints. First, a positive definiteness constraint is used, which zeros out a subset of diffusion tensor components that do not lie on a manifold of symmetric positive definite matrices. Second, marginal distribution constraints are used, which further partition the manifold of symmetric positive definite matrices into even smaller domains to improve DTD estimates in each MR voxel.

The invention discloses a structure tensor anisotropic diffusion interference suppression method guided by a time-frequency energy threshold, and relates to the technical field of radars, and the method comprises the steps: determining a mixed signal; performing short-time Fourier transform on the mixed signal to obtain a time-frequency diagram; calculating an energy boundary threshold value based on the relationship between the interference-to-signal ratio and the energy of the time-frequency diagram; a core layer is determined from the time-frequency graph based on the energy boundary threshold value, a structure tensor is determined by performing Gaussian smoothing operation on the core layer, and the core layer is an interference core area; using the empirical coefficient and the structure tensor to construct a diffusion tensor used for controlling the diffusion direction and intensity; and carrying out anisotropic diffusion iterative filtering on the time-frequency diagram by using the diffusion tensor to obtain an anti-interference mixed signal, thereby providing an interference suppression method which can realize self-adaptive suppression and maximally maintain target information under the condition that an interference model, threshold experience or a training sample does not need to be preset.

A method of controlling a magnetic resonance (MR) imaging system includes: receiving a diffusion tensor imaging (DTI) command, the command including a number of gradient vectors; obtaining a set of gradient vectors according to the number in the command, by: (i) selecting an initial set of gradient vectors; (ii) adjusting each gradient vector in the set, to generate a set of adjusted gradient vectors; (iii) determining a cost function based on the set of adjusted gradient vectors, the cost function indicating an impact of the set of adjusted gradient vectors on diffusion tensor variance; (iv) repeating the selecting, the adjusting, and the determining until an optimization condition is met, to obtain a final set of gradient vectors; and controlling the MR imaging system to capture a set of images based on the final set of gradient vectors.

This specification discloses an ultrasound image enhancement method and system. The ultrasound image enhancement method includes: acquiring an ultrasound image; determining the structure tensor of the ultrasound image; determining the diffusion tensor, image mask, and radiation pattern of the ultrasound image based on the structure tensor; performing speckle and noise suppression processing on the ultrasound image based on the diffusion tensor to obtain a first image; performing edge enhancement processing on the ultrasound image based on the image mask and the radiation pattern to obtain a second image; and fusing the first image and the second image to obtain a target image.

A diffusion skewness imaging method with positive semi-definite constraint based on Q-space trajectory imaging comprises the following steps: 1) expanding a diffusion signal and a diffusion tensor distribution model, and introducing a high-order skewness tensor; 2) solving the diffusion tensor distribution model expanded to the high-order skewness direction by utilizing positive semi-definite programming to obtain a high-order skewness tensor; diffusion tensor lt is carried out on related solving variables; dgt; performing positive semidefinite constraint on the covariance tensor and the skewness tensor; and 3) designing a linear trajectory weighting filter and a secondary trajectory weighting filter according to a result obtained by calculation. A large number of experimental verification is carried out on a public data set, a noisy data set and a synthetic data set, and experimental results show that the method can obtain an estimation result closer to a true value on the synthetic data set and shows high robustness on the noisy data set.

The disclosure provides a diffusion kurtosis imaging method, which includes acquiring scan image signals of a scanned object; fitting the scan image signals using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor; determining at least one type of parameters of diffusion tensor imaging parameters or kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor; and generating a parameter image based on the at least one type of parameters of diffusion tensor imaging parameters or kurtosis tensor imaging parameters.

The invention discloses a multi-modal brain disease diagnosis method based on double contrast learning, which comprises the following steps of: acquiring multi-modal brain image data containing functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) and corresponding diagnosis text description, and constructing a multi-modal brain disease data set; establishing a multi-modal brain disease diagnosis model architecture based on double contrast learning, wherein the architecture comprises a multi-modal contrast learning stage and a cross-modal recovery learning stage; a two-stage training strategy is adopted, firstly, a complete sample is used for multi-modal contrast learning training, then, an incomplete sample is used for cross-modal recovery learning training, and the training process is optimized through joint optimization of contrast loss, reconstruction loss and classification loss; and finally, performing modal recovery and feature fusion on the to-be-diagnosed sample with the missing modal through the training model, and outputting a brain disease diagnosis result. According to the method, the accuracy and robustness of brain disease diagnosis are remarkably improved, the adaptability to multi-modal data under different missing rate conditions is enhanced, and the technical problems that in the prior art, the diagnosis performance is reduced due to modal missing, semantic consistency of recovery features is lacked, and the model generalization ability is insufficient under the high missing rate condition are solved.

The present invention discloses a diffusion magnetic resonance (MR) method for measuring arterial pulsation dependence of perivascular cerebrospinal fluid flow in glymphatic system: DTI acquisition: Brain MRI images were acquired using dynamic diffusion tensor imaging (DTI); Cardiac signal synchronization: Simultaneously collect heart rate fluctuation time-series signals; Peak coordinate determination: Identify the peak timing of cardiac pulsation signals from the time-series data; Image realignment: Reorganize brain MR images according to their temporal positions within the cardiac cycle; Temporal interpolation: Perform uniform time-sampling reconstruction to generate equidistant diffusion MRI datasets across the cardiac cycle; Parameter calculation: Compute axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) at each voxel level; Mask-based analysis: Generate characteristic curves of AD / RD / MD and spin density(S) dynamics using region-specific masks in individual space. This method enables non-invasive measurement of: (1) Cerebrospinal fluid (CSF) flow velocity / direction in large perivascular spaces during heartbeats; (2) Microvascular perivascular CSF dynamics through diffusion parameter analysis.

The invention relates to the technical field of nerve tract recognition, in particular to a spinal nerve tract injury assessment method based on diffusion tensor imaging. The method comprises the following steps: firstly, acquiring diffusion tensor imaging data of a spinal cord part of a patient, and constructing a fault-tolerant attraction domain based on atlas registration and morphological dilation processing so as to overcome anatomical positioning errors; determining an individualized liquefaction entropy reference according to entropy distribution of the high-diffusion sample set, and respectively constructing a conduction resistance field and a microstructure confusion field; constructing a nonlinear suppression weight by using an entropy reference to carry out weighted coupling on the resistance field, and generating a cost field for suppressing liquefaction artifacts, so that a liquefaction region is converted into a high-resistance barrier; executing anisotropic path finding based on the cost field in the fault-tolerant attraction domain to obtain an optimal transmission path; and finally, extracting multi-dimensional features of the path, and determining the functional state of the neural pathway in combination with hierarchical logic. According to the method, liquefaction artifact interference can be effectively eliminated, and adaptive and refined quantitative evaluation of the spinal nerve tract injury state is realized.

The present application provides a magnetic resonance imaging apparatus, an image analysis apparatus, and a fluid analysis method. Fluid parameters such as WSS and EL are accurately calculated using flow velocity information obtained by diffusion tensor imaging. A variance of a flow velocity distribution of a fluid is calculated using a diffusion tensor image obtained for an examination object including the fluid, a model for estimation is set for a distribution shape of the flow velocity within a voxel, and a differential value of the flow velocity is calculated using the model for estimation and the variance of the flow velocity distribution. Fluid parameters representing characteristics of flow of the fluid are calculated using the calculated differential value.

The present application relates to the technical field of three-dimensional rendering, in particular to a nerve pathway three-dimensional modeling and rendering method and system, comprising the following steps: obtaining diffusion tensor data to solve the principal eigenvector field, generating fiber tracks based on divergence step length and vorticity correction, obtaining a geometric model by using curvature frequency to reconstruct abnormal sections, mapping vertex geometric curvature eigenvalues, attenuating transparency according to the curvature difference of adjacent depth layers, and synthesizing nerve pathway visualization images. The present application uses the principal eigenvector field to realize dynamic adjustment of spatial step length and correction of tracking direction by divergence and vorticity characteristics calculation, identifies high-frequency disturbance sections after statistical distribution of the change rate frequency of the discrete curvature derivative of the track point sequence, effectively corrects the model distortion caused by sudden changes in curvature, and performs saliency judgment according to the curvature value difference between depth layers in the rendering stage, controls the transparency in a regional manner, so that the image retains structural details while optimizing rendering efficiency.

The present application relates to geological exploration data processing and three-dimensional geological modeling technical field, disclose a kind of abnormal detection and repair method of geology and mineral exploration drilling data, the method first constructs unstructured three-dimensional Delaunay tetrahedron grid, establishes spatial topology;Using linear unit theory analysis tetrahedron unit internal attribute variation rate, calculate original discrete gradient vector;Based on the curvature of drilling trajectory constructs the geological confidence field reflecting positioning accuracy;By minimizing the component of rotation to optimize gradient field, output the modified gradient field in line with physical conservation;Further calculate local structure tensor, identify stratum direction by eigenvalue decomposition and construct anisotropic diffusion tensor;Finally, establish the anisotropic Poisson equation containing diffusion tensor and the divergence of modified gradient field, solve the equation using confidence screening boundary condition.The present application can effectively remove outlier noise, and maintain stratum bedding structure in repair process, improve geology and mineral data reconstruction accuracy.

The application belongs to the technical field of brain image processing, and aims to solve the problem of inaccurate classification of Alzheimer's disease. A method and system for classifying Alzheimer's disease based on topology perception and group hypergraph are proposed. The method extracts time series from resting-state functional magnetic resonance imaging data and constructs dynamic functional connectivity network sequences through a sliding window strategy. Local topology features and global topology features of each time window are extracted using local and global topology perception encoders, respectively, and then subjected to deep interaction and fusion to generate comprehensive feature representations at the subject level. Each subject is treated as a hypergraph node, and hyperedges are constructed based on the comprehensive feature representations at the subject level, combined with the feature similarity calculated from the diffusion tensor imaging features and clinical embedding features of the subjects. A group hypergraph is then constructed, and a hypergraph neural network is used to obtain the classification results, effectively improving the accuracy of early classification and diagnosis of Alzheimer's disease.