38 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 invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention discloses a magnetic resonance method for evaluating the rehabilitation curative effect of child cerebral palsy, and relates to the technical field of medical image processing. The method comprises: obtaining structural image data (T1 weighted imaging and diffusion tensor imaging) and resting state image data of each child patient; and extracting brain and tissue boundaries from the T1 weighted imaging by using a 3D-Unet segmentation model. The resting state and structure image data are preprocessed and aligned to the brain boundary, and then the data are registered to a child standard brain template; based on a child standard brain template, obtaining and normalizing various clinical indexes, splicing the clinical indexes into multi-index vector data, inputting the multi-index vector data into a pre-trained support vector machine model, and outputting clinical index differences; and determining an abnormal brain region of each child according to the difference, finally providing a multi-modal fusion index analysis report, and observing changes of brain functions and white matter integrity before and after rehabilitation.

The invention relates to the technical field of medical image detection, in particular to a medical image analysis method and system based on image processing, and the method comprises the following steps: collecting a diffusion tensor image, carrying out the filtering preprocessing, normalizing the main diffusion direction, extracting an energy super-threshold voxel, calculating the node distance offset, and carrying out the correlation analysis to generate an atlas. And interpolating and aligning the brain return boundary to obtain a coupling graph, and outputting an abnormal probability distribution graph in a classified manner. According to the method, fiber details are ensured by adopting anisotropic diffusion filtering noise reduction of a diffusion tensor image sequence, three-dimensional normalization and wavelet energy analysis are coordinated, node detection specificity is enhanced by dynamic threshold screening, space-time coupling is quantified by combining displacement Euclidean distance and time sequence relevance, and an individualized coupling model is established by interpolating and fusing brain return boundaries; the template rigid constraint is broken through, the support vector machine recognizes an abnormal mode, structural distortion and functional abnormality cross-scale detection is achieved, and the early lesion image marker recognition efficiency is improved.

The invention discloses a noninvasive brain lymphatic network assessment method and system based on magnetic resonance imaging, and relates to the field of medical image processing, and the system comprises a data acquisition module which obtains a 3D-T1WI image, a 3D-T2WI image, a DWI image and a DTI image; the data preprocessing module preprocesses the image; the multi-dimensional function analysis module is used for determining multi-dimensional parameters of a gap around a blood vessel according to the preprocessed 3D-T1WI image and the preprocessed 3D-T2WI image; performing perfusion and dispersion component separation on the preprocessed DWI image to obtain incoherent motion imaging parameters in voxels, and calculating a free water fraction; calculating a diffusion tensor imaging index along the peripheral gap of the blood vessel based on the preprocessed DTI image; and the evaluation analysis module performs evaluation according to an output result of the multi-dimensional function analysis module, and can non-invasively, comprehensively and systematically evaluate the function state of the brain lymphatic system.

The present invention provides a method for identifying mental illness based on pulse structure-function brain network coupling, which belongs to the field of intelligent auxiliary medical diagnosis technology and effectively solves the technical problem of the neurobiological mechanism between structural connection and functional connection that is often ignored in the traditional diagnosis of mental illness. Its technical solution is: first, functional and structural brain networks are extracted from functional magnetic resonance imaging and diffusion tensor imaging; then, the information feature maps of these two brain networks are extracted through BrainNetCNN; then a pulse coupled neural network is constructed to learn the brain structure-function coupling mechanism, so as to derive the pulse structure-function coupling; finally, the obtained coupling information is input into the classification layer to obtain the disease identification result, and the cross-entropy loss function is used to train and optimize the result. The beneficial effects of the present invention are: the present invention helps to deeply understand the neural mechanism of mental illness and has a wide range of application prospects in clinical applications.

The present application relates to a diffusion kurtosis imaging method, apparatus, computer device, storage medium, and computer program product. The method comprises: acquiring a scanned image signal of a scanned object, fitting the scanned image signal using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor, determining diffusion tensor imaging parameters and / 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 parametric image based on the diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters. Because the unconstrained optimization algorithm requires a shorter computation time and higher computational efficiency, the time required to obtain the elements of the first diffusion tensor and the elements of the first kurtosis tensor using the unconstrained optimization algorithm in this embodiment is shorter, thereby improving the efficiency of generating the parametric image and reducing the computation time required to generate the parametric image.

The application discloses a non-invasive brain lymphoid network evaluation method and system based on magnetic resonance imaging, and relates to the field of medical image processing.The system comprises a data acquisition module which acquires 3D-T1WI images, 3D-T2WI images, DWI images and DTI images; a data preprocessing module which pre-processes the images; and a multi-dimensional function analysis module which is used for determining multi-dimensional parameters of perivascular spaces according to the pre-processed 3D-T1WI images and the pre-processed 3D-T2WI images; separating perfusion and diffusion components of the pre-processed DWI images to obtain intra-voxel incoherent motion imaging parameters, and calculating free water fractions; and calculating a perivascular space diffusion tensor imaging index based on the pre-processed DTI images; and an evaluation and analysis module which evaluates according to the output results of the multi-dimensional function analysis module, so that the functional state of the brain lymphoid system can be evaluated non-invasively, comprehensively and systematically.

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.

Embodiments of the present disclosure disclose a method, apparatus, electronic device, and storage medium for spatio-temporal measurement of a neurological network. Among them, a method for spatio-temporal measurement of a neurological network includes: a step of selecting a region of interest, wherein, based on the original relaxation time weighted imaging data of magnetic resonance imaging, a first registration is performed to obtain the relaxation time weighted imaging data after the first registration, and an accurate atlas and a region of interest are obtained based on the relaxation time weighted imaging data after the first registration; a fiber bundle analysis step, wherein, based on the diffusion tensor imaging data of the magnetic resonance imaging, a first preprocessing and a second registration are performed to obtain the diffusion tensor imaging data after the second registration, and fiber bundle construction and processing are performed on the diffusion tensor imaging data after the second registration to obtain a fiber bundle analysis result, the fiber bundle analysis result including a structural network; a network topology analysis step; and a spatio-temporal measurement step.

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 present invention discloses a method and device for analyzing nervous system images, and relates to the technical field of diagnosis and treatment of nervous system diseases. The specific steps of the analysis method are: S100, multimodal data fusion acquisition: collecting patients' functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI), and positron emission tomography (PET) nervous system imaging data through the hospital's internal system. The present invention integrates multimodal data of functional magnetic resonance imaging, magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI), and positron emission tomography (PET), and performs quality assessment and integration processes in a central data processing unit, thereby significantly enhancing the accuracy and reliability of the data. At the same time, a spatiotemporal joint feature analysis model is introduced to connect the time and space dimension information in the image data, and to explore the changing patterns and intrinsic correlations of the data in the spatiotemporal and temporal dimensions, thereby improving the diagnostic accuracy of nervous system diseases.

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 present invention relates to a method and system for identifying the movement of a substance across a biological barrier using diffusion magnetic resonance imaging (dMRI). The method involves administering an exogenous substance to a subject undergoing imaging, and then obtaining one or more images of targeted regions post-administration using dMRI. The images are analyzed to characterize changes in diffusivity, permeability, or diffusion tensor parameters, enabling the determination of the transport of the exogenous substance through the biological barrier.

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 present application provides a method and device for processing ultrasonic images, which are used to solve the problem of how to improve the clarity and contrast of the edges of ultrasonic images at the image level. In the present application, the ultrasonic image is decomposed at multiple scales, the edges of the image at at least one scale are extracted, the eigenvalues and eigenvectors of the image at this scale are obtained based on the structure tensor matrix, and then a diffusion tensor is constructed based on the eigenvalues and eigenvectors to improve the image quality. Thus, an optimization process of the ultrasonic image is realized by using a non-linear diffusion method combined with an edge detection algorithm, and the edges and contrast of the ultrasonic image at this scale are optimized. Then, the ultrasonic images at multiple scales are reconstructed to obtain high-quality ultrasonic images.

Electrical non-invasive brain stimulation (NIBS) delivers weak electrical currents to the brain via electrodes that are affixed to the scalp. NIBS can excite or inhibit the brain in areas that are impacted by that electrical current during and for a short time following stimulation. Electrical NIBS can be used to change brain structure in terms of increasing white matter integrity as measured by diffusion tensor imaging. Together the electrical NIBS can induce changes in brain structure and function. The present methods and devices are adaptable to and configurable for facilitating the enhancement of brain performance, and the treatment of neurological diseases and tissues. The present methods and devices are advantageously designed to utilize modern electrodes deployed with, inter alia, various spatial arrangements, polarities, and current strengths to target brain areas or networks to thereby enhance performance or deliver therapeutic interventions.

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.