A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

The present invention concerns a method for determining whether brain regions of interest having reversible or irreversible pathology. The method includes acquiring over a predetermined period of time a plurality of magnetic resonance imaging (MRI) images for each brain region of interest; analyzing the MRI images to obtain quantitative measurements of the fractional anisotropy (FA) for each brain region of interest and determining that brain regions of interest have reversible pathology when the measured FA increases and then decreases over the predetermined period of time.

1. A medical data processing method of determining information describing the probable position of a neural fiber in a patient's brain, the method comprising the following steps which are constituted to be executed by a computer: a) acquiring patient-specific medical image data describing the brain of the patient; b) acquiring atlas data defining an image-based model of a human brain; c) determining, based on the patient-specific medical image data and the atlas data, seed region data describing seed regions (A, B) in the patient-specific medical image data in which the ends of neural fibers of the patient's brain may be located; d) determining, based on the patient-specific medical image data and the seed region data, neural fiber tract data describing a plurality of potential tracts (T1, T2, T3) which a specific neural fiber may take through the patient's brain; e) determining, based on the atlas data and the neural fiber tract data, a figure of merit for each one of the potential tracts (T1, T2, T3).

Disclosed are a hearing state prediction apparatus and method based on a diffusion tensor image, including: obtaining a diffusion tensor image to be processed, wherein the diffusion tensor image comprises a diffusion-weighted image; generating a diffusion index image based on the diffusion-weighted image; determining a white matter microstructural feature corresponding to the diffusion tensor image, and determining a hearing state corresponding to the diffusion tensor image according to a mapping relationship between the white matter microstructural feature and the hearing state. The apparatus may generate the diffusion index image through the diffusion-weighted image included in the diffusion tensor image, and then the white matter microstructural feature is determined based on the diffusion index image, so that the white matter microstructural feature can be identified more accurately, and the accuracy of a hearing state evaluation result is improved. Meanwhile, the relation between hearing disorder and brain microstructure change is disclosed.

Methods and devices for generating novel diffusion ellipsoid maps from diffusion tensor imaging (DTI) scan data. One example method includes: (i) generating, from DTI scan data, for each voxel in a plurality of voxels in one or more slabs of a target tissue, a respective diffusion tensor; (ii) generating, for each voxel, eigenvalues and eigenvectors of the respective diffusion tensor and a respective set of diffusion parameters; (iii) partitioning the voxels into two groups, wherein voxels, whose respective set of diffusion parameters is such that each element in the set is smaller than a corresponding element in a set of thresholds, are substantially all in a first group of the two groups; and (iv) providing a graphical representation of a diffusion ellipsoid map of at least one of the one or more slabs, wherein ellipsoids, associated with voxels in the first group, are displayed differently to the other ellipsoids. The utility of the disclosed methods and devices in breast cancer and prostate cancer detection is demonstrated.

The invention relates to a medical data processing method for determining the position of a region of interest serving as a start condition for conducting diffusion image-based tracking of nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data.

Methods and devices for generating novel diffusion ellipsoid maps from diffusion tensor imaging (DTI) scan data. One example method includes: (i) generating, from DTI scan data, for each voxel in a plurality of voxels in one or more slabs of a target tissue, a respective diffusion tensor; (ii) generating, for each voxel, eigenvalues and eigenvectors of the respective diffusion tensor and a respective set of diffusion parameters; (iii) partitioning the voxels into two groups, wherein voxels, whose respective set of diffusion parameters is such that each element in the set is smaller than a corresponding element in a set of thresholds, are substantially all in a first group of the two groups; and (iv) providing a graphical representation of a diffusion ellipsoid map of at least one of the one or more slabs, wherein ellipsoids, associated with voxels in the first group, are displayed differently to the other ellipsoids. The utility of the disclosed methods and devices in breast cancer and prostate cancer detection is demonstrated.

System and method for diagnosing brain conditions including evaluating fiber pathways of white matter tracts using a diffusion tensor imaging (DTI) process, tracking the propagation of waves traveling at specific angles to the fiber pathways by performing a 3D magnetic resonance elastography (MRE) process at the same spatial resolution and voxel position as the DTI, analyzing the viscoelastic properties using an inversion having at least nine elastic coefficients, determining the curvature along the pathways, differentiating the spatial-spectral filter twice with respect to arc length along the pathways, and diagnosing a brain condition based on the viscoelastic properties.

Exemplary method, system, and computer-accessible medium can be provided for determining a measure of diffusional kurtosis by receiving data relating to at least one diffusion weighted image, and determining a measure of a diffusional kurtosis as a function of the received data using a closed form solution procedure. In accordance with certain exemplary embodiments of the present disclosure, provided herein are computer-accessible medium, systems and methods for, e.g., imaging in an MRI system, and, more particularly for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging (DKI). For example, DKI can facilitate a characterization of non-Gaussian diffusion of water molecules in biological tissues. The diffusion and kurtosis tensors parameterizing the DKI model can typically be estimated via unconstrained least squares (LS) methods. In the presence of noise, motion, and imaging artifacts, these methods can be prone to producing physically and/or biologically implausible tensor estimates. The exemplary embodiments of the present disclosure can address at least this deficiency by formulating an exemplary estimation problem, e.g., as linearly constrained linear LS, where the constraints can ensure acceptable tensor estimates.

The invention relates to a medical data processing method for determining the position of a nerve fiber based on a diffusion image-based tracking method of tracking nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data.