A method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive impairment and predict its future change based on the at least one feature obtained.

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.

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.

There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output. The intermediate outputs are inputted into a single common decoding-expanding component for computing the indication of segmented 3D ROI(s).

Aspects of the disclosure provide a method for denoising an image. The method can include receiving an acquired image from an image acquisition system, and processing the acquired image with a nonlinear diffusion coefficient based filter having a diffusion coefficient that is calculated using gradient vector orientation information in the acquired image.

The invention relates to a system and method for quantifying a novel biomarker of the tissue activity of a human or animal organ. By way of preferred application, such a biomarker describes the diffusivity of biological fluids in living tissues in the form of a novel indicator of the diffusion of water molecules in living tissues on the basis of diffusion data resulting from the acquisition of a sequence of images of one or more parts of the body of an animal or human patient. Particularly resistant and stable with respect to noise present in the medical imaging signals from which the experimental data stem, the novel biomarker is relevant in a large number of applications including, inexhaustively, the analysis and/or monitoring of cancers, or the assessment of strokes.

A method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive impairment and predict its future change based on the at least one feature obtained.

Distortion generated in an image is effectively corrected in imaging using an EPI sequence such as DWI without extending an imaging time. After one excitation RF pulse of EPI is applied, a navigator scan in which the polarity of the phase encoding is opposite to that of the main scan is performed continuously to the main scan, and the distortion of the image by using the navigator scan data obtained by the navigator scan is corrected. In a case of multi-shot, phase information obtained from the navigator scan data for each shot is used to perform phase correction and multi-shot reconstruction on the main scan data of each shot.

The present disclosure provides an operating method of a brain imaging neurological abnormality prediction system, which includes steps as follows. The T1-weighted image and the diffusion-weighted image of the patient are acquired; the image process is performed on the T1-weighted image and the diffusion-weighted image to obtain a smoothed brain standard space infarction image; the smoothed brain standard space infarction image is multiplied by and a weighted image for a post-processing to obtain a post-weight image; the post-weight image is inputted to the deep learning cross validation classification model of transfer learning to predict whether the neurological abnormality occurs within a predetermined period after the patient's brain disease.

Described are a system, method, and computer program product for detecting neurodegeneration using differential tractography and treating neurological disorders accordingly. The method includes obtaining a first diffusion magnetic resonance imaging (MRI) scan of the brain of the patient and obtaining a plurality of diffusion MRI scans of a group of other brains. The method also includes generating a control diffusion MRI scan based on the plurality of diffusion MRI scans of the group of other brains. The method further includes determining a first anisotropy of first neural tracks of the first diffusion MRI scan and a second anisotropy of second neural tracks of the control diffusion MRI scan. The method further includes determining a differential by comparing the first anisotropy to the second anisotropy and identifying at least one neurological disorder based on the differential and a location of the first neural tracks in the brain of the patient.