The present disclosure is directed to systems and methods of multiphase pseudo-continuous arterial spin labeling.

A MRS (magnetic resonance spectroscopy or nuclear magnetic resonance NMR)-based PTSD (post-traumatic stress disorder) and mTBI (mild traumatic brain injury) diagnostic system and method uses MRS signals, already pre-processed by the MRS scanner software. The signals are collected in vivo from specific regions of the brain. A wavelet decomposition is applied to the MRS signals, and the amplitude of the wavelet coefficients and their location in the MRS signals are used as features for training diagnostic classifiers of disease states. These classifiers are identified through analysis of features of individuals whose health status is known. Once the classifiers are trained, patients can be diagnosed by using the same wavelet features extracted from in vivo MRS scans of their brain regions.

A cannula with a proximally mounted camera and proximally mounted light sources. The lighting sources have beam axes directed distally, toward a workspace at the distal end of the cannula. The light sources are coupled with focusing lenses, to reduce the beam angle of the lighting sources and reduce glare within the cannula tube.

A device for locating target points on a magnetic resonance image of the brain of a subject includes a trained neural network configured to receive as input a 3D MR image of the brain of a subject, and to output the location, on the image, of at least one determined brain target point. The neural network includes a plurality of processing stages. Each processing stage processes an image at a respective resolution, and the processing stage of lowest resolution outputs an estimate of the location of each target point. Each other processing stage is configured to receive, from a lower resolution processing stage, an estimate of the locations of the target points, crop the input image to a smaller region surrounding each estimated target point, determine an updated estimate of the location of each target point, and provide the updated estimation to the processing stage of the next higher resolution.

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.

An information processing method includes: obtaining first image data indicating an image of at least one portion of a face of a target person from a camera connected to or built into a first computer; obtaining cerebral blood flow information indicating a state of cerebral blood flow of the target person from a detector that is connected to or built into the first computer and that detects the cerebral blood flow information; and displaying, on a display connected to or built into a second computer connected to the first computer through a remote network, an output image including a first image based on the first image data and a second image based on the cerebral blood flow information. The first image is a moving image including the at least one portion of the face, and the second image indicates changes over time in the cerebral blood flow information.

A computing device for use in a system for mapping brain activity of a subject includes a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

Disclosed is apparatus and method for motion tracking of a subject in medical brain imaging. The method comprises providing a light projector and a first camera; projecting a first pattern sequence (S1) onto a surface region of the subject with the light projector, wherein the subject is positioned in a scanner borehole of a medical scanner, the first pattern sequence comprising a first primary pattern (P.sub.1,1) and/or a first secondary pattern (P.sub.1,2); detecting the projected first pattern sequence (S1′) with the first camera; determining a second pattern sequence (S2) comprising a second primary pattern (P.sub.2,1) based on the detected first pattern sequence (S1′); projecting the second pattern sequence (S2) onto a surface region of the subject with the light projector; detecting the projected second pattern sequence (S2′) with the first camera; and determining motion tracking parameters based on the detected second pattern sequence (S2′).

Methods and systems for controlling aneurysm initiation or formation in an individual are presented; the technique comprises receiving morphological data of an artery being indicative of at least first and second geometrical parameters of the artery along its trajectory; analyzing the data to identify at least one flow-diverting location along the artery satisfying first and second predetermined conditions of the geometrical parameters; classifying the individual as having or not having disposition for future formation of an aneurysm, depending respectively on whether the at least one flow-diverting location is identified or not and generating classification data; and generating prediction data for the individual with regard to future aneurysm formation.