A brain measurement apparatus includes: a static magnetic field forming unit for forming a static magnetic field in a measurement area; a gradient magnetic field coil for forming a gradient magnetic field in the measurement area; a transmission coil for transmitting a transmission pulse toward a subject in the measurement area; a detection coil for detecting a nuclear magnetic resonance signal generated in the subject by transmission of the transmission pulse; and a generator for generating an MR image based on the nuclear magnetic resonance signal detected by the detection coil.

Among the various aspects of the present disclosure is the provision of a noninvasive and localized brain liquid biopsy using focused ultrasound. Briefly, therefore, the present disclosure is directed to methods and systems to identify brain lesion or tumor characteristics without the need for a solid brain biopsy.

In one aspect, there is provided a method performed by one or more data processing apparatus, the method including obtaining a baseline image of a baseline biological organism brain, obtaining a follow-up image of a target biological organism brain, wherein the follow-up image shows at least a damaged region of the target biological organism brain, processing the baseline image and the follow-up image to generate data defining a predicted anatomical microstructure of the damaged region of the target biological organism brain before the target biological organism brain was damaged, and generating a design for a neural prosthesis for replacing the damaged region of the target biological organism brain based on the predicted anatomical microstructure of the damaged region of the target biological organism brain before the target biological organism brain was damaged.

Disclosed herein is a method for diagnosing a neurological disorder based on at least one magnetic resonance imaging (MRI) image. The method includes identifying brain image regions that contain a respective portion of diffusion index values of at least one diffusion index. For each of the brain image regions, a characteristic parameter based on the respective portion of the diffusion index values is calculated. a diagnoses is then made for the brain using one of predetermined categories of the neurological disorder by performing classification on a combination of the characteristic parameters via a classifier.

A method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification comprises providing a multilayer convolutional network for neurological tumor identification configured for segmenting data sets of full neurologic scans into resolution voxels; supervised learning and validation of the platform by classification of tissue within classification voxels of a specific given training and validation data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training and validation data sets having a predetermined ground truth; and implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of each data set assigned a label. The platform may be used for T-cell therapy initiation and tracking. An artificial intelligence based neuroradiology platform implemented according to the method is disclosed.

An ambulance-compatible magnetic resonance imaging (MRI) system for on-site emergency diagnosis includes a mid-field super-conducting head-only magnet including a bore and an active shield arranged relative to the magnet, a passive shield arranged relative to the magnet, the passive shield including a first flange arranged adjacent to a first side of the magnet bore, a second flange arranged adjacent to a second side of the magnet bore, wherein the first flange and the second flange are electrically connected to each other, and wherein the passive shield is operative to capture flux extending out from the magnet bore and return the flux to the magnet. An asymmetric head gradient assembly for generating magnetic gradient field in the mid-field super-conducting magnet is also provided, the magnetic gradient field being between 100-150 mT/m or having a slew rate between 400-800 T/m/s. The MRI system includes a receiver coil and a controller operatively coupled to the receive coil, the controller configured to produce an image based on data obtained from the receive coil. The MRI system is mountable in an ambulance vehicle.

A system and a method for Alzheimer's disease prediction using a neural network, a computer-readable recording medium with a stored program, and a computer program product are provided. The processor obtains a first brain MRI data, a second brain MRI data, a first neuropsychological assessment score, and a second neuropsychological assessment score. The processor obtains a plurality of image feature data according to the first brain MRI data and the second brain MRI data. Each image feature data is selected from a group consisting of a plurality of hippocampal subfield geometric change data. The processor obtains a neuropsychological change data according to the first neuropsychological assessment score and the second neuropsychological assessment score. The processor obtains an Alzheimer's disease prediction result according to the neural network module, the image feature data, and the neuropsychological change data.

The present invention is directed to a method for determining a paroxysmal slow waves event (PSWE) so as to determine blood-brain barrier dysfunction (BBBD) or increased risk of developing a neurological disease or disorder in a subject.

Systems and methods are provided for evaluating a patient for a neurological movement disorder. A three-dimensional medical image of a brain of the patient is captured and provided to an artificial neural network having at least one convolutional layer to provide a set of output values. The set of output values is provided to a machine learning model to provide a clinical parameter representing one of a presence of the neurological movement disorder in the patient and a response of the patient to a specific treatment for the neurological movement disorder.

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.