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.

An image analysis system including at least one processor and at least one memory is provided. The image analysis system is configured to receive image data associated with a brain of a patient, the image data including a first three-dimensional (3D) diffusion weighted imaging (DWI) image acquired using a magnetic resonance imaging (MRI) system and a second 3D DWI image, concurrently provide the first 3D DWI image to a first channel of a trained model and the second 3D DWI image to a second channel of the trained model, receive an indicator associated with the first 3D DWI image and the second 3D DWI image from the model, generate a report based on the indicator, and cause the report to be output to at least one of a memory or a display.

An imaging method may include obtaining imaging data associated with a region of interest (ROI) of an object. The imaging data may correspond to a plurality of time-series images of the ROI. The imaging method may also include determining, based on the imaging data, a data set including a spatial basis and one or more temporal bases. The spatial basis may include spatial information of the imaging data. The one or more temporal bases may include temporal information of the imaging data. The imaging method may also include storing, in a storage medium, the spatial basis and the one or more temporal bases.

A medical image processing apparatus including processing circuitry configured to: obtain from medical imaging measurements, observations of one or more vector or tensor valued fields as projected from one or more 2D acquisition planes; use an optimisation procedure to determine from the observations a superset of 3D fields (which may be scalar, vector, or tensor) via a solution ansatz constrained by a system of partial differential equations, and output the plurality of these fields.

A method of generating a quantitative characterization of injury presence and status of spinal cord tissue using an adaptive CNN system for use in diagnostic assessment, surgical planning, and therapeutic strategy comprises preprocessing for artifact correction of diffusion based, spinal cord MM data, training an adaptive CNN system with healthy and abnormal (injured/pathologic) spinal cord images obtained by imaging a population of healthy, typically developed spinal cord subjects and subjects with spinal cord injury, evaluating a novel, diffusion-based MM image for injury biomarkers using the adaptive CNN system, generating a three-dimensional predictive axonal damage map for quantitative characterization and visualization of the novel, diffusion-based MM image, and transmitting the sets of healthy and injured spinal cord images back to a central database for continued improvement of the adaptive CNN system training. A system for defining a predictive spinal axonal damage map is also described.

A method for localizing structural connectivity biomarkers in neurological diseases, includes dividing a diffusion magnetic resonance imaging brain volume into a set of connected brain regions; extracting three-dimensional voxels along fiber connections which structurally connect the connected brain regions, wherein the brain regions comprise bundles of neurons; applying a deep neural network to diffusion magnetic resonance imaging features extracted from the three-dimensional voxels for each set of fiber connections which structurally connect brain regions; outputting a disease classification based on applying the deep neural network; and applying multi-instance learning to predict whether each fiber connection is indicative of a healthy brain or a diseased brain.

Systems and methods for denoising a magnetic resonance (MR) image utilize an unsupervised deep convolutional neural network (U-DCNN). Magnetic resonance (MR) image data of an area of interest of a subject can be acquired, which can include noisy input images that comprise noise data and noise free image data. For each of the noisy input images, iterations can be run of a converging sequence in an unsupervised deep convolutional neural network. In each iteration, parameter settings are updated; the parameter settings are used in calculating a series of image feature sets with the U-DCNN. The image feature sets predict an output image. The converging sequence of the U-DCNN is terminated before the feature sets predict a respective output image that replicates all of the noise data from the noisy input image. Based on a selected feature set, a denoised MR image of the area of interest of the subject can be output.

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 present disclosure provides a method and system for processing multi-modality images. The method may include obtaining multi-modality images; registering the multi-modality images; fusing the multi-modality images; generating a reconstructed image based on a fusion result of the multi-modality images; and determining a removal range with respect to a focus based on the reconstructed image. The multi-modality images may include at least three modalities. The multi-modality images may include a focus.

Systems and methods for evaluating an anatomical structure in a brain of a subject are provided. In an embodiment, a system for evaluating an anatomical structure in a brain of a subject includes a computing device in communication with a magnetic resonance imaging (MRI) device. The computing device operable to determine an abnormality in the anatomical structure by comparing a test activation level within a geometry of the anatomical structure to data in a normative database, and output, to a display device, a graphical representation of the abnormality in the anatomical structure. The test activation level is determined by aligning functional magnetic resonance imaging (fMRI) data obtained by use of the MRI device and the geometry of the anatomical structure. The geometry of the anatomical structure is delineated based on segmentation of magnetic resonance (MR) data obtained by use of the MRI device. The data in the normative database include activation levels of the anatomical structure of a plurality of neurologically non-diseased subjects.