A method is provided for determining an orientation of nerve fibres relative to a non-physiological electric field. Patient medical image data is acquired, which describes a patient medical image of an anatomical body part of a patient's body. The anatomical body part includes nerve tissue comprising white matter nerve fibres. Diffusion image data is acquired, which describes a diffusion-enhanced image of the anatomical body part. Atlas data is acquired, which describes a spatial distribution of grey value-based tissue classes in a model body part representing a model of the anatomical body part. Based on the patient image data, the diffusion image data, and the atlas data, fibre orientation data is determined. The fibre orientation data describes an orientation of the white matter nerve fibres. Electric field orientation data is acquired, which describes an orientation of the non-physiological electric field. The non-physiological electric field is an electric field simulated around a simulated electrode virtually placed in a predetermined spatial relationship relative to the anatomical body part. Based on the fibre orientation data and the electric field orientation data, relative orientation data is determined. The relative orientation data describes a relative orientation between the orientation of the white matter nerve fibres and the orientation of the electric field.

A method (400) including: determining (702) a registration function [705, Niirf(T1)] for the particular brain in a coordinate space, determining (706) a registered atlas [708, Ard(T1)] from the registration function and an HCP-MMP1 Atlas (102) containing a standard parcellation scheme, performing (310, 619) diffusion tractography to determine a set [621, DTIp(DTI)] of brain tractography images of the particular brain, for a voxel in a particular parcellation in the registered atlas, determining (1105, 1120) voxel level tractography vectors [1123, Vje, Vjn] showing connectivity of the voxel with voxels in other parcellations, classifying (1124) the voxel based on the probability of the voxel being part of the particular parcellation, and repeating (413) the determining of the voxel level tractography vectors and the classifying of the voxels for parcellations of the HCP-MMP1 Atlas to form a personalised brain atlas [1131, PBs Atlas] containing an adjusted parcellation scheme reflecting the particular brain (Bbp).

A method (400) including: determining (702) a registration function [705, Niirf(T1)] for the particular brain in a coordinate space, determining (706) a registered atlas [708, Ard(T1)] from the registration function and an HCP-MMP1 Atlas (102) containing a standard parcellation scheme, performing (310, 619) diffusion tractography to determine a set [621, DTIp(DTI)] of brain tractography images of the particular brain, for a voxel in a particular parcellation in the registered atlas, determining (1105, 1120) voxel level tractography vectors [1123, Vje, Vjn] showing connectivity of the voxel with voxels in other parcellations, classifying (1124) the voxel based on the probability of the voxel being part of the particular parcellation, and repeating (413) the determining of the voxel level tractography vectors and the classifying of the voxels for parcellations of the HCP-MMP1 Atlas to form a personalised brain atlas [1131, PBs Atlas] containing an adjusted parcellation scheme reflecting the particular brain (Bbp).

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.

Method and apparatus for processing diffusion data for identification of white matter tracts in the brain of a patient is provided herein. The method involves, with a processor: generating a connectivity based representation of white matter fibers for multiple different subjects from the connectivity signatures of the fibers from a diffusion magnetic resonance imaging (dMRI) without using the physical coordinates of the fibers; generating a fiber bundle atlas from the connectivity based fiber representation of (a) which define a model of the human brain; adaptively clustering fibers of a new patient utilizing the fiber bundle atlas of (b) to extract white matter tracts without manual intervention in the form of drawing regions of interest; and presenting the selected white matter tracts and diffusion data in a report or on a display device. This method and apparatus can be used even for patients having edema or brain perturbations.

A computing device: compares an anatomical magnetic resonance (MR) image of a patient region and reference anatomical data associated with the region to determine a first transform of a bore anatomical coordinate space of the anatomical MR image to a patient anatomical coordinate space associated with the patient; determines, from the first transform, a second transform of a bore DWMR coordinate space of a DWMR image to a patient DWMR coordinate space associated with the patient, the anatomical and the DWMR images being in respective bore coordinate spaces associated with a bore of an MR device which acquired the anatomical and the DWMR images; transforms, using the second transform, the DWMR image to the patient DWMR coordinate space; and controls a display screen to render the DWMR image, as transformed, according to visual attributes associated with the patient DWMR coordinate space.

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).

Discussed herein is a parametric model for DTI MD histogram fitting, named the Generalized Voss-Dyke function, which is highly successful in segregating NPH cases from potential confounders without reliance on operator dependent region-of-interest analyses or inter-subject registration. The Generalized Voss-Dyke function is useful for managing the imaging of any tissue interfaces.

Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function. The method further includes determining, by the data processing system, the at least one position and/or orientation parameter based on the matched template.

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.