In white matter tractography from magnetic resonance imaging, a mathematical representation of diffusion (e.g., fiber orientation distributions) is first estimated from the diffusion MR data. Fiber tracing is performed via deterministic or probabilistic tractography where the tract maps and brain regions from multiple atlases and/or templates can be used for seeding and/or as spatial constraints. Field map correction and/or denoising may improve the diffusion weighted imaging data used in tractography.

A method for selecting a seed area for tracking nerve fibers in a brain includes performing registration of an atlas which shows a plurality of areas which are included in the brain and image data which relates to the brain, displaying a brain area list with respect to the plurality of areas, selecting a first area from the atlas based on a first user input with respect to the brain area list, extracting an area of the image data which corresponds to the first area, as a seed area, based on a result of the registration, and generating a first image which corresponds to the seed area from the image data, and displaying the generated first image.

The identification and determination of aspects of the construction of a patient's heart is important for cardiologists and cardiac surgeons in the diagnosis, analysis, treatment, and management of cardiac patients. For example minimally invasive heart surgery demands knowledge of heart geometry, heart fiber orientation, etc. While medical imaging has advanced significantly the accurate three dimensional (3D) rendering from a series of imaging slices remains a critical step in the planning and execution of patient treatment. Embodiments of the invention construct using diffuse MRI data 3D renderings from iterating connections forms derived from arbitrary smooth frame fields to not only corroborate biological measurements of heart fiber orientation but also provide novel biological views in respect of heart fiber orientation etc.

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.

Methods and systems for analyzing a medical image of a subject's brain are disclosed. Analysis of a medical image of a subject's brain for predictive and diagnostic determination of neurodegenerative disease state. The method comprises parcellating the grey matter in the image of the brain and determining the size of each region to generate an initial pattern of the disease process; applying a diffusion kernel to obtain an output vector; and predicting future changes to the brain based on the output vector. Another method of analyzing a medical image of a subject's brain includes solving for eigen-modes of a connectivity matrix, projecting the eigen-modes onto the initial disease state to produce an output product and diagnosing a disease or lack thereof based on a comparison of the output product to one or more reference standards.

The present disclosure discusses systems and methods for identifying biomarkers that can help with the diagnosis, prognosis, and treatment choices of patients with neurodegenerative diseases. Diffusion based magnetic resonance imaging can often fail for patients with a neurodegenerative disease because parameters fractional anisotropy, mean diffusivity, and radial diffusivity are based on simple models that can fail in the presence of neurodegeneration, such as demyelination. The present disclosure discusses systems and methods that enhance dMRI images and enable tractography to be performed on images of a damaged nervous system. The damaged tracks identified by the present system can be used as a biomarker for the assessment of patients. In some implementations, the biomarkers are converted into clinical scales that can be used to compare patients to one another or over time.

A medical image processing apparatus according to an embodiment includes processing circuitry and a display. The processing circuitry acquires a 1st medical image collected by predetermined imaging for an imaging part of a subject, and a 2nd medical image that is collected by imaging different from the predetermined imaging and includes a blood vessel related to the imaging part, detects a disease candidate region indicating a candidate for a region of a disease at the imaging part based on the 1st medical image, detects a constriction part related to constriction of the blood vessel based on the 2nd medical image, and estimates certainty of the disease for the disease candidate region based on the disease candidate region and the constriction part. The display displays the region of the disease related to the certainty and the 2nd medical image that are superimposed on the 1st medical image.

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).

An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with at least one diffusion magnetic resonance imaging dMRI image of a patient's brain. The processing unit is configured to: 1) determine an estimate of an orientation of neurons at each voxel in the dMRI image; 2) determine a plurality of fiber tracts in the at least one dMRI image; 3) select a plurality of voxels along at least one fiber tract of the plurality of fiber tracts; and 4) determine a neurological disease classification.

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.