BACKGROUND AND PURPOSE Observing changes to water diffusivity and fractional anisotropy (FA) for particular hippocampal regions may improve the sensitivity and specificity of diffusion tensor MR imaging for hippocampal pathologies like Alzheimer disease and mesial temporal sclerosis. As a first step toward this goal, this study characterized the cytoarchitectural features underlying diffusion a...

This paper presents a framework for analyzing the spatial distribution of neural fibers in the brain, with emphasis on interhemispheric fiber bundles crossing through the corpus callosum. The proposed approach combines methodologies for fiber tracking and spatial normalization and is applied on diffusion tensor images and standard magnetic resonance images.

Diffusion-weighted images of the human brain are acquired more and more routinely in clinical research settings, yet segmenting and labeling white matter tracts in these images is still challenging. We present in this paper a fully automated method to extract many anatomical tracts at once on diffusion tensor images, based on a Markov random field model and anatomical priors. The approach provi...

We present a method for segmenting the thalamus and its subnuclei from Diffusion Tensor Magnetic Resonance Images(DT-MRI) using coupled, region based, level sets in 3D. Each surface, formed from the zero’th level set of the level set function, is associated with the most representative tensor contained within the surface. All neighboring voxels are then assigned to a region by finding the surfa...

In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces conta...