An important problem in medical image analysis is the segmentation of anatomical regions of interest. Once regions of interest are segmented, one can extract shape, appearance, and structural features that can be analyzed for disease diagnosis or treatment evaluation. Diffusion tensor magnetic resonance imaging (DT-MRI) is a relatively new medical imaging modality that captures unique water dif...

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a non invasive method for brain neuronal fibers delineation. Here we show a modification for DT-MRI that allows delineation of neuronal fibers which are infiltrated by edema. We use the Muliple Tensor Variational (MTV) framework which replaces the diffusion model of DT-MRI with a multiple component model and fits it to the signal attenuati...

We address three crucial issues encountered in DT-MRI (Diffusion Tensor Magnetic Resonance Imaging) : diffusion tensor Estimation, Regularization and fiber bundle Visualization. We first review related algorithms existing in the literature and propose then alternative variational formalisms that lead to new and improved schemes, thanks to the preservation of important tensor constraints (positi...

Background In vivo DT-MRI is challenging because the motion of the heart and respiration influence the parameters of diffusion tensor imaging, and the most of in vivo DT-MRI is performed under breath-hold. The purpose of this study was to evaluate the feasibility of in vivo DT-MRI without breath-hold with regard to changes in direction-dependent water diffusivity reflecting alterations in tissu...

Magnetic resonance electrical impedance tomography (MREIT) is an emerging method to visualize electrical conductivity and/or current density images at low frequencies (below 1 KHz). Injecting currents into an imaging object, one component of the induced magnetic flux density is acquired using an MRI scanner for isotropic conductivity image reconstructions. Diffusion tensor MRI (DT-MRI) measures...

In this work parametric and non-parametric statistical methods are proposed to analyze Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. A Multivariate Normal Distribution is proposed as a parametric statistical model of diffusion tensor data when magnitude MR images contain no artifacts other than Johnson noise. We test this model using Monte Carlo (MC) simulations of DT-MRI experimen...

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI): Method and Application Lan Lin Abstract Diffusion tensor magnetic resonance imaging (DT-MRI) is a recently developed MRI technique that provides the quantitative information about the integrity and orientation of the random motion of hydrogen atoms within water molecules in all three dimensions. It is a unique method of assessing the importa...

The objective of this study was to quantify microstructural remodeling in peri-infarcted and infarcted porcine myocardium using diffusion tensor MRI (DT-MRI) for the first time. High resolution ex vivo late gadolinium enhanced (LGE) MRI was used to segment the DT-MRI data into normal, peri-infarct and infarcted myocardium. LGE-MRI based segmentation produces regions with significantly different...

A new method for diffusion tensor MRI (DT-MRI) regularization is presented that relies on graph diffusion. We represent a DT image using a weighted graph, where the weights of edges are functions of the geodesic distances between tensors. Diffusion across this graph with time is captured by the heat-equation, and the solution, i.e. the heat kernel, is found by exponentiating the Laplacian eigen...

INTRODUCTION Diffusion tensor MRI (DT-MRI) provides a discrete representation of the diffusion tensor field ofwater in living tissues. A graphical means to display the diffusion tensor field, '[\]is to construct diffusion ellipsoid maps or images (1). Moreover, from the diffusion tensor field, one can infer the fiber-tract direction field in some anisotropic tissues (1). 1n white matter and ske...