INTRODUCTION Data analysis in Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is highly sophisticated and can be thought of as a “pipeline” of closely connected processing and modeling steps. Cluster analysis of the orientation of the fiber direction and fiber tracts is typically carried on the major eigenvector. This type of cluster analysis is also important in reducing sorting bias in t...

Introduction: High-angular resolution diffusion imaging (HARDI) is a powerful extension of MRI that maps the directional diffusion of water in the brain, based on applying diffusion-sensitized gradients in 30-100 or more different directions. In the Diffusion Tensor (DT) model, a tensor is fitted to the set of diffusion images, and maps of fiber integrity such as fractional or relative anisotro...

This work helps elucidate how background noise introduces statistical artifacts in the distribution of the sorted eigenvalues and eigenvectors in diffusion tensor MRI (DT-MRI) data. Although it was known that sorting eigenvalues (principal diffusivities) by magnitude introduces a bias in their sample mean within a homogeneous region of interest (ROI), here it is shown that magnitude sorting als...

PURPOSE Various methods exist for interpolating diffusion tensor fields, but none of them linearly interpolate tensor shape attributes. Linear interpolation is expected not to introduce spurious changes in tensor shape. METHODS Herein we define a new linear invariant (LI) tensor interpolation method that linearly interpolates components of tensor shape (tensor invariants) and recapitulates th...

Recent applications of diffusion tensor (DT) magnetic resonance imaging (MRI) in primary progressive (PP) multiple sclerosis (MS) patients have contributed to the understanding of the nature of the damage in the grey and white matter (WM) of the brain (1). Nevertheless, correlations with clinical disability are weak. Regional studies have shown promising results, when DT-derived metrics were av...

BACKGROUND There is growing evidence that schizophrenia is a disorder of cortical connectivity. Specifically, frontotemporal and frontoparietal connections are thought to be functionally impaired. Diffusion tensor magnetic resonance imaging (DT-MRI) is a technique that has the potential to demonstrate structural disconnectivity in schizophrenia. AIMS To investigate the structural integrity of...

Until very recently, the study of neural architecture using fixed tissue has been a major scientific focus of neurologists and neuroanatomists. A non-invasive detailed insight into the brain's axonal connectivity in vivo has only become possible since the development of diffusion tensor magnetic resonance imaging (DT-MRI). This unique approach of analyzing axonal projections in the living brain...

While isosurfaces of anisotropy measures for data from diffusion tensor magnetic resonance imaging (DT-MRI) are known to depict major anatomical structures, the anisotropy metric reduces the rich tensor data to a simple scalar field. In this work, we suggest that the part of the data which has been ignored by the metric can be used to segment anisotropy isosurfaces into anatomically meaningful ...

At the time of the invention of DT-MRI (or DTI), I was employed as a Staff Fellow (a postdoctoral fellow position) in the Mechanical Engineering Section (MES) of the Biomedical Engineering and Instrumentation Branch (BEIB) in the Intramural Research Program (IRP) of the National Center for Research Resources (NCRR) of the National Institutes of Health (NIH). (People in the federal government li...

A new method for mapping diffusivity profiles in tissue is presented. The Bloch-Torrey equation is modified to include a diffusion term with an arbitrary rank Cartesian tensor. This equation is solved to give the expression for the generalized Stejskal-Tanner formula quantifying diffusive attenuation in complicated geometries. This makes it possible to calculate the components of higher-rank te...