Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis

Abstract

Abstract. Diffusion tensor imaging (DTI) has become the major modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics based on tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics calculated within cross-sections. Examples from a clinical neuroimaging study of the early developing brain illustrate the potential of this new method to assess white matter fiber maturation and integrity.

Diagnostic performance of diffusion tensor imaging parameters in breast cancer and correlation with the prognostic factors

“…FA and RA are diffusion anisotropy indices thus made of combinations of the terms of the diffusion tensor eigenvalues λ 1 , λ 2 , and λ3 . GA is a measure of the “geodesic” distance between tensor and the closest isotropic diffusion tensor …”

Diffusion-Based Population Statistics Using Tract Probability Maps

“…However, TBSS relies on a whole-brain approach disregarding anatomical specificity. Recently, techniques tried to alleviate this problem by analysing WM tracts individually by representing each tract as a lower-dimensional structure, using tubular geometries [3,4,5] or sheet-like parametrizations [6,7]. However, there is an issue in these tract-specific techniques: the need to a priori choose between a tubular or a sheet-like representation of the WM tracts which introduces an artificial bias as some fasciculi as inferior-fronto occipital have a spreading structure which is not accurately represented by a tube and the cingulum are tube-like and not well represented by a sheet.…”

Spatial Consistency in 3D Tract-Based Clustering Statistics

“…Relevant related work includes affine matching of fibers [5], such that diffusion properties are represented along tracts. Inter-subject comparison is not facilitated by either approach, though.…”