Registration, Atlas Generation, and Statistical Analysis of High Angular Resolution Diffusion Imaging based on Riemannian Structure of Orientation Distribution Functions

Abstract

Progress in the diffusion weighted magnetic resonance imaging (DW-MRI) techniques over the last two decades has enabled neuroscientists to image the human brain white matter in-vivo and from large populations. Recent advances in DW-MRI, such as high angular resolution diffusion imaging (HARDI), model the water diffusion with an orientation distribution function (ODF) that can capture multiple axonal orientations at a voxel. For both scientific and clinical applications, it is necessary to develop methods to represent, compare and make correct inferences from the rich information provided by HARDI data. The main contribution of this thesis is providing such an HARDI-based analysis framework for the studies of white matter similarities and differences across large populations. Under a Riemannian manifold of ODF, the framework includes three components: registration, atlas generation and statistical analysis.