*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
TITLE: Statistical Methods for Analysis of Diffusion Weighted Magnetic Resonance Imaging
SPEAKER: Sofia Olhede
ABSTRACT:
High angular resolution diffusion imaging data is the observed
characteristic function for the local diffusion of water molecules in
tissue. This data is used to infer structural information in brain
imaging. Non-parametric scalar measures are proposed to summarize
such data, and to locally characterize spatial features of the
diffusion probability density function (PDF), relying on the geometry
of the characteristic function. Summary statistics are defined so
that their distributions are, to first order, both independent of
nuisance parameters and analytically tractable. The dominant
direction of the diffusion at a spatial location (voxel) is
determined, and a new set of axes are introduced in Fourier space.
Variation quantified in these axes determines the local spatial
properties of the diffusion density. Non-parametric hypothesis tests
for determining whether the diffusion is unimodal, isotropic or
multi-modal are proposed. More subtle characteristics of white-matter
microstructure, such as the degree of anisotropy of the PDF and
symmetry compared with a variety of asymmetric PDF alternatives, may
be ascertained directly in the Fourier domain without parametric
assumptions on the form of the diffusion~PDF. We simulate a set of
diffusion processes and characterize their local properties using the
newly introduced summaries. We show how complex white-matter
structures across multiple voxels exhibit clear ellipsoidal and
asymmetric structure in simulation, and assess the performance of the
statistics in clinically-acquired magnetic resonance imaging data.
Joint work with Brandon Whitcher, GSK.
BIO: Sofia C. Olhede was awarded the M.Sci. and Ph.D. degrees in
mathematics from Imperial College London, London, U.K., in 2000 and 2003,
respectively. She was a Lecturer (2002�2006) and Senior Lecturer
(2006�2007) with the Mathematics Department, Imperial College London. In
2007, she joined the Department of Statistical Science, University College
London, where she is Pearson Professor of Statistics and Honorary
Professor of Computer Science. Her research interests include the analysis
of nonstationary time series, inhomogeneous random fields and applications
in geoscience, medical imaging and oceanography. Prof. Olhede is an
Associate Editor of the Journal of the Royal Statistical Society, Series B
(Statistical Methodology) and of the IEEE Transactions on Signal
Processing. She is a member of the Programme Committee of the
International
Centre for Mathematical Sciences, and is an Isaac Newton Institute
Correspondent.
