Statistical Methods for Analysis of Diffusion Weighted Magnetic Resonance Imaging

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TITLE: Statistical Methods for Analysis of Diffusion Weighted Magnetic Resonance Imaging

SPEAKER: Sofia Olhede


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.


  • Workflow Status:Published
  • Created By:Anita Race
  • Created:10/13/2010
  • Modified By:Fletcher Moore
  • Modified:10/07/2016


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