Tract-Specific Analysis of Brain White Matter

Speaker:	Dr. James C. GEE
		Department of Radiologic Science and
		Department of Computer and Information Science
		University of Pennsylvania

Title:		"Tract-Specific Analysis of Brain White Matter"

Date:		Wednesday, 20 August 2008

Time:		11:00am - 12 noon

Venue:		Room 3501 (via lifts 25/26), HKUST

Abstract:

We present a new model-based framework for the statistical analysis of
diffusion imaging data associated with specific white matter tracts. The
framework takes advantage of the fact that several of the major white
matter tracts are thin sheet-like structures that can be effectively
modeled by medial representations. The approach involves segmenting major
tracts and fitting them with deformable geometric medial models. The
medial representation makes it possible to average and combine
tensor-based features along directions locally perpendicular to the
tracts, thus reducing data dimensionality and accounting for errors in
normalization. The framework enables the analysis of individual white
matter structures, and provides a range of possibilities for computing
statistics and visualizing differences between cohorts.


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Biography:

James C. GEE, Ph.D., is Associate Professor of Radiologic Science and
Computer and Information Science, Director of the Penn Image Computing and
Science Laboratory (PICSL), and Co-Director of the Translational
Biomedical Imaging Center, Penn CTSA, and the HHMI-NIBIB Interfaces
Program in Biomedical Imaging and Informational Sciences, all at the
University of Pennsylvania, Philadelphia.  Well known for its
contributions to biomedical image analysis and, in particular, the
emerging field of computational anatomy, PICSL's work focuses on the
development of methods for quantifying the ways in which anatomy can vary
in nature, over time, or as a consequence of disease or intervention.
Some recent developments include approaches for symmetric diffeomorphic
image registration, manifold-based construction of population templates,
statistical shape characterization with the medial representation,
tract-specific diffusion tensor analysis, and nonparametric Markov models
for image segmentation.