Seminars/Colloquia/Invited Talks

Seminars

Baba Vemuri

A Novel Mathematical Model for the Diffusion Weighted MR Signal Reconstruction

PLACE:	Clark 314
EVENT:	CIS Seminar
DATE:	May 01, 2007
TIME:	1:00 - 2:00 PM

Abstract

Diffusion MRI is a non-invasive imaging technique that allows the measurement of water molecular diffusion through tissue in vivo. The directional features of water diffusion allow one to infer the connectivity patterns prevalent in tissue and possibly track changes in this connectivity over time for various clinical applications. In this talk, a novel statistical model for diffusion-attenuated MR signal is presented which involves a continuous probability distribution over the space of symmetric positive definite tensors Pn. This model is general enough to explain water molecular diffusion in a variety of situations involving complex tissue geometry including single and multiple fiber occurrences. The signal at each voxel is represented as a continuous mixture of second order tensors, i.e. the weights in the mixture are represented by a continuous mixing distribution f(D), where D is a member of Pn. In this work, the MR signal is expressed as the Laplace transform of f(D) on Pn. I will present a closed form expression for this Laplace transform, when f(D) is a Wishart or a mixture of Wishart distributions that can be used to represent a large class of distributions on Pn. In this case, the MR signal behavior is given by a Rigaut-type asymptotic fractal expression. I will show that our model is more accurate in representing the diffusion-attenuated MR signal than the classic diffusion tensor models currently in vogue. Using this new model in conjunction with a deconvolution approach, I will present an efficient estimation scheme for the number of fibers and the water molecular displacement probability functions at each lattice point in a HARDI data set. Both synthetic and real data sets are used to depict the performance of the proposed algorithms.

Brief Biography:

Baba Vemuri received his Ph.D. degree in Electrical and Computer Engineering from the University of Texas at Austin in 1987 and joined the Department of Computer and Information Sciences at the University of Florida, Gainesville, where he is currently a University Research Foundation professor of Computer & Information Sciences & Engineering.
His research interests are in Bio-imaging, neuro-image analysis, computational vision, statistical learning, modeling for vision and graphics and applied mathematics. He has published over 100 refereed articles in the fields of medical imaging, computer vision, computer graphics and applied mathematics. His neuro-image analysis research is funded by several NIH grants.
Dr. Vemuri has received several awards for his scholarly work, including the IEEE Fellow (2000), the NSF Research Initiation Award (RIA) (in 1988) and the Whitaker Foundation Award (in 1994). He also received several best paper awards, from IAPR in 1992, 3rd ECCV ’94, and best poster awards from IPMI’01 and IPMI’05. His research paper on level-sets for image segmentation in IEEE TPAMI’95 co-authored with Malladi and Sethian has received over 900+ citations in literature thus far.
Dr. Vemuri has served, as the chair of several conference and has been on the program committees of numerous IEEE conferences and is currently an Associate Editor for the journal of Medical Image Analysis (MedIA) and the journal of Computer Vision and Image Understanding (CVIU). He also served as an Associate Editor for the IEEE TPAMI from ’94-’98 and IEEE TMI from ’97-’03.