2011 Nov 25 at 15:30
Oleg V. Michailovich, Assistant Professor, Department of ECE University of Waterloo
The unique ability of diffusion-weighted MRI (DW-MRI) to generate contrast based on the morphological properties of white matter opens the door to developing qualitatively new methods of early detection and diagnosis of many brain-related disorders. Unfortunately, practical implementation of DW-MRI still poses a number of challenges which hamper its wide-spread integration into standard clinical practice. Chief among these is the problem of prohibitively long scanning times, which necessitates the development of time-efficient methods for acquisition of diffusion data. In many such methods, however, the acceleration entails a trade-off between the time efficiency and the accuracy of signal reconstruction. In such a case, it is imperative for one to be able to understand the effect the above trade-off might have on the accuracy of diagnostic inference. Accordingly, the objective of this talk is twofold. First, using high-angular resolution diffusion imaging (HARDI) as a specific instance of DW-MRI, we will introduce the notion of a directional diffusion structure which, in combination with multidimensional scaling, allows representing HARDI data in a lower dimensional Euclidean space. Subsequently, based on this representation, we will develop an algorithm for detection and classification of first episode schizophrenia. Finally, the above algorithm will be applied to HARDI data acquired by means of compressed sensing and we will demonstrate that the resulting classification error increases insignificantly when the sampling density is reduced to as low as a fourth of its conventional value.
SHORT BIO Oleg Michailovich was born in Saratov (Russia) in 1972. He received an M.Sc. degree (magna cum laude) in electrical engineering from the Saratov State University in 1994, and an M.Sc. and Ph.D. (with distinction) degree in biomedical engineering from the Technion - Israel Institute of Technology in 2003. In the period 2003-2006, Dr. Michailovich was with Tannenbaum's lab at the School of Electrical and Computer Engineering, at the Georgia Tech. He is currently with the Department of Electrical and Computer Engineering at the University of Waterloo. His research interests include the application of image processing to various problems of image reconstruction, segmentation, inverse problems, non-parametric estimations, approximation theory and multiresolution analysis.