[CTN] CTN seminar: Dr. Oleg Michailovich, Nov 22nd, PAS 2464, 3:30

[Matthijs van der Meer]

Dear all,

Please join us for next Tuesday's CTN seminar (November 22) by Dr. Oleg
Michailovich, from our very own ECE department. Title and abstract
follow below.

Time and place are the usual, 3.30pm on Tuesday in PAS 2464.
Refreshments will be provided.

If you would like to meet with Dr. Michailovich, feel free to contact
him directly -- details are below.

Hope to see you all there!

- Matt


Oleg V. Michailovich, PhD
Assistant Professor
Department of ECE
University of Waterloo
<olegm at uwaterloo.ca>

TITLE
HARDI-based diagnosis of first episode schizophrenia using isometric
embedding and compressed sensing

ABSTRACT
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.