COLLOQUIUM
Department of Computer Science and Engineering
University of South Carolina
 
High Field MR Technology Development

Yu Li

McKnight Brain Institute
University of Florida

Date: February 25, 2003 (Tuesday)
Time: 3:30-4:30PM
Place: Swearingen 1A03 (Faculty Lounge)

Abstract

Magnetic resonance (MR) imaging and spectroscopy have been greatly
advanced due to concomitant development in physics, biology,
electrical engineering, chemistry, and computer engineering.  So far,
MR techniques have been widely used in many research and industry
areas, including structural biology, medical diagnosis, biomedical
engineering, pharmaceutics, analytical chemistry, agriculture, and oil
exploration.  The most recent MR research focus has been the
development of high field technologies, with the goal of extracting
very useful bioinformation to address and resolve significant
biomedical and biological issues.  MR signal is information-rich but
has intrinsically low signal-to-noise ratio (SNR). This drawback has
been the most limiting factor in the further application of MR imaging
and spectroscopy.  High field technologies can ultimately improve SNR
performance in that the SNR increases significantly with main magnetic
field (B0).  However, new challenges arise from the B0 increase and
may compromise the SNR improvement.  It is important to optimize all
the factors that determine SNR.  We explain how we incorporate
bioinformation into MR signals in imaging and spectroscopy, and how we
make the optimization in terms of SNR in both hardware and algorithm
design.

Yu Li is currently working as a postdoctoral associate in the RF lab
of Advanced Magnetic Resonance Imaging and Spectroscopy Facilities in
the McKnight Brain Institute at the University of Florida.  He
received his M.S. degree in 2000 and Ph.D. degree in 2002, both in
Electrical and Computer Engineering from the University of Illinois at
Urbana-Champaign.  His research interests include RF coil design for
MR imaging and spectroscopy, high sensitivity probe design for protein
NMR, small volume NMR spectroscopy, and microimaging.