Keynote Lecture
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Abstract
Conventional imaging methods are based on Shannon sampling theory. As such, the number of Nyquist samples (or measurements) grows exponentially as the physical dimension of the underlying imaging problem increases (the so-called curse of dimensionality), rendering it difficult to achieve high resolution for high-dimensional imaging. Sub-Nyquist sampling is possible for sparse and/or partially seperable signals and is providing a powerful way to speed up various imaging experiments. This talk will provide an overview of recent sparse sampling methods based on compressed sensing theory and partial seperable function theory. An emphasis will be placed on discussing the issues of image reconstruction from sub-Nyquist data with sparsity and rank constraints and demonstrating their potential applications.
Biography
Dr. Liang's research interests include magnetic
resonance imaging, superresolution image reconstruction using a priori
constraints, statistical and learning-based methods for biomedical image
analysis, and their application to functional brain mapping, cancer imaging, and
cardiac imaging.
Dr. Liang is a recipient of the Sylvia Sorkin Greenfield Best Paper Award of the
Medical Physics Journal (1990), an NSF Research Initiation Award (1994) and
CAREER Award (1995), and an IEEE-EMBS Early Career Achievement Award (1999). He
was named Fellow of the UIUC Center for Advanced Study (1997), Henry Magnuski
Scholar (1999-2001), and University Scholar (2001-2004). He has appeared several
times in the Daily Illini List of Excellent Teachers (1998-2000; 2005; 2007,
2008), was selected as a Distinguished Lecturer of IEEE-EMBS (2002-2005), and
received the Ronald W. Pratt Faculty Outstanding Teaching Award (2005), and the
Engineering Council Award for Excellence in Advising (2006, 2007, 2008). Dr.
Liang was elected as Vice President (2006-2009), President-elect (2010), and
President (2011-2012) of IEEE-EMBS. Dr. Liang is a Fellow of American Institute
for Medical and Biological Engineering (2005), a Fellow of IEEE (2006), and a
Fellow of International Society of Magnetic Resonance in Medicine (2010).