A classical problem with tomographic imaging is the so-called limited data reconstruction problem, which occurs when physical and temporal constraints prevent sufficient coverage of the data space in the Nyquist sense. Traditionally, image reconstruction is performed using Fourier transform-based models, which often results in significant image artifacts (e.g., spurious ringing and loss of spatial resolution). To address this problem, numerous methods have emerged in the past three decades to incorporate a priori information into the imaging process.  This talk will give an overview of recent advances in constrained imaging methods and applications.

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.  He is a recipient of the Sylvia Sorkin Greenfield Best Paper Award of the Medical Physics Journal (1990), the NSF Research Initiation Award (1994) and CAREER Award (1995), and the 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 Incomplete List of Excellent Teachers (1998-2000; 2005; 2007), and was selected as a Distinguished Lecturer of IEEE-EMB Society (2002-2005).  He was inducted as Fellow of American Institute for Medical and Biological Engineering (2004), received the Ronald W. Pratt Faculty Outstanding Teaching Award (2005), elected vice-president (conferences) of IEEE-EMBS (2006-2007; 2008-2009), elected Fellow of IEEE (2006), and received the Engineering Council Award for Excellence in Advising (2006, 2007).