Magnetic resonance (MR) imaging technologies provide unique capabilities to probe the mysteries of biological systems, and have enabled novel insights into anatomy, metabolism, and physiology in both health and disease. However, while MR imaging is decades old and has already revolutionized fields like medicine and neuroscience, current methods are still far from fully realizing the potential of the MR signal. In particular, traditional methods are based on the Fourier transform, and suffer from fundamental trade-offs between signal-to-noise ratio (SNR), spatial resolution, and data acquisition speed. These issues are exacerbated in high-dimensional applications, due to the curse of dimensionality. Our work addresses the limitations of traditional MR imaging using signal processing approaches that have recently become practical because of improvements in modern computational capabilities. These approaches are possible because of the "blessings of dimensionality," i.e., the observation that high-dimensional data often possesses unexpectedly simple structure, which can be exploited to alleviate the classical barriers to fast high-resolution imaging. This seminar will describe approaches we have developed that use novel constrained imaging models (based on sparsity, partial separability, linear predictability, etc.) to guide the design of new MR data acquisition and image reconstruction methods, and enable substantial acceleration of both low-dimensional and high-dimensional MR imaging experiments. These methods will be illustrated in the context of applications such as fast high-resolution T1-weighted anatomical imaging, fast sub-millimeter diffusion imaging, ungated free-breathing cardiac imaging, and a novel high-dimensional diffusion-relaxation hybrid experiment that provides unique insights into tissue microstructure.
About the Speaker
Justin Haldar received the B.S. and M.S. degrees in Electrical Engineering in 2004 and 2005, respectively, and the Ph.D. in Electrical and Computer Engineering in 2011, all from the University of Illinois at Urbana-Champaign. He is currently an Assistant Professor of Electrical Engineering and Biomedical Engineering at the University of Southern California, where he co-directs the Biomedical Imaging Group and is affiliated with the Signal and Image Processing Institute, the Dana & David Dornsife Cognitive Neuroscience Imaging Center, and the Brain and Creativity Institute. His research interests include image reconstruction, signal modeling, parameter estimation, computational methods, and experiment design for imaging problems, with a particular focus on biomedical magnetic resonance imaging applications. His research has been recognized with a 2014 CAREER award from the National Science Foundation, a best student paper award at the 2010 IEEE International Symposium on Biomedical Imaging, and the first-place award in the student paper competition at the 2010 international conference of the IEEE Engineering in Medicine and Biology Society. He is a member of the IEEE Signal Processing Society's Bio Imaging and Signal Processing (BISP) Technical Committee, a member of the IEEE Signal Processing Society's Computational Imaging (CI) Special Interest Group, and an Associate Editor for IEEE Transactions on Medical Imaging.