Events Calendar

Faster and Better: Signal Processing Approaches to High Dimensional MR Neuroimaging

Speaker: Research Assistant Professor Justin Haldar, University of Southern California

Talk Title: Faster and Better: Signal Processing Approaches to High Dimensional MR Neuroimaging

Abstract: Magnetic resonance (MR) imaging technologies have enabled new opportunities to reveal the mysteries of the central nervous system -- its function and organization, and what goes wrong when it is injured or diseased. MR experiments are quite flexible, and the MR signal can be manipulated to noninvasively probe anatomy, physiology, and metabolism. However, while MR imaging is decades old and has already revolutionized medical imaging, current methods are still far from utilizing the full potential of the MR signal. In particular, traditional MR methods are based on the Fourier transform, and suffer from fundamental trade-offs between signal-to-noise ratio, spatial resolution, and data acquisition speed. These issues are exacerbated in high-dimensional applications, due to the curse of dimensionality.

Classical approaches to addressing these trade-offs have relied on improved imaging hardware and more efficient pulse sequences. In contrast, our work addresses the limitations of MR using relatively less-explored signal processing approaches, which have recently become practical because of increasing computational capabilities. This talk will illustrate some of our new approaches in the context of MR diffusion imaging, a powerful 6 dimensional imaging modality that can be used to characterize the microstructure and connectivity of the brain. To reconstruct three of these dimensions, we leverage an appropriate imaging model to guide the design of both data acquisition and image reconstruction, which can free us from some of the constraints of traditional Fourier imaging. For the remaining three dimensions, we describe new linear transform techniques to extract important diffusion information from reduced data, i.e., data sampled on the surface of a Fourier 2-sphere. The benefits of these approaches are illustrated in the context of microstructural and connectivity assessments of the brain and spinal cord.


Biography: 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 a Research Assistant Professor in the Ming Hsieh Department of Electrical Engineering at the University of Southern California, where he 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, and experiment design for biomedical imaging applications, with a particular focus on magnetic resonance imaging and spectroscopy. His work on constrained imaging has been recognized with 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. Weblink: http://mr.usc.edu/

Host: Professor Shrikanth Narayanan

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

Audiences: Everyone Is Invited

Contact: Mary Francis