Multidimensional Signal Processing, Biomedical Imaging, Neuroimaging, Magnetic Resonance Imaging, Constrained Image Reconstruction, Signal Modeling, Inverse Problems, Parameter Estimation, Experiment Design
Magnetic resonance (MR) neuroimaging technology has created unprecedented opportunities to unveil the mysteries of the central nervous system, probing scales ranging from organs and systems down to individual cells and molecules, and enabling the visualization and quantification of anatomy, physiology, and metabolism. However, while MR neuroimaging techniques have been developing for decades, many advanced imaging protocols are still impractical for common use due to long data acquisition times, limited signal-to-noise ratio, and various other practical and experimental factors – this limits the amount of information we can extract from living human subjects, despite the known power and flexibility of current MR technology. Our research group addresses such limitations from a signal processing perspective, developing novel methods for data acquisition, image reconstruction, and parameter estimation approaches that combine: (1) the modeling and manipulation of physical imaging processes; (2) use of novel constrained signal and image models; (3) novel theory to characterize signal estimation frameworks; and (4) fast computational algorithms and hardware. We are seeking excellent students with a strong background in signal processing, with an interest in developing methods to improve existing advanced MR methods and enable the next generation of imaging-based biomedical and neuroscientific inquiry.
Ph.D. in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2011, Champaign, IL
Justin P. 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 Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign.
He is currently an Assistant Professor in the Ming Hsieh Department of Electrical Engineering at the University of Southern California. His research focuses primarily on the development of new data acquisition and signal processing methods for improved and accelerated magnetic resonance neuroimaging. His work has been recognized with a number of awards, including best paper awards at the 2010 International Symposium on Biomedical Imaging and the 2010 Annual International Conference of the IEEE Engineering in Medicine in Biology Society.