Compressed sensing in multi-sensor architectures

My postdoc Il Yong Chun and I recently completed a new paper titled Compressed sensing and parallel acquisition.  In it we present a theoretical framework for the use of compressed sensing in multi-sensor systems.  These systems are found numerous applications, ranging from parallel MRI, to multi-view imaging and generalized sampling theory.  Our work provides a first mathematical analysis of the gains offered by parallel acquisition and compressed sensing, and presents new insight into questions of optimal sensor design.

Work featured in the AIM Newsletter

My new collaboration (with Rick Archibald, Anne Gelb, Jan Hesthaven, Rodrigo Platte, Guohui Song and Ed Walsh) was featured in the 2015 American Institute of Mathematics Newsletter.

To quote from the article: …our group is proving how other data inherent to the [MRI] scanning process, such as resonant frequencies and signal decay rates, which are currently used only to provide contrast, can be useful in diagnosing a condition or measuring a response to treatment. Much more information can be extrapolated from the same scan: temperature, blood ow, di usion, structure, and physiology, for example. We’re developing nonconventional image reconstruction techniques to get this information faster and better than has ever been possible before.