A look at how CS can improve on current imaging techniques ]

IEEE SIGNAL PROCESSING MAGAZINE [72] MARCH 2008 1053-5888/08/$25.00©2008IEEE C ompressed sensing (CS) aims to reconstruct signals and images from significantly fewer measurements than were traditionally thought necessary. Magnetic resonance imaging (MRI) is an essential medical imaging tool with an inherently slow data acquisition process. Applying CS to MRI offers potentially significant scan time reductions, with benefits for patients and health care economics. MRI obeys two key requirements for successful application of CS: 1) medical imagery is naturally compressible by sparse coding in an appropriate transform domain (e.g., by wavelet transform), and 2) MRI scanners naturally acquire encoded samples, rather than direct pixel samples (e.g., in spatial-frequency encoding). In this article we review the requirements for successful CS, describe their natural fit to MRI, and then give examples of four interesting applications of CS in MRI. We emphasize [Michael Lustig, David L. Donoho, Juan M. Santos, and John M. Pauly] Compressed Sensing MRI