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Abstract #3256

Efficient 2D MRI Relaxometry via Compressed Sensing

Ruiliang Bai 1,2 , Alexander Cloninger 3 , Wojciech Czaja 4 , and Peter J. Basser 1

1 Section on Tissue Biophysics and Biomimetics, National Institutes of Health, Bethesda, Maryland, United States, 2 Biophysics Program, University of Maryland, College Park, Marland, United States, 3 Applied Mathematics Program, Yale University, New Haven, Connecticut, United States, 4 Department of Mathematics, University of Maryland, College Park, Maryland, United States

The power of 2D relaxation spectrum NMR and MRI to characterize complex water dynamics (e.g., compartmental exchange) in biology and other disciplines has been demonstrated in recent years. However, the large amount of data and long MR acquisition times required for conventional 2D MR relaxometry limits its applicability for in vivo preclinical and clinical MRI. We present a new MR pipeline that incorporates compressed sensing (CS) as a means to vastly reduce the amount of relaxation data needed for material and tissue characterization without compromising data quality. This framework is validated using synthetic data, with NMR data acquired in a well-characterized urea-phantom, and on fixed porcine spinal cord tissue.

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