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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