Amaresha Sridhar Konar1,
Steen Moeller2, Julianna Czum3, Barjor Gimi3,
Sairam Geethanath1
1Biomedical
Research Center, Dayananda Sagar Institutions, Bangalore, Karnataka, India; 2Center
for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN,
United States; 3Dept. of Radiology, Giesel School of Medicine at
Dartmouth, Lebanon, NH, United States
Compressed sensing (CS) performance depends significantly on sparsity of the image data.The current work aims at providing additional sparsity regardless of the transform chosen to achieve increased acceleration than the conventional CS approach, usinga novel technique called Region of Interest Compressed Sensing (ROICS). ROICS allows for enhanced sparsity by decreasing the number of non-zero coefficients to be estimated by restricting the CS reconstruction to a ROI. This work demonstrates that ROICS outperforms CS at higher acceleration factors, quantified through reduced normalized root mean square error, as applied to cardiac MRI frames.
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