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Abstract #2617

Residual Reordering for Motion Compensated Compressed Sensing Cardiac Perfusion MR Imaging

Huisu Yoon1, Ganesh Adluru2, Edward V.R. DiBella2, Jong Chul Ye1

1Department of Bio and Brain Engineering, KAIST, Daejeon, Korea; 2Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT, United States

k-t FOCUSS with motion estimation and compensation is a promising tool for highly accelerated dynamic MRI. One of the limitations of k-t FOCUSS with ME/MC is, however, that the motion residual signals are often contaminated with background noises, so the reconstruction of the residual signal using standard l1 sparsity constraint are often inefficient in capturing the physiological features and results in temporal blurring. In this work, we exploit the reordering algorithm to make the residual reconstruction more efficient. Using cardiac perfusion imaging, the spatio-temporal constrained reconstruction using re-ordering was found effective for reconstruction of the motion residual in k-t FOCUSS with ME/MC. By combining k-t FOCUSS ME/MC, the ordering based residual reconstruction may be a useful tool for compressed sensing MRI.