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Abstract #0606

Compressed Sensing Reconstruction with an Additional Respiratory-Phase Dimension for Free-Breathing Imaging

Li Feng1, 2, Jing Liu3, Kai Tobias Block4, Jian Xu5, Leon Axel1, 2, Daniel K. Sodickson1, 2, Ricardo Otazo1, 2

1Center for Biomedical Imaging, New York University, School of Medicine, New York, United States; 2Sackler Institute of Graduate Biomedical Sciences, New York University, School of Medicine, New York, United States; 3Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States; 4Center for Biomedical Imaging, NYU Langone Medical Center, New York, United States; 5Siemens Medical Solutions, New York, United States

Respiratory motion reduces the temporal sparsity and thus the performance of compressed sensing reconstruction. In this work, we propose a respiratory motion compensation method for compressed sensing reconstruction using golden-angle radial sampling by creating an extra respiratory-phase dimension estimated from the acquired data with self-gating. The additional respiratory-phase dimension improves the performance of compressed sensing for free-breathing imaging due to (a) additional correlation and thus increased overall multidimensional sparsity and (b) higher incoherence, since the dimension is formed by sorting complementary golden-angle radial data. We demonstrate the feasibility of the technique for accelerated free breathing cardiac cine and liver imaging.