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.