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Abstract #4589

Incorporating Self-Referenced Information Into Compressed Sensing in Dynamic Imaging

Tzu-Cheng Chao1,2, Bruno Madore2, Ming-Long Wu3, Jing Yuan2, Hsiao-Wen Chung1

1Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan; 2Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; 3Brain Imaging and Analysis Center, Duke University, Durham, NC, USA

Compressed Sensing is a newly developed fast imaging method aimed at robustly recovering the signal from undersampled datasets. In this work, we propose a modified sampling scheme to facilitate the reconstruction algorithm, based on an Orthogonal Matching Pursuit, for dynamic imaging purposes. Cardiac cine and fMRI data were used to test the approach and evaluate performance. The proposed modifications enabled significant reductions in computation time (about a 1.4 to 2 fold reduction).