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Abstract #4200

Combination of Compressed Sensing and Parallel Imaging with Adaptive Motion Compensation for Accelerated Dynamic MRI

Cagdas Bilen1, Ricardo Otazo2, Daniel K. Sodickson2, Ivan Selesnick1, Yao Wang1

1Department of Electrical Engineering, Polytechnic Institute of NYU, Brooklyn, NY, United States; 2Bernard and Irene Schwartz Center for Biomedical Imaging, NYU School of Medicine, New York, NY, United States

Video coding techniques such as motion compensation has been proposed to exploit temporal redundancy and improve compressed sensing reconstructions of undersampled dynamic MRI data. Many of these methods require reference frames and/or fully sampled low pass k-space data which limits the acceleration factor. We propose a regularization framework with motion compensating prior that adaptively estimates the motion field during the reconstruction iterations with no need for reference frames or fully sampled k-space data.

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Keywords

acceleration adaptive among approaches augmented avoiding better blurring body borrows cine clarity coding coil compensation compressed correlations criteria define deform deformable demon dependence domain dynamic efficient enables engineering especially estimating estimation except expense experimenting explicit exploit field fold frame frames fully function future helps hods includes index indicate iteration iterations iterative joined jointly keeps limited locations makes making manner mates matrix measured might minimization missing motion need note optimization parallel parts pass pixel pixels polytechnic power practice prior priors problems processing proposed quality random reconstructed reconstruction sampled scenarios school sensing sign smooth solving space sparse spatial speed steps temp temporal transform trio types useful utilizing variation version video wavelets