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Abstract #4593

Compressed Sensing for Motion Artifact Reduction

Joelle Karine Barral1, Dwight George Nishimura1

1Electrical Engineering, Stanford University, Stanford, CA, USA

Navigators can effectively track rigid-body motion of limited amplitude. However, data associated with significant motion need to be discarded, which often results in unacceptable artifacts. We propose to use a pseudo-random trajectory and compressed sensing theory to reconstruct datasets where data corrupted by motion and detected by navigators have been rejected. When data are acquired with a pseudo-random trajectory, motion occurring over several TRs results in a randomly undersampled dataset that can be accurately reconstructed. Simulation and experimental results are presented.