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Abstract #4661

Non-Cartesian k-space Deep Learning for Accelerated MRI

Yoseob Han1 and Jong Chul Ye1

1Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, Republic of

The annihilating filter-based low-rank Hankel matrix approach (ALOHA) [1] is one of the most recent compressed sensing (CS) approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion. Inspired by the recent low-rank Hankel matrix decomposition using data-driven framelet basis [2], we propose a completely data-driven deep learning algorithm for k-space interpolation. In particular, our method can be applied directly by simply adding an additional re-gridding layer to non-Cartesian k-space trajectories such as radial trajectories.

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