Abstract #4300

MR artifacts removal using sparse + low rank decomposition of annihilating filter based Hankel matrix

Kyong Hwan Jin¹, Dongwook Lee¹, Paul Kyu Han¹, Juyoung Lee¹, Sung-Hong Park¹, and Jong Chul Ye¹

¹Dept. of Bio and Brain Engineering, KAIST, Daejeon, Korea, Republic of

In this paper, we propose a sparse and low-rank decomposition of annihilating filter-based Hankel matrix for removing MR artifacts such as motion, RF noises, or herringbone artifacts. Based on the observation that some MR artifacts are originated from k-space outliers, we employ a recently proposed image modeling method using annihilating filter-based low-rank Hankel matrix approach (ALOHA) to decompose the sparse outliers from the low-rank component. The proposed approach can be applied even for static images, because the k-space low rank component comes from the intrinsic image properties. We demonstrate that the proposed algorithm clearly removes several types of artifacts such as impulse noises, motion artifacts, and herringbone artifacts.

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