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