Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix

Abstract

In this paper, we propose a sparse and low-rank decomposition of annihilating filter-based Hankel matrix for MRI artifact removal. Based on the observation that some MR artifacts are originated from k-space outliers, we employ the 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. Unlike the recent sparse and low rank decomposition for dynamic MRI, 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 from MR images.