Simulation and Mitigation of the Wrap-Around Artifact in the MRI Image - Hu, Runze; Yang, Rui; Liu, Yutao; Li, Xiu (Frontiers in Computational Neuroscience, 2021)
Magnetic resonance imaging (MRI) is an essential clinical imaging modality for diagnosis and medical research. While various artifacts occur during the acquisition of MRI image, resulting in severe degradation of the perceptual quality and diagnostic efficacy. To tackle such challenges, this paper deals with one of the most frequent artifact sources, namely the wrap-around artifact. In particular, given that the MRI data is limited and difficult to access, we first propose a method to simulate the wrap-around artifact on the artifact-free MRI image to increase the quantity of MRI data. Then, an image restoration technique, based on the deep neural networks, is proposed for wrap-around artifact reduction and overall perceptual quality improvement. This paper presents a comprehensive analysis regarding both the occurrence and reduction of the wrap-around artifact, with the aim of facilitating the detection and mitigation of MRI artifacts in clinical situations.