Hybrid Method for Gibbs-Ringing Artifact Suppression in Magnetic Resonance Images

Suppression of ringing artifacts in images is a well-known image restoration problem. Gibbs-ringing artifacts occur when, in the process of magnetic resonance imaging, the source data from the frequency domain are mapped onto the spatial domain by using the discrete Fourier transform. The artifacts are caused by the incompleteness of these data, which, in turn, is due to cutting off the high frequencies of the Fourier spectrum. In this paper, we propose a hybrid method for Gibbs-ringing artifact suppression in magnetic resonance images that combines a deep learning model and a classical non-machine-learning algorithm for Gibbs-ringing artifact suppression based on optimal subvoxel shifts.