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In this paper, we develop an efficient retrospective deep learning methodcalled stacked U-Nets with self-assisted priors to address the problem of rigidmotion artifacts in MRI. The proposed work exploits the usage of additionalknowledge priors from the corrupted images themselves without the need foradditional contrast data. The proposed network learns missed structural detailsthrough sharing auxiliary information from the contiguous slices of the samedistorted subject. We further design a refinement stacked U-Nets thatfacilitates preserving of the image spatial details and hence improves thepixel-to-pixel dependency. To perform network training, simulation of MRImotion artifacts is inevitable. We present an intensive analysis using varioustypes of image priors: the proposed self-assisted priors and priors from otherimage contrast of the same subject. The experimental analysis proves theeffectiveness and feasibility of our self-assisted priors since it does notrequire any further data scans.

Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI