  In this paper, we develop an efficient retrospective deep learning method
called stacked U-Nets with self-assisted priors to address the problem of rigid
motion artifacts in MRI. The proposed work exploits the usage of additional
knowledge priors from the corrupted images themselves without the need for
additional contrast data. The proposed network learns missed structural details
through sharing auxiliary information from the contiguous slices of the same
distorted subject. We further design a refinement stacked U-Nets that
facilitates preserving of the image spatial details and hence improves the
pixel-to-pixel dependency. To perform network training, simulation of MRI
motion artifacts is inevitable. We present an intensive analysis using various
types of image priors: the proposed self-assisted priors and priors from other
image contrast of the same subject. The experimental analysis proves the
effectiveness and feasibility of our self-assisted priors since it does not
require any further data scans.
