In this work we investigate the effect of motion on the data consistency error coil-mixing matrix, obtained by singular value decomposition. More specifically, a Neural Network is trained to translate motion induced deviations of this coil-mixing matrix relative to a reference acquisition into a motion score. This score can be used for the prospective detection of the most corrupted echo trains for removal or triggering a replacement by reacquisition. We show that a selective removal/replacement using the prospective motion score increases the image quality.
This abstract and the presentation materials are available to members only; a login is required.