Keywords: Motion Correction, ArtifactsPatient movement during MRI scan can cause severe degradation of image quality. In Susceptibility-Weighted Imaging (SWI), several echoes are measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the higher echoes are more susceptible to artifacts and signal dropout. This paper proposes a data-driven retrospective deep learning method by taking the advantage of interactively learning multiple echoes together through sharing their knowledge using unified training parameters. The proposed method allows to share information and gain an understanding of the correlations between multiple echoes towards generating high-resolution susceptibility enhanced contrast images.
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