In this educational, we give an overview of the current developments in deep learning-based MRI reconstruction of undersampled k-space data. We show the advantages of deep learning-based approaches over compressed sensing approaches in terms of improved image quality and suppressed artifacts. We will also discuss several challenges that are encountered during learning covering the design of a training database, deep network architectures and image quality measures.
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