Deep-learning (DL) can be used to extend compressed sensing (CS) to learn the regularization function in a data-driven manner. In contrast, super resolution (SR) algorithms have been used to transform rapidly-acquired low-resolution images into higher-resolution images. This work compares DL-CS with DL-SR for accelerated MRI on a test dataset of 50 patients with conventional image quality metrics and clinically-relevant quantitative T2 relaxation measurements. We demonstrate that DLCS approaches outperform DLSR approaches for accelerated MRI.
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