The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MRI images from undersampled data using a deep cascade of convolutional neural networks. We show, for Cartesian undersampling of 2D cardiac MR images, the proposed deep learning reconstruction method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, both in terms of reconstruction error, the perceptual quality and the reconstruction speed for 4-fold and 8-fold undersampling.
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