Compressed sensing MRI: a review of the clinical literature.

By Oren Jaspan, R. Fleysher, M. Lipton. British Journal of Radiology, 2015

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Scan-specific Robust Artificial-neural-networks for k-space Interpolation (RAKI) Reconstruction: Database-free Deep Learning for Fast Imaging (2018, methodology): “40,41 For these quantitative MRI data sets, the spatial variability of the T 1 times in the homogenous muscle tissue of the myocardium were used as a surrogate for noise performance 42 for both reconstruction methods.”
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018, background): “Although there are promising studies applying fast CS-MRI in clinical environments [31]–[33], most routine clinical MRI scanning is still based on standard fully-sampled Cartesian sequences or is accelerated only using parallel imaging.”