q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans

By Vladimir Golkov, Alexey Dosovitskiy, P. Sämann, J. Sperl, T. Sprenger, M. Czisch, M. Menzel, Pedro A. Gómez, Axel Haase, Thomas Brox and 1 more. 2015

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An overview of deep learning in medical imaging focusing on MRI (2018, background): “[186] in their proposed “q-space deep learning” family of methods.”
Current Applications and Future Impact of Machine Learning in Radiology. (2018, unknown): “Machine learning–based data processing methods have the potential to decrease imaging time (19).”
Activation Functions: Comparison of trends in Practice and Research for Deep Learning (2018, background): “These trends summarised in 1, outlining the remarkable architectural design improvements alongside the position of the respective AF’s in the architecture from AlexNet; winner of ILSVRC 2012 [28] and the modified networks like SqueezeNet [53], which had multiple AFs embedded in it.”
Learning a Variational Network for Reconstruction of Accelerated MRI Data (2017, methodology): “In medical imaging, deep learning has also been applied to areas like segmentation (7, 8), q-space image processing (9), and skull stripping (10).”