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Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it accuracy} for {\it speed} in real-time imaging.
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“Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,”
A. Radford, L. Metz, and S. Chintala · 2016
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“Loss Functions for Image Restoration with Neural Networks,”
H. Zhao, O. Gallo, I. Frosio, and J. Kautz, · 2017
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J. Johnson, A. Alahi, and F-F Li · 2016
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“Identity mappings in deep residual networks,”
K. He, X. Zhang, S. Ren, and J. Sun, · 2016
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[online] https://github.com/gongenhao/GANCS.html
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R. Yeh, C. Chen, T. Y. Lim, M. Hasegawa-Johnson, M. N. Do,
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H. Chen, Y. Zhang, M. K. Kalra, F. Lin, P. Liao, J. Zhou, and G. Wang, · 2017
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