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Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours.
Communication in the presence of noise
C.E. Shannon · 1949
Earlier work this paper cites.
Probability and Statistics: The Harald Cramér Volume
U. Grenander, H. Cramér, and Karreman Mathematics Research Collection · 1959
Earlier work this paper cites.
On the use of the normalized mean square error in evaluating dispersion model performance
Attilio Poli and Mario Cirillo · 1993
Earlier work this paper cites.
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Earlier work this paper cites.
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Klaas P. Pruessmann, Markus Weiger, Markus B. Scheidegger, and Peter Boesiger · 1999
Earlier work this paper cites.
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Mark Griswold, Peter Jakob, Robin Heidemann, Mathias Nittka, Vladimir Jellus, Jianmin Wang, Berthold Kiefer, and Axel Haase · 2002
Earlier work this paper cites.
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Erik G. Larsson, Deniz Erdogmus, Rui Yan, Jose C. Principe, and Jeffrey R. Fitzsimmons · 2003
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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David J. Larkman and Rita Gouveia Nunes · 2007
Earlier work this paper cites.
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Earlier work this paper cites.
The ill-posed problem and regularization in parallel magnetic resonance imaging
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Earlier work this paper cites.
Spirit: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space
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Earlier work this paper cites.
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John N. Morelli, Val M. Runge, Fei Ai, Ulrike Attenberger, Lan Vu, Stuart H. Schmeets, Wolfgang R. Nitz, and John E. Kirsch · 2011
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Combination of compressed sensing and parallel imaging for highly-accelerated dynamic mri
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Iterative estimation of mri sensitivity maps and image based on sense reconstruction method (isense)
Angshul Majumdar and Rabab K. Ward · 2012
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Espirit—an eigenvalue approach to autocalibrating parallel mri: Where sense meets grappa
Martin Uecker, Peng Lai, M. J. Murphy, Patrick Virtue, Michael Elad, John M. Pauly, Shreyas S. Vasanawala, and Michael Lustig · 2014
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Results of the 2020 fastmri challenge for machine learning mr image reconstruction
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