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The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing).
Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays
Daniel K Sodickson and Warren J Manning · 1997
Earlier work this paper cites.
SENSE: sensitivity encoding for fast MRI
Klaas P Pruessmann, Markus Weiger, Markus B Scheidegger, and Peter Boesiger · 1999
Earlier work this paper cites.
Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Mark A. Griswold, Peter M. Jakob, Robin M. Heidemann, Mathias Nittka, Vladimir Jellus, Jianmin Wang, Berthold Kiefer, and Axel Haase · 2002
Earlier work this paper cites.
Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli · 2004
Earlier work this paper cites.
Compressive sampling
Emmanuel J Candès et al · 2006
Earlier work this paper cites.
Compressed sensing
David Donoho · 2006
Cited alongside, same era.
Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging
Michael Lustig, David Donoho, and John M Pauly · 2007
Cited alongside, same era.
ESPIRiT -an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA
Martin Uecker, Peng Lai, Mark J Murphy, Patrick Virtue, Michael Elad, John M Pauly, Shreyas S Vasanawala, and Michael Lustig · 2014
Cited alongside, same era.
U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Cited alongside, same era.
Learning a variational network for reconstruction of accelerated MRI data
Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P. Recht, Daniel K. Sodickson, Thomas Pock, and Florian Knoll · 2018
Cited alongside, same era.
A deep cascade of convolutional neural networks for dynamic MR image reconstruction
fastmri: An open dataset and benchmarks for accelerated MRI
Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, and Yvonne W. Lui · 2018
Later among the works it cites.
Deep learning methods for parallel magnetic resonance image reconstruction
Florian Knoll, Kerstin Hammernik, Chi Zhang, S. Möller, Thomas Pock, Daniel K. Sodickson, and Mehmet Akçakaya · 2019
Later among the works it cites.
Deep mri reconstruction: Unrolled optimization algorithms meet neural networks
Dong Liang, Jing Cheng, Ziwen Ke, and Leslie Ying · 2019
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Invert to learn to invert
Patrick Putzky and Max Welling · 2019
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Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony N. Price, and Daniel Rueckert · 2018
Cited alongside, same era.
Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan Caan, and Max Welling · 2019
Later among the works it cites.
Grappanet: Combining parallel imaging with deep learning for multi-coil mri reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell, C Lawrence Zitnick, Aaron Defazio, and Daniel K Sodickson · 2019
Later among the works it cites.