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The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.
Diffusions hypercontractives
Dominique Bakry and Michel Émery · 1985
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Simultaneous acquisition of spatial harmonics (smash): fast imaging with radiofrequency coil arrays
Daniel K Sodickson and Warren J Manning · 1997
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Sense: sensitivity encoding for fast mri
Klaas P Pruessmann, Markus Weiger, Markus B Scheidegger, and Peter Boesiger · 1999
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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
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David L Donoho · 2006
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Zhou Wang and Alan C Bovik · 2006
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Sparse mri: The application of compressed sensing for rapid mr imaging
Michael Lustig, David Donoho, and John M Pauly · 2007
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The restricted isometry property and its implications for compressed sensing
Emmanuel J Candes · 2008
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The ∞ \infty -Wasserstein distance: Local solutions and existence of optimal transport maps
Thierry Champion, Luigi De Pascale, and Petri Juutinen · 2008
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Random projections for manifold learning
Chinmay Hegde, Michael Wakin, and Richard G Baraniuk · 2008
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A software channel compression technique for faster reconstruction with many channels
Feng Huang, Sathya Vijayakumar, Yu Li, Sarah Hertel, and George R Duensing · 2008
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Optimal transport: old and new
Cédric Villani · 2008
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Random projections of smooth manifolds
Richard G Baraniuk and Michael B Wakin · 2009
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Simultaneous analysis of lasso and dantzig selector
Peter J Bickel, Ya’acov Ritov, and Alexandre B Tsybakov · 2009
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Robust recovery of signals from a structured union of subspaces
Yonina C Eldar and Moshe Mishali · 2009
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Model-based compressive sensing
Richard G Baraniuk, Volkan Cevher, Marco F Duarte, and Chinmay Hegde · 2010
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Improved pediatric mr imaging with compressed sensing
Shreyas S. Vasanawala, Marcus T. Alley, Brian A. Hargreaves, Richard A. Barth, John M. Pauly, and Michael Lustig · 2010
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Realistic analytical phantoms for parallel magnetic resonance imaging
Matthieu Guerquin-Kern, Laurent Lejeune, Klaas Paul Pruessmann, and Michael Unser · 2011
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Mr image reconstruction from highly undersampled k-space data by dictionary learning
Saiprasad Ravishankar and Yoram Bresler · 2011
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Parallel mr imaging
Anagha Deshmane, Vikas Gulani, Mark A Griswold, and Nicole Seiberlich · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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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
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Berkeley advanced reconstruction toolbox
Martin Uecker, Frank Ong, Jonathan I Tamir, Dara Bahri, Patrick Virtue, Joseph Y Cheng, Tao Zhang, and Michael Lustig · 2015
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
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Global guarantees for enforcing deep generative priors by empirical risk
Paul Hand and Vladislav Voroninski · 2017
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Deep convolutional neural network for inverse problems in imaging
Kyong Hwan Jin, Michael T. McCann, Emmanuel Froustey, and Michael Unser · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid · 2017
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Deep generative adversarial networks for compressed sensing automates mri
Morteza Mardani, Enhao Gong, Joseph Y Cheng, Shreyas Vasanawala, Greg Zaharchuk, Marcus Alley, Neil Thakur, Song Han, William Dally, John M Pauly, et al · 2017
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Data-driven models and approaches for imaging
Saiprasad Ravishankar and Jeffrey A. Fessler · 2017
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One network to solve them all–solving linear inverse problems using deep projection models
JH Rick Chang, Chun-Liang Li, Barnabas Poczos, BVK Vijaya Kumar, and Aswin C Sankaranarayanan · 2017
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A deep cascade of convolutional neural networks for dynamic mr image reconstruction
Jo Schlemper, Jose Caballero, Joseph V Hajnal, Anthony N Price, and Daniel Rueckert · 2017
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Modl: Model-based deep learning architecture for inverse problems
Hemant K Aggarwal, Merry P Mani, and Mathews Jacob · 2018
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Solving bilinear inverse problems using deep generative priors
Muhammad Asim, Fahad Shamshad, and Ali Ahmed · 2018
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On instabilities of deep learning in image reconstruction and the potential costs of ai
Vegard Antun, Francesco Renna, Clarice Poon, Ben Adcock, and Anders C Hansen · 2020
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High dimensional channel estimation using deep generative networks
Eren Balevi, Akash Doshi, Ajil Jalal, Alexandros Dimakis, and Jeffrey G Andrews · 2020
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Mathematical models for magnetic resonance imaging reconstruction: An overview of the approaches, problems, and future research areas
Mariya Doneva · 2020
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Reinhard Heckel and Mahdi Soltanolkotabi · 2020
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Image-adaptive gan based reconstruction
Shady Abu Hussein, Tom Tirer, and Raja Giryes · 2020
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Ambientgan: Generative models from lossy measurements
Ashish Bora, Eric Price, and Alexandros G Dimakis · 2018
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Modeling sparse deviations for compressed sensing using generative models
Manik Dhar, Aditya Grover, and Stefano Ermon · 2018
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Plug-in estimation in high-dimensional linear inverse problems: A rigorous analysis
Alyson K Fletcher, Parthe Pandit, Sundeep Rangan, Subrata Sarkar, and Philip Schniter · 2018
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Inference in deep networks in high dimensions
Alyson K Fletcher, Sundeep Rangan, and Philip Schniter · 2018
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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
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Phase retrieval under a generative prior
Paul Hand, Oscar Leong, and Vlad Voroninski · 2018
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Deep decoder: Concise image representations from untrained non-convolutional networks
Reinhard Heckel and Paul Hand · 2018
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Sure-based automatic parameter selection for espirit calibration
Siddharth Iyer, Frank Ong, Kawin Setsompop, Mariya Doneva, and Michael Lustig · 2020
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Computational mri: Compressive sensing and beyond [from the guest editors]
Mathews Jacob, Jong Chul Ye, Leslie Ying, and Mariya Doneva · 2020
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Robust compressed sensing using generative models
Ajil Jalal, Liu Liu, Alexandros G Dimakis, and Constantine Caramanis · 2020
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fastmri: A publicly available raw k-space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning
Florian Knoll, Jure Zbontar, Anuroop Sriram, Matthew J Muckley, Mary Bruno, Aaron Defazio, Marc Parente, Krzysztof J Geras, Joe Katsnelson, Hersh Chandarana, et al · 2020
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Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
Agostina J Larrazabal, Nicolás Nieto, Victoria Peterson, Diego H Milone, and Enzo Ferrante · 2020
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Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, and Jonathan Scarlett · 2020
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Deep learning techniques for inverse problems in imaging
Gregory Ongie, Ajil Jalal, Christopher A Metzler, Richard G Baraniuk, Alexandros G Dimakis, and Rebecca Willett · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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End-to-end variational networks for accelerated mri reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, and Patricia Johnson · 2020
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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 · 2020
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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 · 2020
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Transform learning for magnetic resonance image reconstruction: From model-based learning to building neural networks
Bihan Wen, Saiprasad Ravishankar, Luke Pfister, and Yoram Bresler · 2020
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Fast unsupervised mri reconstruction without fully-sampled ground truth data using generative adversarial networks
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