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Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation.
H. Robbins, “An empirical Bayes approach to statistics,” in
1956
Earlier work this paper cites.
Y. Nesterov, “A method for solving the convex programming problem with convergence rate O(1/kˆ2),”
1983
Earlier work this paper cites.
A. Macovski, “Noise in MRI,”
1996
Earlier work this paper cites.
A. Chambolle, R. A. De Vore, N.-Y. Lee, and B. J. Lucier, “Nonlinear wavelet image processing: Variational problems, compression, and noise removal through wavelet shrinkage,”
1998
Earlier work this paper cites.
A. Hyvärinen, “Estimation of non-normalized statistical models by score matching,”
2005
Earlier work this paper cites.
M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,”
2007
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,”
2007
Earlier work this paper cites.
M. Buehrer, K. P. Pruessmann, P. Boesiger, and S. Kozerke, “Array compression for MRI with large coil arrays,”
2007
Earlier work this paper cites.
L. Ying and J. Sheng, “Joint image reconstruction and sensitivity estimation in SENSE (JSENSE),”
2007
Earlier work this paper cites.
S. Ramani, T. Blu, and M. Unser, “Monte-Carlo SURE: A black-box optimization of regularization parameters for general denoising algorithms,”
2008
Earlier work this paper cites.
A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,”
2009
Earlier work this paper cites.
A. Beck and M. Teboulle, “Gradient-based algorithms with applications to signal recovery,” in
2009
Earlier work this paper cites.
D. L. Donoho, A. Maleki, and A. Montanari, “Message passing algorithms for compressed sensing,”
2009
Earlier work this paper cites.
J. A. Fessler, “Model-based image reconstruction for MRI,”
2010
Earlier work this paper cites.
C. Bilen, I. W. Selesnick, Y. Wang, R. Otazo, D. Kim, L. Axel, and D. K. Sodickson, “On compressed sensing in parallel MRI of cardiac perfusion using temporal wavelet and TV regularization,” in
2010
Earlier work this paper cites.
E. Esser, X. Zhang, and T. F. Chan, “A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science,”
2010
Earlier work this paper cites.
P. Schniter, “Turbo reconstruction of structured sparse signals,” in
2010
Earlier work this paper cites.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,”
2011
Earlier work this paper cites.
Z. Shen, K.-C. Toh, and S. Yun, “An accelerated proximal gradient algorithm for frame-based image restoration via the balanced approach,”
2011
Earlier work this paper cites.
B. Efron, “Tweedie’s formula and selection bias,”
2011
Earlier work this paper cites.
P. L. Combettes and J.-C. Pesquet, “Proximal splitting methods in signal processing,” in
2011
Earlier work this paper cites.
K. Kunisch and T. Pock, “A bilevel optimization approach for parameter learning in variational models,”
2013
Earlier work this paper cites.
S. V. Venkatakrishnan, C. A. Bouman, and B. Wohlberg, “Plug-and-play priors for model based reconstruction,” in
2013
Earlier work this paper cites.
N. Parikh and S. Boyd, “Proximal algorithms,”
2013
Earlier work this paper cites.
M. Maggioni, V. Katkovnik, K. Egiazarian, and A. Foi, “Nonlocal transform-domain filter for volumetric data denoising and reconstruction,”
2013
Earlier work this paper cites.
P. J. Shin, P. E. Larson, M. A. Ohliger, M. Elad, J. M. Pauly, D. B. Vigneron, and M. Lustig, “Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion,”
2014
Earlier work this paper cites.
M. Uecker, P. Lai, M. J. Murphy, P. Virtue, M. Elad, J. M. Pauly, S. S. Vasanawala, and M. Lustig, “ESPIRiT—An eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA,”
2014
Earlier work this paper cites.
Z. Tan, Y. C. Eldar, A. Beck, and A. Nehorai, “Smoothing and decomposition for analysis sparse recovery,”
2014
Cited alongside, same era.
M. S. Hansen and P. Kellman, “Image reconstruction: An overview for clinicians,”
2015
Cited alongside, same era.
R. Ahmad and P. Schniter, “Iteratively reweighted
2015
Cited alongside, same era.
C. A. Metlzer, A. Maleki, and R. G. Baraniuk, “BM3D-AMP: A new image recovery algorithm based on BM3D denoising,” in
2015
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
2015
Cited alongside, same era.
R. Ahmad, H. Xue, S. Giri, Y. Ding, J. Craft, and O. P. Simonetti, “Variable density incoherent spatiotemporal acquisition (VISTA) for highly accelerated cardiac MRI,”
P. Schniter, S. Rangan, and A. K. Fletcher, “Plug-and-play image recovery using vector AMP,” presented at the Intl. Biomedical and Astronomical Signal Processing (BASP) Frontiers Workshop, Villars-sur-Ollon, Switzerland (available at
2017
Later among the works it cites.
A. M. Teodoro, J. M. Bioucas-Dias, and M. A. T. Figueiredo, “Scene-adapted plug-and-play algorithm with convergence guarantees,” in
2017
Later among the works it cites.
S. Ravishankar, B. E. Moore, R. R. Nadakuditi, and J. A. Fessler, “Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging,”
2017
Later among the works it cites.
B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning,”
2018
Later among the works it cites.
C. M. Hyun, H. P. Kim, S. M. Lee, S. Lee, and J. K. Seo, “Deep learning for undersampled MRI reconstruction,”
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2015
Cited alongside, same era.
R. Otazo, E. Candès, and D. K. Sodickson, “Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components,”
2015
Cited alongside, same era.
Y. Liu, Z. Zhan, J.-F. Cai, D. Guo, Z. Chen, and X. Qu, “Projected iterative soft-thresholding algorithm for tight frames in compressed sensing magnetic resonance imaging,”
2016
Cited alongside, same era.
S. Sreehari, S. V. Venkatakrishnan, B. Wohlberg, G. T. Buzzard, L. F. Drummy, J. P. Simmons, and C. A. Bouman, “Plug-and-play priors for bright field electron tomography and sparse interpolation,”
2016
Cited alongside, same era.
2016
Cited alongside, same era.
C. A. Metzler, A. Maleki, and R. G. Baraniuk, “From denoising to compressed sensing,”
2016
Cited alongside, same era.
L. Zdeborová and F. Krzakala, “Statistical physics of inference: Thresholds and algorithms,”
2016
Cited alongside, same era.
2018
Later among the works it cites.
H. K. Aggarwal, M. P. Mani, and M. Jacob, “Model based image reconstruction using deep learned priors (MODL),” in
2018
Later among the works it cites.
K. Hammernik, T. Klatzer, E. Kobler, M. P. Recht, D. K. Sodickson, T. Pock, and F. Knoll, “Learning a variational network for reconstruction of accelerated MRI data,”
2018
Later among the works it cites.
S. Lunz, O. Öktem, and C.-B. Schönlieb, “Adversarial regularizers in inverse problems,” in
2018
Later among the works it cites.
A. Dave, A. K. Vadathya, R. Subramanyam, R. Baburajan, and K. Mitra, “Solving inverse computational imaging problems using deep pixel-level prior,”
2018
Later among the works it cites.
E. M. Eksioglu and A. K. Tanc, “Denoising AMP for MRI reconstruction: BM3D-AMP-MRI,”
2018
Later among the works it cites.
2018
Later among the works it cites.
G. T. Buzzard, S. H. Chan, S. Sreehari, and C. A. Bouman, “Plug-and-play unplugged: Optimization-free reconstruction using consensus equilibrium,”
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Fessler, “Optimization methods for MR image reconstruction (long version),”
2019
Closest in time.
A. Hauptmann, S. Arridge, F. Lucka, V. Muthurangu, and J. A. Steeden, “Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning—Proof of concept in congenital heart disease,”
2019
Closest in time.
M. Akçakaya, S. Moeller, S. Weingärtner, and K. Uğurbil, “Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging,”
2019
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F. Knoll, K. Hammernik, E. Kobler, T. Pock, M. P. Recht, and D. K. Sodickson, “Assessment of the generalization of learned image reconstruction and the potential for transfer learning,”
2019
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2019
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S. V. Venkatakrishnan, “Code for plug-and-play-priors,”
2019
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2019
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Y. Sun, B. Wohlberg, and U. S. Kamilov, “An online plug-and-play algorithm for regularized image reconstruction,”
2019
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E. T. Reehorst and P. Schniter, “Regularization by denoising: Clarifications and new interpretations,”
2019
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S. Bigdeli and S. Süsstrunk, “Image denoising via MAP estimation using deep neural networks,” in
2019
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S. H. Chan, “Performance analysis of plug-and-play ADMM: A graph signal processing perspective,”
2019
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R. Berthier, A. Montanari, and P.-M. Nguyen, “State evolution for approximate message passing with non-separable functions,”
2019
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