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Inverse problems for accelerated MRI typically incorporate domain-specific knowledge about the forward encoding operator in a regularized reconstruction framework.
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K. P. Pruessmann, M. Weiger, P. Börnert, and P. Boesiger, · 2001
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“Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities,”
B. P. Sutton, D. C. Noll, and J. A. Fessler, · 2003
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“Smooth minimization of non-smooth functions,”
Y. Nesterov, · 2005
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“Sparse MRI: The application of compressed sensing for rapid MR imaging,”
M. Lustig, D. Donoho, and J. Pauly, · 2007
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“Undersampled radial MRI with multiple coils. iterative image reconstruction using a total variation constraint,”
K. T. Block, M. Uecker, and J. Frahm, · 2007
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“On accelerated proximal gradient methods for convex-concave optimization,”
P. Tseng, · 2008
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“A fast iterative shrinkage-thresholding algorithm for linear inverse problems,”
A. Beck and M. Teboulle, · 2009
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“Proximal splitting methods in signal processing,”
P. L. Combettes and J.-C. Pesquet, · 2009
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“The split bregman method for L1-regularized problems,”
T. Goldstein and S. Osher, · 2009
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“Compressed sensing with wavelet domain dependencies for coronary MRI: a retrospective study,”
M. Akçakaya, S. Nam, et al., · 2010
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“Parallel MR image reconstruction using augmented lagrangian methods,”
S. Ramani and J. A. Fessler, · 2010
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F. Knoll, K. Bredies, T. Pock, and R. Stollberger, · 2011
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M. Akçakaya, T. A. Basha, et al., · 2011
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“NESTA: A fast and accurate first-order method for sparse recovery,”
S. Becker, J. Bobin, and E. J. Candès, · 2011
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“A first-order primal-dual algorithm for convex problems with applications to imaging,”
A. Chambolle and T. Pock, · 2011
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“Undersampled MRI reconstruction with patch-based directional wavelets,”
X. Qu, D. Guo, et al., · 2012
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“Gradient methods for minimizing composite functions,”
Y. Nesterov, · 2013
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“Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE,”
S. N. Sotiropoulos, S. Moeller, et al., · 2013
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“ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA,”
M. Uecker, P. Lai, et al., · 2014
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“Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator,”
X. Qu, Y. Hou, et al., · 2014
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“Adam: A method for stochastic optimization,”
D. P. Kingma and J. Ba, · 2014
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“A remark on accelerated block coordinate descent for computing the proximity operators of sum of convex functions,”
A. Chambolle and T. Pock, · 2015
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“Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction,”
Z. Zhan, J.-F. Cai, et al., · 2015
Cited alongside, same era.
“Accelerating magnetic resonance imaging via deep learning,”
S. Wang, Z. Su, et al., · 2016
Cited alongside, same era.
“Deep ADMM-Net for compressive sensing MRI,”
Y. Yang, J. Sun, H. Li, and Z. Xu, · 2016
Cited alongside, same era.
“Learning optimal nonlinearities for iterative thresholding algorithms,”
U. S. Kamilov and H. Mansour, · 2016
Cited alongside, same era.
“Iteration complexity analysis of block coordinate descent methods,”
M. Hong, X. Wang, M. Razaviyayn, and Z.-Q. Luo, · 2016
Cited alongside, same era.
“Deep residual learning for image recognition,”
K. He, X. Zhang, S. Ren, and J. Sun, · 2016
Cited alongside, same era.
“Convolutional recurrent neural networks for dynamic MR image reconstruction,”
C. Qin, J. Schlemper, et al., · 2018
Later among the works it cites.
“Learned Primal-Dual Reconstruction,”
J. Adler and O. Oktem, · 2018
Later among the works it cites.
“Learning-based image reconstruction via parallel proximal algorithm,”
E. Bostan, U. S. Kamilov, and L. Waller, · 2018
Later among the works it cites.
“Tradeoffs between convergence speed and reconstruction accuracy in inverse problems,”
R. Giryes, Y. C. Eldar, A. M. Bronstein, and G. Sapiro, · 2018
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“k-space deep learning for accelerated MRI,”
Y. Han, L. Sunwoo, and J. C. Ye, · 2019
Closest in time.
“Simba: Scalable inversion in optical tomography using deep denoising priors,”
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“A parallel MR imaging method using multilayer perceptron,”
K. Kwon, D. Kim, and H. Park, · 2017
Cited alongside, same era.
“Low-dose CT with a residual encoder-decoder convolutional neural network,”
H. Chen, Y. Zhang, et al., · 2017
Cited alongside, same era.
“A deep cascade of convolutional neural networks for dynamic MR image reconstruction,”
J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price, and D. Rueckert, · 2017
Cited alongside, same era.
M. Mardani, H. Monajemi, et al., · 2017
Cited alongside, same era.
“Densely connected convolutional networks,”
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, · 2017
Cited alongside, same era.
“Ntire 2017 challenge on single image super-resolution: Methods and results,”
R. Timofte, E. Agustsson, L. Van Gool, M.-H. Yang, and L. Zhang, · 2017
Cited alongside, same era.
Z. Wu, Y. Sun, et al., · 2019
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“Physics-based learned design: Optimized coded-illumination for quantitative phase imaging,”
M. Kellman, E. Bostan, N. Repina, and L. Waller, · 2019
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“VS-Net: Variable splitting network for accelerated parallel MRI reconstruction,”
J. Duan, J. Schlemper, et al., · 2019
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“Model learning: Primal dual networks for fast MR imaging,”
J. Cheng, H. Wang, L. Ying, and D. Liang, · 2019
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“Scan-specific robust artificial-neural-net-works for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging,”
M. Akçakaya, S. Moeller, S. Weingärtner, and K. Uğurbil, · 2019
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“sRAKI-RNN: accelerated MRI with scan-specific recurrent neural networks using densely connected blocks,”
S. A. H. Hosseini, C. Zhang, K. Uǧurbil, S. Moeller, and M. Akçakaya, · 2019
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“Gradient methods with memory,”
Y. Nesterov and M. Florea, · 2019
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“Assessment of the generalization of learned image reconstruction and the potential for transfer learning,”
F. Knoll, K. Hammernik, et al., · 2019
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“Unsupervised deep basis pursuit: Learning inverse problems without ground-truth data,”
J. I. Tamir, S. X. Yu, and M. Lustig, · 2019
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“Scene parsing via dense recurrent neural networks with attentional selection,”
H. Fan, P. Chu, L. J. Latecki, and H. Ling, · 2019
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“Self-supervised physics-based deep learning MRI reconstruction without fully-sampled data,”
B. Yaman, S. A. H. Hosseini, et al., · 2020
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“Self-supervised learning of physics-guided reconstruction neural networks without fully-sampled reference data,”
B. Yaman, S. A. H. Hosseini, et al., · 2020
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“Accelerated coronary mri with sraki: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling,”
S. A. H. Hosseini, C. Zhang, et al., · 2020
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“Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues,”
F. Knoll, K. Hammernik, et al., · 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,”
J. Zbontar, F. Knoll, et al., · 2020
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“Optimization methods for magnetic resonance image reconstruction: Key models and optimization algorithms,”
J. A. Fessler, · 2020
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“Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI,”
X. Zhang, D. Guo, et al., · 2020
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