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Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI).
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“Joint image reconstruction and sensitivity estimation in SENSE (JSENSE),”
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“Sparse MRI: The application of compressed sensing for rapid MR imaging,”
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“Compressed Sensing MRI,”
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Cited alongside, same era.
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M. Lustig and J. M. Pauly, · 2010
Cited alongside, same era.
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K. Gregor and Y. LeCun, · 2010
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S. Boyd, N. Parikh, B. P. E Chu, and J. Eckstein, · 2011
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F. Knoll, K. Bredies, T. Pock, and R. Stollberger, · 2011
Cited alongside, same era.
“Low-dimensional-structure self-learning and thresholding: Regularization beyond compressed sensing for MRI reconstruction,”
“Challenges and open problems in signal processing: Panel discussion summary from icassp 2017 [panel and forum],”
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“Least Squares Generative Adversarial Networks,”
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M. Akcakaya, T. A. Basha, B. Goddu, et al., · 2011
Cited alongside, same era.
“Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR,”
S. G. Lingala, Y. Hu, E. DiBella, and M. Jacob, · 2011
Cited alongside, same era.
“Parallel imaging with nonlinear reconstruction using variational penalties,”
F. Knoll, C. Clason, K. Bredies, M. Uecker, and R. Stollberger, · 2012
Cited alongside, same era.
“Nonlinear GRAPPA: A kernel approach to parallel MRI reconstruction,”
Y. Chang, D. Liang, and L. Ying, · 2012
Cited alongside, same era.
“Efficient BackProp,”
Y. A. LeCun, L. Bottou, G. B. Orr, and K. R. Müller, · 2012
Cited alongside, same era.
“Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project,”
K. Ugurbil, J. Xu, E. J. Auerbach, et al., · 2013
Cited alongside, same era.
“ESPIRiT – An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA,”
M. Uecker, P. Lai, M. J. Murphy, et al., · 2014
Cited alongside, same era.
O. Shitrit and T. Riklin Raviv, · 2017
Later among the works it cites.
“Learning a variational network for reconstruction of accelerated MRI data,”
K. Hammernik, T. Klatzer, E. Kobler, et al., · 2018
Later among the works it cites.
“Fast gpu implementation of a scan-specific deep learning reconstruction for accelerated magnetic resonance imaging,”
C. Zhang, S. Weingärtner, S. Moeller, K. Uğurbil, and M. Akçakaya, · 2018
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“Accelerated simultaneous multi-slice mri using subject-specific convolutional neural networks,”
C. Zhang, S. Moeller, S. Weingärtner, K. Uğurbil, and M. Akçakaya, · 2018
Later among the works it cites.
“DeepSPIRiT: Generalized parallel imaging using deep convolutional neural networks,”
J. Y. Cheng, M. Mardani, M. T. Alley, J. M. Pauly, and S. S. Vasanawala, · 2018
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“k-space deep learning for accelerated MRI,”
Y. Han and J. C. Ye, · 2018
Later among the works it cites.
“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, · 2018
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“Image reconstruction by domain-transform manifold learning,”
B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, · 2018
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“Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks,”
F. Chen, V. Taviani, I. Malkiel, et al., · 2018
Later among the works it cites.
“Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss,”
T. M. Quan, T. Nguyen-Duc, and W.-K. Jeong, · 2018
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“DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction,”
G. Yang, S. Yu, H. Dong, et al., · 2018
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“Variational Adversarial Networks for Accelerated MR Image Reconstruction,”
K. Hammernik, E. Kobler, T. Pock, et al., · 2018
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“Improving resolution of MR images with an adversarial network incorporating images with different contrast,”
K. H. Kim, W. J. Do, and S. H. Park, · 2018
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“Deep Generative Adversarial Neural Networks for Compressive Sensing (GANCS) MRI,”
M. Mardani, E. Gong, J. Y. Cheng, et al., · 2018
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“fastMRI: An open dataset and benchmarks for accelerated MRI,”
J. Zbontar, F. Knoll, A. Sriram, et al., · 2018
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“MoDL: Model-Based Deep Learning Architecture for Inverse Problems,”
H. K. Aggarwal, M. P. Mani, and M. Jacob, · 2019
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“Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging,”
M. Akcakaya, S. Moeller, S. Weingartner, and K. Ugurbil, · 2019
Closest in time.
“Accelerated coronary MRI using 3D SPIRiT-RAKI with sparsity regularization,”
S. A. H. Hosseini, S. Moeller, S. Weingärtner, K. Ugurbil, and M. Akçakaya, · 2019
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“Accelerated MRI using residual RAKI: Scan-specific learning of reconstruction artifacts,”
C. Zhang, S. Moeller, S. Weingärtner, K. U g ˇ \check{\textrm{g}} urbil, and M. Akçakaya, · 2019
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“Assessment of the generalization of learned image reconstruction and the potential for transfer learning,”
F. Knoll, K. Hammernik, E. Kobler, et al., · 2019
Closest in time.
“Convolutional recurrent neural networks for dynamic MR image reconstruction,”
C. Qin, J. Schlemper, J. Caballero, et al., · 2019
Closest in time.