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Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical practice.
E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, “Pyramid methods in image processing,” RCA engineer , vol. 29, no. 6, pp. 33–41, 1984
1984
Earlier work this paper cites.
E. Mjolsness, C. Garrett, and W. L. Miranker, “Multiscale optimization in neural nets,” IEEE Transactions on Neural Networks , vol. 2, no. 2, pp. 263–274, 1991
1991
Earlier work this paper cites.
K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger, “Sense: sensitivity encoding for fast mri,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 42, no. 5, pp. 952–962, 1999
1999
Earlier work this paper cites.
M. A. Griswold, P. M. Jakob, R. M. Heidemann, M. Nittka, V. Jellus, J. Wang, B. Kiefer, and A. Haase, “Generalized autocalibrating partially parallel acquisitions (grappa),” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 47, no. 6, pp. 1202–1210, 2002
2002
Earlier work this paper cites.
M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing mri,” IEEE signal processing magazine , vol. 25, no. 2, pp. 72–82, 2008
2008
Earlier work this paper cites.
S. Ma, W. Yin, Y. Zhang, and A. Chakraborty, “An efficient algorithm for compressed mr imaging using total variation and wavelets,” in 2008 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2008, pp. 1–8
2008
Earlier work this paper cites.
Y. Wang, J. Yang, W. Yin, and Y. Zhang, “A new alternating minimization algorithm for total variation image reconstruction,” SIAM Journal on Imaging Sciences , vol. 1, no. 3, pp. 248–272, 2008
2008
Earlier work this paper cites.
S. Ramani and J. A. Fessler, “Parallel mr image reconstruction using augmented lagrangian methods,” IEEE Transactions on Medical Imaging , vol. 30, no. 3, pp. 694–706, 2010
2010
Earlier work this paper cites.
A. Chambolle and T. Pock, “A first-order primal-dual algorithm for convex problems with applications to imaging,” Journal of mathematical imaging and vision , vol. 40, no. 1, pp. 120–145, 2011
2011
Earlier work this paper cites.
T. Zhang, J. M. Pauly, S. S. Vasanawala, and M. Lustig, “Coil compression for accelerated imaging with cartesian sampling,” Magnetic resonance in medicine , vol. 69, no. 2, pp. 571–582, 2013
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
M. Uecker, F. Ong, J. I. Tamir, D. Bahri, P. Virtue, J. Y. Cheng, T. Zhang, and M. Lustig, “Berkeley advanced reconstruction toolbox,” in Proc. Intl. Soc. Mag. Reson. Med , vol. 23, no. 2486, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. De Freitas, “Learning to learn by gradient descent by gradient descent,” in Advances in neural information processing systems , 2016, pp. 3981–3989
2016
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” International Conference on Learning Representations (ICLR) , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. Sun, H. Li, Z. Xu et al. , “Deep admm-net for compressive sensing mri,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
G. Yang, S. Yu, H. Dong, G. Slabaugh, P. L. Dragotti, X. Ye, F. Liu, S. Arridge, J. Keegan, Y. Guo et al. , “Dagan: Deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1310–1321, 2017
2017
Earlier work this paper cites.
J. Schlemper, J. Caballero, J. V. Hajnal, A. Price, and D. Rueckert, “A deep cascade of convolutional neural networks for mr image reconstruction,” in International Conference on Information Processing in Medical Imaging . Springer, 2017, pp. 647–658
2017
Earlier work this paper cites.
K. Li and J. Malik, “Learning to optimize,” in International Conference on Learning Representations (ICLR) , 2017
2017
Earlier work this paper cites.
K. Li and J. Malik, “Learning to optimize neural nets,” arXiv preprint arXiv:1703.00441 , 2017
2017
Earlier work this paper cites.
K. Lv, S. Jiang, and J. Li, “Learning gradient descent: Better generalization and longer horizons,” in International Conference on Machine Learning . PMLR, 2017, pp. 2247–2255
2017
Earlier work this paper cites.
Y. Chen, M. W. Hoffman, S. G. Colmenarejo, M. Denil, T. P. Lillicrap, M. Botvinick, and N. Freitas, “Learning to learn without gradient descent by gradient descent,” in International Conference on Machine Learning . PMLR, 2017, pp. 748–756
2017
Earlier work this paper cites.
E. Khalil, H. Dai, Y. Zhang, B. Dilkina, and L. Song, “Learning combinatorial optimization algorithms over graphs,” Advances in Neural Information Processing Systems , vol. 30, pp. 6348–6358, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
Earlier work this paper cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2881–2890
2017
Earlier work this paper cites.
C. M. Hyun, H. P. Kim, S. M. Lee, S. Lee, and J. K. Seo, “Deep learning for undersampled mri reconstruction,” Physics in Medicine & Biology , vol. 63, no. 13, p. 135007, 2018
2018
Earlier work this paper cites.
M. Seitzer, G. Yang, J. Schlemper, O. Oktay, T. Würfl, V. Christlein, T. Wong, R. Mohiaddin, D. Firmin, J. Keegan et al. , “Adversarial and perceptual refinement for compressed sensing mri reconstruction,” in International conference on medical image computing and computer-assisted intervention . Springer, 2018, pp. 232–240
2018
Earlier work this paper cites.
M. Mardani, E. Gong, J. Y. Cheng, S. S. Vasanawala, G. Zaharchuk, L. Xing, and J. M. Pauly, “Deep generative adversarial neural networks for compressive sensing mri,” IEEE transactions on medical imaging , vol. 38, no. 1, pp. 167–179, 2018
2018
Earlier work this paper cites.
T. M. Quan, T. Nguyen-Duc, and W.-K. Jeong, “Compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1488–1497, 2018
2018
Earlier work this paper cites.
T. Eo, Y. Jun, T. Kim, J. Jang, H.-J. Lee, and D. Hwang, “Kiki-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images,” Magnetic resonance in medicine , vol. 80, no. 5, pp. 2188–2201, 2018
2018
Earlier work this paper cites.
D. Lee, J. Yoo, S. Tak, and J. C. Ye, “Deep residual learning for accelerated mri using magnitude and phase networks,” IEEE Transactions on Biomedical Engineering , vol. 65, no. 9, pp. 1985–1995, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
C. Finn and S. Levine, “Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm,” in International Conference on Learning Representations (ICLR) , 2018
2018
Earlier work this paper cites.
K. C. Tezcan, C. F. Baumgartner, R. Luechinger, K. P. Pruessmann, and E. Konukoglu, “Mr image reconstruction using deep density priors,” IEEE transactions on medical imaging , vol. 38, no. 7, pp. 1633–1642, 2018
2018
Earlier work this paper cites.
C. Bermudez, A. J. Plassard, L. T. Davis, A. T. Newton, S. M. Resnick, and B. A. Landman, “Learning implicit brain mri manifolds with deep learning,” in Medical Imaging 2018: Image Processing , vol. 10574. International Society for Optics and Photonics, 2018, p. 105741L
2018
Earlier work this paper cites.
B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning,” Nature , vol. 555, no. 7697, pp. 487–492, 2018
2018
Earlier work this paper cites.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 9446–9454
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Zhang and B. Ghanem, “Ista-net: Interpretable optimization-inspired deep network for image compressive sensing,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1828–1837
2018
Cited alongside, same era.
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,” Magnetic resonance in medicine , vol. 79, no. 6, pp. 3055–3071, 2018
2018
Cited alongside, same era.
H. K. Aggarwal, M. P. Mani, and M. Jacob, “Modl: Model-based deep learning architecture for inverse problems,” IEEE transactions on medical imaging , vol. 38, no. 2, pp. 394–405, 2018
2018
Cited alongside, same era.
C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal, and D. Rueckert, “Convolutional recurrent neural networks for dynamic mr image reconstruction,” IEEE transactions on medical imaging , vol. 38, no. 1, pp. 280–290, 2018
2018
Cited alongside, same era.
A. Sriram, J. Zbontar, T. Murrell, C. L. Zitnick, A. Defazio, and D. K. Sodickson, “Grappanet: Combining parallel imaging with deep learning for multi-coil mri reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 315–14 322
2020
Closest in time.
J. Zhang, Y. Gu, H. Tang, X. Wang, Y. Kong, Y. Chen, H. Shu, and J.-L. Coatrieux, “Compressed sensing mr image reconstruction via a deep frequency-division network,” Neurocomputing , vol. 384, pp. 346–355, 2020
2020
Closest in time.
2020
Closest in time.
S. U. Dar, M. Yurt, M. Shahdloo, M. E. Ildız, B. Tınaz, and T. Çukur, “Prior-guided image reconstruction for accelerated multi-contrast mri via generative adversarial networks,” IEEE Journal of Selected Topics in Signal Processing , vol. 14, no. 6, pp. 1072–1087, 2020
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2018
Cited alongside, same era.
J. C. Ye, “Compressed sensing mri: a review from signal processing perspective,” BMC Biomedical Engineering , vol. 1, no. 1, pp. 1–17, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. Abdal, Y. Qin, and P. Wonka, “Image2stylegan: How to embed images into the stylegan latent space?” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 4432–4441
2019
Cited alongside, same era.
A. Lucas, S. Lopez-Tapia, R. Molina, and A. K. Katsaggelos, “Efficient fine-tuning of neural networks for artifact removal in deep learning for inverse imaging problems,” in 2019 IEEE International Conference on Image Processing (ICIP) . IEEE, 2019, pp. 3591–3595
2019
Cited alongside, same era.
N. Rahaman, A. Baratin, D. Arpit, F. Draxler, M. Lin, F. Hamprecht, Y. Bengio, and A. Courville, “On the spectral bias of neural networks,” in International Conference on Machine Learning . PMLR, 2019, pp. 5301–5310
2019
Cited alongside, same era.
2019
Cited alongside, same era.
B. Ronen, D. Jacobs, Y. Kasten, and S. Kritchman, “The convergence rate of neural networks for learned functions of different frequencies,” Advances in Neural Information Processing Systems , vol. 32, pp. 4761–4771, 2019
2019
Cited alongside, same era.
2020
Closest in time.
F. Knoll, T. Murrell, A. Sriram, N. Yakubova, J. Zbontar, M. Rabbat, A. Defazio, M. J. Muckley, D. K. Sodickson, C. L. Zitnick et al. , “Advancing machine learning for mr image reconstruction with an open competition: Overview of the 2019 fastmri challenge,” Magnetic resonance in medicine , vol. 84, no. 6, pp. 3054–3070, 2020
2020
Closest in time.
X. Zhu, L. Zhang, L. Zhang, X. Liu, Y. Shen, and S. Zhao, “Gan-based image super-resolution with a novel quality loss,” Mathematical Problems in Engineering , vol. 2020, 2020
2020
Closest in time.
F. Knoll, K. Hammernik, C. Zhang, S. Moeller, T. Pock, D. K. Sodickson, and M. Akcakaya, “Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues,” IEEE signal processing magazine , vol. 37, no. 1, pp. 128–140, 2020
2020
Closest in time.
A. Sriram, J. Zbontar, T. Murrell, A. Defazio, C. L. Zitnick, N. Yakubova, F. Knoll, and P. Johnson, “End-to-end variational networks for accelerated mri reconstruction,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 64–73
2020
Closest in time.
N. Pezzotti, S. Yousefi, M. S. Elmahdy, J. H. F. Van Gemert, C. Schuelke, M. Doneva, T. Nielsen, S. Kastryulin, B. P. Lelieveldt, M. J. Van Osch et al. , “An adaptive intelligence algorithm for undersampled knee mri reconstruction,” IEEE Access , vol. 8, pp. 204 825–204 838, 2020
2020
Closest in time.
A. Pramanik, H. K. Aggarwal, and M. Jacob, “Deep generalization of structured low-rank algorithms (deep-slr),” IEEE Transactions on Medical Imaging , vol. 39, no. 12, pp. 4186–4197, 2020
2020
Closest in time.
S. Wang, H. Cheng, L. Ying, T. Xiao, Z. Ke, H. Zheng, and D. Liang, “Deepcomplexmri: Exploiting deep residual network for fast parallel mr imaging with complex convolution,” Magnetic Resonance Imaging , vol. 68, pp. 136–147, 2020
2020
Closest in time.
S. Bhadra, W. Zhou, and M. A. Anastasio, “Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks,” in Medical Imaging 2020: Physics of Medical Imaging , vol. 11312. International Society for Optics and Photonics, 2020, p. 113120V
2020
Closest in time.
G. Luo, N. Zhao, W. Jiang, E. S. Hui, and P. Cao, “Mri reconstruction using deep bayesian estimation,” Magnetic resonance in medicine , vol. 84, no. 4, pp. 2246–2261, 2020
2020
Closest in time.
Q. Liu, Q. Yang, H. Cheng, S. Wang, M. Zhang, and D. Liang, “Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors,” Magnetic resonance in medicine , vol. 83, no. 1, pp. 322–336, 2020
2020
Closest in time.
B. Wen, S. Ravishankar, L. Pfister, and Y. Bresler, “Transform learning for magnetic resonance image reconstruction: From model-based learning to building neural networks,” IEEE Signal Processing Magazine , vol. 37, no. 1, pp. 41–53, 2020
2020
Closest in time.
R. Souza, M. Bento, N. Nogovitsyn, K. J. Chung, W. Loos, R. M. Lebel, and R. Frayne, “Dual-domain cascade of u-nets for multi-channel magnetic resonance image reconstruction,” Magnetic resonance imaging , vol. 71, pp. 140–153, 2020
2020
Closest in time.
B. Zhou and S. K. Zhou, “Dudornet: Learning a dual-domain recurrent network for fast mri reconstruction with deep t1 prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4273–4282
2020
Closest in time.
M. Zhang, M. Li, J. Zhou, Y. Zhu, S. Wang, D. Liang, Y. Chen, and Q. Liu, “High-dimensional embedding network derived prior for compressive sensing mri reconstruction,” Medical Image Analysis , vol. 64, p. 101717, 2020
2020
Closest in time.
J. Liu, Y. Sun, C. Eldeniz, W. Gan, H. An, and U. S. Kamilov, “Rare: Image reconstruction using deep priors learned without groundtruth,” IEEE Journal of Selected Topics in Signal Processing , vol. 14, no. 6, pp. 1088–1099, 2020
2020
Closest in time.
S. Wang, Y. Chen, T. Xiao, Z. Ke, Q. Liu, and H. Zheng, “Lantern: Learn analysis transform network for dynamic magnetic resonance imaging,” Inverse Problems and Imaging , vol. 0, pp. –, 2020
2020
Closest in time.
S. U. H. Dar, M. Özbey, A. B. Çatlı, and T. Çukur, “A transfer-learning approach for accelerated mri using deep neural networks,” Magnetic resonance in medicine , vol. 84, no. 2, pp. 663–685, 2020
2020
Closest in time.
E. Z. Chen, T. Chen, and S. Sun, “Mri image reconstruction via learning optimization using neural odes,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 83–93
2020
Closest in time.
T. Nguyen, R. Baraniuk, A. Bertozzi, S. Osher, and B. Wang, “Momentumrnn: Integrating momentum into recurrent neural networks,” Advances in Neural Information Processing Systems , vol. 33, pp. 1924–1936, 2020
2020
Closest in time.
U. Nakarmi, J. Y. Cheng, E. P. Rios, M. Mardani, J. M. Pauly, L. Ying, and S. S. Vasanawala, “Multi-scale unrolled deep learning framework for accelerated magnetic resonance imaging,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2020, pp. 1056–1059
2020
Closest in time.
W. Tan, B. Wen, C. Chen, Z. Zeng, and X. Yang, “Systematic analysis of circular artifacts for stylegan,” in 2021 IEEE International Conference on Image Processing (ICIP) . IEEE, 2021, pp. 3902–3906
2021
Closest in time.
W. Chen, Y. Ma, X. Liu, and Y. Yuan, “Hierarchical generative adversarial networks for single image super-resolution,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 355–364
2021
Closest in time.
2021
Closest in time.
Y. Liu, Z. Yi, Y. Zhao, F. Chen, Y. Feng, H. Guo, A. T. Leong, and E. X. Wu, “Calibrationless parallel imaging reconstruction for multislice mr data using low-rank tensor completion,” Magnetic Resonance in Medicine , vol. 85, no. 2, pp. 897–911, 2021
2021
Closest in time.
M. Z. Darestani and R. Heckel, “Accelerated mri with un-trained neural networks,” IEEE Transactions on Computational Imaging , vol. 7, pp. 724–733, 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
J. Yoo, K. H. Jin, H. Gupta, J. Yerly, M. Stuber, and M. Unser, “Time-dependent deep image prior for dynamic mri,” IEEE Transactions on Medical Imaging , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Z. Ke, W. Huang, Z.-X. Cui, J. Cheng, S. Jia, H. Wang, X. Liu, H. Zheng, L. Ying, Y. Zhu et al. , “Learned low-rank priors in dynamic mr imaging,” IEEE Transactions on Medical Imaging , 2021
2021
Closest in time.
C. Oh, D. Kim, J.-Y. Chung, Y. Han, and H. Park, “A k-space-to-image reconstruction network for mri using recurrent neural network,” Medical Physics , vol. 48, no. 1, pp. 193–203, 2021
2021
Closest in time.
K. Hammernik, J. Schlemper, C. Qin, J. Duan, R. M. Summers, and D. Rueckert, “Systematic evaluation of iterative deep neural networks for fast parallel mri reconstruction with sensitivity-weighted coil combination,” Magnetic Resonance in Medicine , 2021
2021
Closest in time.
Y. Jun, H. Shin, T. Eo, and D. Hwang, “Joint deep model-based mr image and coil sensitivity reconstruction network (joint-icnet) for fast mri,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 5270–5279
2021
Closest in time.
T. M. Hospedales, A. Antoniou, P. Micaelli, and A. J. Storkey, “Meta-learning in neural networks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Closest in time.
M. J. Muckley, B. Riemenschneider, A. Radmanesh, S. Kim, G. Jeong, J. Ko, Y. Jun, H. Shin, D. Hwang, M. Mostapha et al. , “Results of the 2020 fastmri challenge for machine learning mr image reconstruction,” IEEE transactions on medical imaging , vol. 40, no. 9, pp. 2306–2317, 2021
2021
Closest in time.