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We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction.
1910
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, November 1999
1999
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P. Kellman, F. H. Epstein, and E. R. McVeigh, “Adaptive sensitivity encoding incorporating temporal filtering (TSENSE),” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 45, no. 5, pp. 846–852, October 2001
2001
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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, June 2002
2002
Earlier work this paper cites.
J. Tsao, P. Boesiger, and K. P. Pruessmann, “k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 50, no. 5, pp. 1031–1042, November 2003
2003
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A. C. Larson, R. D. White, G. Laub, E. R. McVeigh, D. Li, and O. P. Simonetti, “Self-gated cardiac cine MRI,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 51, no. 1, pp. 93–102, May 2004
2004
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F. A. Breuer, P. Kellman, M. A. Griswold, and P. M. Jakob, “Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA),” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 53, no. 4, pp. 981–985, March 2005
2005
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F. Huang, J. Akao, S. Vijayakumar, G. R. Duensing, and M. Limkeman, “k-t GRAPPA: A k-space implementation for dynamic MRI with high reduction factor,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 54, no. 5, pp. 1172–1184, November 2005
2005
Earlier work this paper cites.
D. Xu, K. F. King, and Z.-P. Liang, “Improving k-t SENSE by adaptive regularization,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 57, no. 5, pp. 918–930, May 2007
2007
Earlier work this paper cites.
H. Jung, J. C. Ye, and E. Y. Kim, “Improved k–t BLAST and k–t SENSE using FOCUSS,” Physics in Medicine & Biology , vol. 52, no. 11, p. 3201, May 2007
2007
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U. Gamper, P. Boesiger, and S. Kozerke, “Compressed sensing in dynamic MRI,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 59, no. 2, pp. 365–373, February 2008
2008
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J. Ji and T. Lang, “Dynamic MRI with compressed sensing imaging using temporal correlations,” 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro , pp. 1613–1616, May 14-17, 2008
2008
Earlier work this paper cites.
H. Jung, K. Sung, K. Nayak, E. Kim, and J. Ye, “k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 61, no. 1, pp. 103–116, January 2009
2009
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R. Otazo, D. Kim, L. Axel, and D. K. Sodickson, “Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion mri,” Magnetic Resonance in Medicine , vol. 64, no. 3, pp. 767–776, September 2010
2010
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S. Lingala, Y. Hu, E. DiBella, and M. Jacob, “Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR,” IEEE Transactions on Medical Imaging , vol. 30, no. 5, pp. 1042–1054, May 2011
2011
Earlier work this paper cites.
Y. Wang and L. Ying, “Compressed sensing dynamic cardiac cine mri using learned spatiotemporal dictionary,” IEEE transactions on Biomedical Engineering , vol. 61, no. 4, pp. 1109–1120, 2013
2013
Earlier work this paper cites.
L. Feng, M. B. Srichai, R. P. Lim, A. Harrison, W. King, G. Adluru, E. V. Dibella, D. K. Sodickson, R. Otazo, and D. Kim, “Highly accelerated real-time cardiac cine MRI using k–t SPARSE-SENSE,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 70, no. 1, pp. 64–74, 2013
2013
Earlier work this paper cites.
L. Feng, R. Grimm, K. T. Block, H. Chandarana, S. Kim, J. Xu, L. Axel, D. K. Sodickson, and R. Otazo, “Golden-angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 72, no. 3, pp. 707–717, September 2014
2014
Cited alongside, same era.
S. Poddar and M. Jacob, “Dynamic MRI using smoothness regularization on manifolds (SToRM),” IEEE Transactions on Medical Imaging , vol. 35, no. 4, pp. 1106–1115, April 2015
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,” Magnetic Resonance in Medicine , vol. 73, no. 3, pp. 1125–1136, April 2015
2015
Cited alongside, same era.
J. Yoo, A. Wahab, and J. C. Ye, “A mathematical framework for deep learning in elastic source imaging,” SIAM Journal on Applied Mathematics , vol. 78, no. 5, pp. 2791–2818, October 2018
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,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 79, no. 6, pp. 3055–3071, June 2018
2018
Later among the works it cites.
Y. Han, J. Yoo, H. Kim, H. Shin, K. Sung, and J. Ye, “Deep learning with domain adaptation for accelerated projection-reconstruction MR,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 80, no. 3, pp. 1189–1205, February 2018
2018
Later among the works it cites.
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2015
Cited alongside, same era.
L. Feng, L. Axel, H. Chandarana, K. Block, D. Sodickson, and R. Otazo, “XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 75, no. 2, pp. 775–788, February 2016
2016
Cited alongside, same era.
J. Yerly, G. Ginami, G. Nordio, A. J. Coristine, S. Coppo, P. Monney, and M. Stuber, “Coronary endothelial function assessment using self-gated cardiac cine MRI and k-t sparse SENSE,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 76, no. 5, pp. 1443–1454, November 2016
2016
Cited alongside, same era.
Y. Yang, J. Sun, H. Li, and Z. Xu, “Deep ADMM-Net for compressive sensing MRI,” Advances in Neural Information Processing Systems , pp. 10–18, December 5-10, 2016
2016
Cited alongside, same era.
S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” Proceedings of the Thirteenth IEEE International Symposium on Biomedical Imaging: From Nano to Macro , pp. 514–517, April 13-16, 2016
2016
Cited alongside, same era.
U. Nakarmi, W. Y., J. Lyu, D. Liang, and L. Ying, “A kernel-based low-rank (KLR) model for low-dimensional manifold recovery in highly accelerated dynamic MRI,” IEEE Transactions on Medical Imaging , vol. 36, no. 11, pp. 2297–2307, November 2017
2017
Cited alongside, same era.
U. Nakarmi, K. Slavakis, J. Lyu, and L. Ying, “M-MRI: A manifold-based framework to highly accelerated dynamic magnetic resonance imaging,” 2017 IEEE 14th International Symposium on Biomedical Imaging , pp. 19–22, April 18-21 2017
2017
Cited alongside, same era.
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,” IEEE Transactions on Medical Imaging , vol. 36, no. 5, pp. 1116–1128, January 2017
2017
Cited alongside, same era.
J. Chaptinel, J. Yerly, Y. Mivelaz, M. Prsa, L. Alamo, Y. Vial, G. Berchier, C. Rohner, F. Gudinchet, and M. Stuber, “Fetal cardiac cine magnetic resonance imaging in utero
2017
Cited alongside, same era.
2018
Later among the works it cites.
V. Lempitsky, A. Vedaldi, and D. Ulyanov, “Deep image prior,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 9446–9454, July 18-23, 2018
2018
Later among the works it cites.
J. Yoo, S. Sabir, D. Heo, K. H. Kim, A. Wahab, Y. Choi, S.-I. Lee, E. Y. Chae, H. H. Kim, Y. M. Bae, Y.-W. Choi, and S. Cho, “Deep learning diffuse optical tomography,” IEEE Transactions on Medical Imaging , vol. 39, no. 4, pp. 877–887, August 2019
2019
Closest in time.
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, July 2019
2019
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A. Hauptmann, S. Arridge, F. Lucka, V. Muthurangu, and J. Steeden, “Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning—Proof of concept in congenital heart disease,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 81, no. 2, pp. 1143–1156, February 2019
2019
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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, January 2019
2019
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S. Biswas, H. Aggarwal, and M. Jacob, “Dynamic MRI using model-based deep learning and SToRM priors: MoDL-SToRM,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 82, no. 1, pp. 485–494, July 2019
2019
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T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 4401–4410, June 16-20, 2019
2019
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A. Yazdanpanah, O. Afacan, and S. Warfield, “Non-learning based deep parallel MRI reconstruction (NLDpMRI),” Medical Imaging 2019: Image Processing , vol. 10949, pp. 1 094 904–1 094 910, February 16-21, 2019
2019
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K. Gong, C. Catana, J. Qi, and Q. Li, “PET image reconstruction using deep image prior,” IEEE Transactions on Medical Imaging , vol. 38, no. 7, pp. 1655–1665, July 2019
2019
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2019
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L. Di Sopra, D. Piccini, S. Coppo, M. Stuber, and J. Yerly, “An automated approach to fully self-gated free-running cardiac and respiratory motion-resolved 5D whole-heart MRI,” Magnetic Resonance in Medicine , vol. 82, no. 6, pp. 2118–2132, July 2019
2019
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Y. Choi, Y. Uh, J. Yoo, and J.-W. Ha, “Stargan v2: Diverse image synthesis for multiple domains,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 8188–8197, June 14-19, 2020
2020
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