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Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but often necessitates a database of fully-sampled measurements for training.
SENSE: Sensitivity encoding for fast MRI
Klaas P. Pruessmann, Markus Weiger, Markus B. Scheidegger, and Peter Boesiger · 1999
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Advances in sensitivity encoding with arbitrary k-space trajectories
Klaas P. Pruessmann, Markus Weiger, Peter Bornert, and Peter Boesiger · 2001
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Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Mark A. Griswold, Peter M. Jakob, Robin M. Heidemann, Mathias Nittka, Vladimir Jellus, Jianmin Wang, Berthold Kiefer, and Axel Haase · 2002
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Sparse MRI: The application of compressed sensing for rapid MR imaging
Michael Lustig, David L. Donoho, Juan M. Santos, and John M. Pauly · 2007
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Comprehensive quantification of signal-to-noise ratio and g-factor for image-based and k-space-based parallel imaging reconstructions
Philip M. Robson, Aaron K. Grant, nanth J. Madhuranthakam, Riccardo Lattanzi, Daniel K. Sodickson, and Charles A. McKenzie · 2008
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Pushing spatial and temporal resolution for functional and diffusion MRI in the human connectome project
Kamil Ugurbil, Junqian Xu, Edward J. Auerbach, Steen Moeller, An T. Vu, Julio Martin Duarte-Carvajalino, Christophe Lenglet, Xiaoping Wu, Sebastian Schmitter, Pierre-François van de Moortele, John P. Strupp, Guillermo Sapiro, Federico De Martino, Dingxin Wang, Noam Harel, Michael Garwood, Liyong Chen, David A. Feinberg, Stephen M. Smith, Karla L. Miller, Stamatios N. Sotiropoulos, Saâd Jbabdi, Jesper L. R. Andersson, Timothy Edward John Behrens, Matthew F. Glasser, David C. Van Essen, and Essa Yacoub · 2013
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ESPIRiT–an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA
Martin Uecker, Peng Lai, Mark J. Murphy, Patrick Virtue, Michael Elad, John Pauly, Shreyas S. Vasanawala, and Michael Lustig · 2014
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An integrated approach to correction for off-resonance effects and subject movement in diffusion mr imaging
Jesper LR Andersson and Sotiropoulos Stamatios N · 2016
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Clinical performance of high-resolution late gadolinium enhancement imaging with compressed sensing
Tamer A Basha, Mehmet Akcakaya, Charlene Liew, Connie W Tsao, Francesca N Delling, Gifty Addae, Long Ngo, Warren J Manning, and Reza Nezafat · 2017
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Challenges and open problems in signal processing: Panel discussion summary from ICASSP
Yonina C. Eldar, Alfred O. Hero III, Li Deng, Jeffrey A. Fessler, Jelena Kovacevic, H. Vincent Poor, and Steve J. Young · 2017
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NTIRE 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, Xintao Wang, Yapeng Tian, Ke Yu, Yulun Zhang, Shixiang Wu, Chao Dong, Liang Lin, Yu Qiao, Chen Change Loy, Woong Bae, Jae Jun Yoo, Yoseob Han, Jong Chul Ye, Jae-Seok Choi, Munchurl Kim, Yuchen Fan, Jiahui Yu, Wei Han, Ding Liu, Haichao Yu, Zhangyang Wang, Honghui Shi, Xinchao Wang, Thomas S. Huang, Yunjin Chen, Kai Zhang, Wangmeng Zuo, Zhimin Tang, Linkai Luo, Shaohui Li, Min Fu, Lei Cao, Wen Heng, Giang Bui, Truc Le, Ye Duan, Dacheng Tao, Ruxin Wang, Xu Lin, Jianxin Pang, Jinchang Xu, Yu Zhao, Xiangyu Xu, Jin-shan Pan, Deqing Sun, Yujin Zhang, Xibin Song, Yuchao Dai, Xueying Qin, Xuan-Phung Huynh, Tiantong Guo, Hojjat Seyed Mousavi, Tiep Huu Vu, Vishal Monga, Cristóvão Cruz, Karen O. Egiazarian, Vladimir Katkovnik, Rakesh Mehta, Arnav Kumar Jain, Abhinav Agarwalla, Ch V. Sai Praveen, Ruofan Zhou, Hongdiao Wen, Che Zhu, Zhiqiang Xia, Zhengtao Wang, and Qi Guo · 2017
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Learning a variational network for reconstruction of accelerated MRI data
Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P. Recht, Daniel K. Sodickson, Thomas Pock, and Florian Knoll · 2018
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A deep cascade of convolutional neural networks for dynamic MR image reconstruction
Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony N. Price, and Daniel Rueckert · 2018
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“Zero-shot” super-resolution using deep internal learning
Assaf Shocher, Nadav Cohen, and Michal Irani · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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Modl: Model-based deep learning architecture for inverse problems
Hemant Kumar Aggarwal, Merry P. Mani, and Mathews Jacob · 2019
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Assessment of the generalization of learned image reconstruction and the potential for transfer learning
Deep magnetic resonance image reconstruction: Inverse problems meet neural networks
Dong Liang, Jing Cheng, Ziwen Ke, and Leslie Ying · 2020
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Self2self with dropout: Learning self-supervised denoising from single image
Yuhui Quan, Mingqin Chen, Tongyao Pang, and Hui Ji · 2020
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Compressed sensing: From research to clinical practice with deep neural networks: Shortening scan times for magnetic resonance imaging
Christopher M Sandino, Joseph Y Cheng, Feiyu Chen, Morteza Mardani, John M Pauly, and Shreyas S Vasanawala · 2020
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Self-supervised learning of physics-guided reconstruction neural networks without fully-sampled reference data
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller, Jutta Ellermann, Kâmil Ugurbil, and Mehmet Akçakaya · 2020
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Measuring robustness in deep learning based compressive sensing
Mohammad Zalbagi Darestani, Akshay S. Chaudhari, and Reinhard Heckel · 2021
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Florian Knoll, Kerstin Hammernik, Erich Kobler, Thomas Pock, Michael P. Recht, and Daniel K. Sodickson · 2019
Cited alongside, same era.
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Florian Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova, Jure Zbontar, Michael G. Rabbat, Aaron Defazio, Matthew J. Muckley, Daniel K. Sodickson, C. Lawrence Zitnick, and Michael P. Recht · 2019
Cited alongside, same era.
Self-supervised learning of inverse problem solvers in medical imaging
Ortal Senouf, Sanketh Vedula, Tomer Weiss, Alex Bronstein, Oleg Michailovich, and Michael Zibulevsky · 2019
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MRI banding removal via adversarial training
Aaron Defazio, Tullie Murrell, and Michael Recht · 2020
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Optimization methods for magnetic resonance image reconstruction: Key models and optimization algorithms
Jeffrey A. Fessler · 2020
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High-fidelity accelerated MRI reconstruction by scan-specific fine-tuning of physics-based neural networks
Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller, and Mehmet Akçakaya
Cited in the paper.
Dense recurrent neural networks for accelerated MRI: history-cognizant unrolling of optimization algorithms
Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller, Mingyi Hong, and Mehmet Akçakaya
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Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training
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Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing
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Results of the 2020 fastmri challenge for machine learning MR image reconstruction
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Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging
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Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective
Mehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung, and Jong Chul Ye · 2022
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