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Deep learning methods have become the state of the art for undersampled MR reconstruction.
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 Börnert, 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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No-reference perceptual quality assessment of jpeg compressed images
Zhou Wang, Hamid R Sheikh, and Alan C Bovik · 2002
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Data compression: the complete reference
David Salomon · 2004
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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No-reference image quality metrics for structural mri
Jeffrey P Woodard and Monica P Carley-Spencer · 2006
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Fast poisson disk sampling in arbitrary dimensions
Robert Bridson · 2007
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Sparse mri: The application of compressed sensing for rapid mr imaging
Michael Lustig, David Donoho, and John M Pauly · 2007
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Blind/referenceless image spatial quality evaluator
Anish Mittal, Anush K Moorthy, and Alan C Bovik · 2011
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Creation of fully sampled mr data repository for compressed sensing of the knee
K. Epperson, A.M Sawyer, M. Lustig, M.T. Alley, M. Uecker, P. Virtue, P. Lai, and Vasanawala SS · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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 M Pauly, Shreyas S Vasanawala, and Michael Lustig · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Blind image quality evaluation using perception based features
N Venkatanath, D Praneeth, Maruthi Chandrasekhar Bh, Sumohana S Channappayya, and Swarup S Medasani · 2015
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Deep admm-net for compressive sensing mri
Jian Sun, Huibin Li, Zongben Xu, et al · 2016
Cited alongside, same era.
The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Cited alongside, same era.
Can signal-to-noise ratio perform as a baseline indicator for medical image quality assessment
Zhicheng Zhang, Guangzhe Dai, Xiaokun Liang, Shaode Yu, Leida Li, and Yaoqin Xie · 2018
Cited alongside, same era.
Noise2self: Blind denoising by self-supervision
Joshua Batson and Loic Royer · 2019
Cited alongside, same era.
Rare: Image reconstruction using deep priors learned without groundtruth
Jiaming Liu, Yu Sun, Cihat Eldeniz, Weijie Gan, Hongyu An, and Ulugbek S Kamilov · 2020
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NTIRE 2020 challenge on real-world image super-resolution: Methods and results
Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, Se Young Chun, Wei Deng, Mostafa El-Khamy, Chiu Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete Michelini, Christian Micheloni, Kalpesh Prajapati, Haoyu Ren, Yonghyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Haoning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, and Xueyi Zou · 2020
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Accelerated mp2rage imaging using cartesian phyllotaxis readout and compressed sensing reconstruction
Emilie Mussard, Tom Hilbert, Christoph Forman, Reto Meuli, Jean-Philippe Thiran, and Tobias Kober · 2020
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Using deep learning to accelerate knee mri at 3 t: results of an interchangeability study
Michael P Recht, Jure Zbontar, Daniel K Sodickson, Florian Knoll, Nafissa Yakubova, Anuroop Sriram, Tullie Murrell, Aaron Defazio, Michael Rabbat, Leon Rybak, et al · 2020
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Deep decoder: Concise image representations from untrained non-convolutional networks
Reinhard Heckel and Paul Hand · 2019
Cited alongside, same era.
Assessment of the generalization of learned image reconstruction and the potential for transfer learning
Florian Knoll, Kerstin Hammernik, Erich Kobler, Thomas Pock, Michael P Recht, and Daniel K Sodickson · 2019
Cited alongside, same era.
Sigpy: a python package for high performance iterative reconstruction
Frank Ong and Michael Lustig · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Mri t2 mapping of the knee providing synthetic morphologic images: comparison to conventional turbo spin-echo mri
Marion Roux, Tom Hilbert, Mahmoud Hussami, Fabio Becce, Tobias Kober, and Patrick Omoumi · 2019
Cited alongside, same era.
High-fidelity reconstruction with instance-wise discriminative feature matching loss
Ke Wang, Jonathan I. Tamir, and Stella X. Yu · 2019
Cited alongside, same era.
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 Uğurbil, and Mehmet Akçakaya · 2020
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Ssfd: Self-supervised feature distance as an mr image reconstruction quality metric
Philip M Adamson, Beliz Gunel, Jeffrey Dominic, Arjun D Desai, Daniel Spielman, Shreyas Vasanawala, John M Pauly, and Akshay Chaudhari · 2021
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Unsupervised deep learning methods for biological image reconstruction
Mehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung, and Jong Chul Ye · 2021
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Accelerated mri with un-trained neural networks
Mohammad Zalbagi Darestani and Reinhard Heckel · 2021
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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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Skm-tea: A dataset for accelerated mri reconstruction with dense image labels for quantitative clinical evaluation
Arjun D Desai, Andrew M Schmidt, Elka B Rubin, Christopher Michael Sandino, Marianne Susan Black, Valentina Mazzoli, Kathryn J Stevens, Robert Boutin, Christopher Re, Garry E Gold, et al · 2021
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Systematic evaluation of iterative deep neural networks for fast parallel mri reconstruction with sensitivity-weighted coil combination
Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, and Daniel Rueckert · 2021
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Results of the 2020 fastmri challenge for machine learning mr image reconstruction
Matthew J Muckley, Bruno Riemenschneider, Alireza Radmanesh, Sunwoo Kim, Geunu Jeong, Jingyu Ko, Yohan Jun, Hyungseob Shin, Dosik Hwang, Mahmoud Mostapha, et al · 2021
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Instabilities in conventional multi-coil mri reconstruction with small adversarial perturbations
Chi Zhang, Jinghan Jia, Burhaneddin Yaman, Steen Moeller, Sijia Liu, Mingyi Hong, and Mehmet Akçakaya · 2021
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fastmri+: Clinical pathology annotations for knee and brain fully sampled multi-coil mri data
Ruiyang Zhao, Burhaneddin Yaman, Yuxin Zhang, Russell Stewart, Austin Dixon, Florian Knoll, Zhengnan Huang, Yvonne W Lui, Michael S Hansen, and Matthew P Lungren · 2021
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Unsupervised mri reconstruction via zero-shot learned adversarial transformers
Yilmaz Korkmaz, Salman UH Dar, Mahmut Yurt, Muzaffer Özbey, and Tolga Cukur · 2022
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