标题/作者	年份
Image reconstruction by domain-transform manifold learning B Zhu, JZ Liu, SF Cauley, BR Rosen, MS Rosen Nature 555 (7697), 487-492	2018
All-optical machine learning using diffractive deep neural networks X Lin, Y Rivenson, NT Yardimci, M Veli, Y Luo, M Jarrahi, A Ozcan Science 361 (6406), 1004-1008	2018
DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction G Yang, S Yu, H Dong, G Slabaugh, PL Dragotti, X Ye, F Liu, S Arridge, ... IEEE transactions on medical imaging 37 (6), 1310-1321	2017
MoDL: Model-based deep learning architecture for inverse problems HK Aggarwal, MP Mani, M Jacob IEEE transactions on medical imaging 38 (2), 394-405	2018
Learned primal-dual reconstruction J Adler, O Öktem IEEE transactions on medical imaging 37 (6), 1322-1332	2018
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data Y Zhu, N Zabaras, PS Koutsourelakis, P Perdikaris Journal of Computational Physics 394, 56-81	2019
Deep learning in medical imaging and radiation therapy B Sahiner, A Pezeshk, LM Hadjiiski, X Wang, K Drukker, KH Cha, ... Medical physics 46 (1), e1-e36	2019
fastMRI: An open dataset and benchmarks for accelerated MRI J Zbontar, F Knoll, A Sriram, T Murrell, Z Huang, MJ Muckley, A Defazio, ... arXiv preprint arXiv:1811.08839	2018
Deep generative adversarial neural networks for compressive sensing MRI M Mardani, E Gong, JY Cheng, SS Vasanawala, G Zaharchuk, L Xing, ... IEEE transactions on medical imaging 38 (1), 167-179	2018
Convolutional recurrent neural networks for dynamic MR image reconstruction C Qin, J Schlemper, J Caballero, AN Price, JV Hajnal, D Rueckert IEEE transactions on medical imaging 38 (1), 280-290	2018
A gentle introduction to deep learning in medical image processing A Maier, C Syben, T Lasser, C Riess Zeitschrift für Medizinische Physik 29 (2), 86-101	2019
ADMM-CSNet: A deep learning approach for image compressive sensing Y Yang, J Sun, H Li, Z Xu IEEE transactions on pattern analysis and machine intelligence 42 (3), 521-538	2018
On instabilities of deep learning in image reconstruction and the potential costs of AI V Antun, F Renna, C Poon, B Adcock, AC Hansen Proceedings of the National Academy of Sciences 117 (48), 30088-30095	2020
Deep learning for undersampled MRI reconstruction CM Hyun, HP Kim, SM Lee, S Lee, JK Seo Physics in Medicine & Biology 63 (13), 135007	2018
Image reconstruction is a new frontier of machine learning G Wang, JC Ye, K Mueller, JA Fessler IEEE transactions on medical imaging 37 (6), 1289-1296	2018
Deep learning techniques for inverse problems in imaging G Ongie, A Jalal, CA Metzler, RG Baraniuk, AG Dimakis, R Willett IEEE Journal on Selected Areas in Information Theory 1 (1), 39-56	2020
CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE) C You, G Li, Y Zhang, X Zhang, H Shan, M Li, S Ju, Z Zhao, Z Zhang, ... IEEE transactions on medical imaging 39 (1), 188-203	2019
On modeling DS Novikov, VG Kiselev, SN Jespersen Magnetic resonance in medicine 79 (6), 3172-3193	2018
LEARN: Learned experts’ assessment-based reconstruction network for sparse-data CT H Chen, Y Zhang, Y Chen, J Zhang, W Zhang, H Sun, Y Lv, P Liao, J Zhou, ... IEEE transactions on medical imaging 37 (6), 1333-1347	2018
Super‐resolution musculoskeletal MRI using deep learning AS Chaudhari, Z Fang, F Kogan, J Wood, KJ Stevens, EK Gibbons, ... Magnetic resonance in medicine 80 (5), 2139-2154	2018

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