| 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 | 603 | 2018 |
| Deep learning in neuroradiology G Zaharchuk, E Gong, M Wintermark, D Rubin, CP Langlotz American Journal of Neuroradiology 39 (10), 1776-1784, 2018 | 323 | 2018 |
| Deep learning enables reduced gadolinium dose for contrast‐enhanced brain MRI E Gong, JM Pauly, M Wintermark, G Zaharchuk Journal of magnetic resonance imaging 48 (2), 330-340, 2018 | 322 | 2018 |
| Ultra–Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs KT Chen, E Gong, FB de Carvalho Macruz, J Xu, A Boumis, M Khalighi, ... Radiology 290 (3), 649-656, 2019 | 246 | 2019 |
| Dsd: Dense-sparse-dense training for deep neural networks S Han, J Pool, S Narang, H Mao, E Gong, S Tang, E Elsen, P Vajda, ... arXiv preprint arXiv:1607.04381, 2016 | 237 | 2016 |
| Quantitative susceptibility mapping using deep neural network: QSMnet J Yoon, E Gong, I Chatnuntawech, B Bilgic, J Lee, W Jung, J Ko, H Jung, ... Neuroimage 179, 199-206, 2018 | 216 | 2018 |
| Deep generative adversarial networks for compressed sensing automates MRI M Mardani, E Gong, JY Cheng, S Vasanawala, G Zaharchuk, M Alley, ... arXiv preprint arXiv:1706.00051, 2017 | 180 | 2017 |
| 200x low-dose PET reconstruction using deep learning J Xu, E Gong, J Pauly, G Zaharchuk arXiv preprint arXiv:1712.04119, 2017 | 176 | 2017 |
| ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI S Winzeck, A Hakim, R McKinley, JA Pinto, V Alves, C Silva, M Pisov, ... Frontiers in neurology 9, 679, 2018 | 170 | 2018 |
| Ultra‐low‐dose PET reconstruction using generative adversarial network with feature matching and task‐specific perceptual loss J Ouyang, KT Chen, E Gong, J Pauly, G Zaharchuk Medical physics 46 (8), 3555-3564, 2019 | 157 | 2019 |
| Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging Y Yu, Y Xie, T Thamm, E Gong, J Ouyang, C Huang, S Christensen, ... JAMA network open 3 (3), e200772-e200772, 2020 | 143 | 2020 |
| Use of gradient boosting machine learning to predict patient outcome in acute ischemic stroke on the basis of imaging, demographic, and clinical information Y Xie, B Jiang, E Gong, Y Li, G Zhu, P Michel, M Wintermark, G Zaharchuk American Journal of Roentgenology 212 (1), 44-51, 2019 | 99 | 2019 |
| A dynamically assembled cell wall synthesis machinery buffers cell growth TK Lee, C Tropini, J Hsin, SM Desmarais, TS Ursell, E Gong, Z Gitai, ... Proceedings of the National Academy of Sciences 111 (12), 4554-4559, 2014 | 98 | 2014 |
| Dsd: Regularizing deep neural networks with dense-sparse-dense training flow S Han, J Pool, S Narang, H Mao, S Tang, E Elsen, B Catanzaro, J Tran, ... arXiv preprint arXiv:1607.04381, 2016 | 94 | 2016 |
| PROMISE: parallel‐imaging and compressed‐sensing reconstruction of multicontrast imaging using SharablE information E Gong, F Huang, K Ying, W Wu, S Wang, C Yuan Magnetic resonance in medicine 73 (2), 523-535, 2015 | 56 | 2015 |
| Synthesize high-quality multi-contrast magnetic resonance imaging from multi-echo acquisition using multi-task deep generative model G Wang, E Gong, S Banerjee, D Martin, E Tong, J Choi, H Chen, ... IEEE transactions on medical imaging 39 (10), 3089-3099, 2020 | 52 | 2020 |
| Low-count whole-body PET with deep learning in a multicenter and externally validated study AS Chaudhari, E Mittra, GA Davidzon, P Gulaka, H Gandhi, A Brown, ... NPJ digital medicine 4 (1), 127, 2021 | 48 | 2021 |
| A generic deep learning model for reduced gadolinium dose in contrast‐enhanced brain MRI S Pasumarthi, JI Tamir, S Christensen, G Zaharchuk, T Zhang, E Gong Magnetic Resonance in Medicine 86 (3), 1687-1700, 2021 | 44 | 2021 |
| Joint multi‐contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging D Polak, S Cauley, B Bilgic, E Gong, P Bachert, E Adalsteinsson, ... Magnetic resonance in medicine 84 (3), 1456-1469, 2020 | 42 | 2020 |
| Predicting 15O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias J Guo, E Gong, AP Fan, M Goubran, MM Khalighi, G Zaharchuk Journal of Cerebral Blood Flow & Metabolism 40 (11), 2240-2253, 2020 | 39 | 2020 |
Greg ZaharchukStanford University在 stanford.edu 的电子邮件经过验证
