| 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 | 248 | 2019 |
| 200x low-dose PET reconstruction using deep learning J Xu, E Gong, J Pauly, G Zaharchuk arXiv preprint arXiv:1712.04119, 2017 | 178 | 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 | 169 | 2018 |
| NeSVoR: implicit neural representation for slice-to-volume reconstruction in MRI J Xu, D Moyer, B Gagoski, JE Iglesias, PE Grant, P Golland, ... IEEE transactions on medical imaging 42 (6), 1707-1719, 2023 | 47 | 2023 |
| Assessing EGFR gene mutation status in non-small cell lung cancer with imaging features from PET/CT M Jiang, Y Zhang, J Xu, M Ji, Y Guo, Y Guo, J Xiao, X Yao, H Shi, M Zeng Nuclear medicine communications 40 (8), 842-849, 2019 | 35 | 2019 |
| Fetal pose estimation in volumetric MRI using a 3D convolution neural network J Xu, M Zhang, EA Turk, L Zhang, PE Grant, K Ying, P Golland, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 33 | 2019 |
| Multi-scale neural odes for 3d medical image registration J Xu, EZ Chen, X Chen, T Chen, S Sun Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 30 | 2021 |
| Deformed2self: Self-supervised denoising for dynamic medical imaging J Xu, E Adalsteinsson Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 29 | 2021 |
| SVoRT: Iterative transformer for slice-to-volume registration in fetal brain MRI J Xu, D Moyer, PE Grant, P Golland, JE Iglesias, E Adalsteinsson International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 28 | 2022 |
| Automated detection and reacquisition of motion‐degraded images in fetal HASTE imaging at 3 T B Gagoski, J Xu, P Wighton, MD Tisdall, R Frost, WC Lo, P Golland, ... Magnetic resonance in medicine 87 (4), 1914-1922, 2022 | 26 | 2022 |
| Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency J Xu, S Lala, B Gagoski, E Abaci Turk, PE Grant, P Golland, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 23 | 2020 |
| Ultra-low-dose 18F-FDG brain PET/MR denoising using deep learning and multi-contrast information J Xu, E Gong, J Ouyang, J Pauly, G Zaharchuk Medical imaging 2020: Image processing 11313, 420-432, 2020 | 20 | 2020 |
| Enhanced detection of fetal pose in 3D MRI by deep reinforcement learning with physical structure priors on anatomy M Zhang, J Xu, E Abaci Turk, PE Grant, P Golland, E Adalsteinsson Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 19 | 2020 |
| STRESS: Super-resolution for dynamic fetal MRI using self-supervised learning J Xu, E Abaci Turk, PE Grant, P Golland, E Adalsteinsson Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 12 | 2021 |
| 3D fetal pose estimation with adaptive variance and conditional generative adversarial network J Xu, M Zhang, EA Turk, PE Grant, P Golland, E Adalsteinsson Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis …, 2020 | 10 | 2020 |
| Latent signal models: Learning compact representations of signal evolution for improved time‐resolved, multi‐contrast MRI Y Arefeen, J Xu, M Zhang, Z Dong, F Wang, J White, B Bilgic, ... Magnetic Resonance in Medicine 90 (2), 483-501, 2023 | 7 | 2023 |
| 200x low-dose PET reconstruction using deep learning. 2017 J Xu, E Gong, J Pauly, G Zaharchuk CoRR, abs/1712.04119, 2019 | 6 | 2019 |
| Zero-shot self-supervised joint temporal image and sensitivity map reconstruction via linear latent space M Zhang, J Xu, Y Arefeen, E Adalsteinsson Medical Imaging with Deep Learning, 1713-1725, 2024 | 5 | 2024 |
| Improved prediction of the final infarct from acute stroke neuroimaging using deep learning Y Niu, E Gong, J Xu, T Thamm, J Pauly, G Zaharchuk Proceedings of the 26th International Society for Magnetic Resonance in …, 2018 | 5* | 2018 |
| Deep Learning-based Fetal-Uterine Motion Modeling from Volumetric EPI Time Series M Li, Y Xiao, T Liu, J Xu, E Turk, B Gagoski, K Ying, P Golland, E Grant, ... ISMRM 2020, 1027, 2020 | 2 | 2020 |
Polina GollandMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
