| SuperDTI: Ultrafast DTI and fiber tractography with deep learning H Li, Z Liang, C Zhang, R Liu, J Li, W Zhang, D Liang, B Shen, X Zhang, ... Magnetic resonance in medicine 86 (6), 3334-3347, 2021 | 54 | 2021 |
| Imaging-based intelligent spectrometer on a plasmonic rainbow chip D Tua, R Liu, W Yang, L Zhou, H Song, L Ying, Q Gan Nature Communications 14 (1), 1902, 2023 | 32 | 2023 |
| SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling H Li, M Yang, JH Kim, C Zhang, R Liu, P Huang, D Liang, X Zhang, X Li, ... Magnetic resonance in medicine 89 (1), 64-76, 2023 | 22 | 2023 |
| Diagnostic interchangeability of deep convolutional neural networks reconstructed knee MR images: preliminary experience N Subhas, H Li, M Yang, CS Winalski, J Polster, N Obuchowski, ... Quantitative Imaging in Medicine and Surgery 10 (9), 1748, 2020 | 22 | 2020 |
| Deep MRI reconstruction without ground truth for training P Huang, C Zhang, H Li, SK Gaire, R Liu, X Zhang, X Li, L Ying Proceedings of ISMRM, 4668, 2019 | 14 | 2019 |
| Ultra-fast simultaneous T1rho and T2 mapping using deep learning H Li, M Yang, J Kim, R Liu, C Zhang, P Huang, SK Gaire, D Liang, X Li, ... Proceedings of the 28th Annual Meeting of ISMRM, Virtual Conference and …, 2020 | 13 | 2020 |
| Unsupervised deep learning reconstruction using the MR imaging model P Huang, C Zhang, H Li, SK Gaire, R Liu, X Zhang, X Li, L Dong, L Ying Proc. ISMRM, 3617, 2020 | 4 | 2020 |
| Deep learned diffusion tensor imaging H Li, C Zhang, Z Liang, D Liang, B Shen, Y Ge, J Zhang, R Liu, P Huang, ... Proceedings of the 27th Annual Meeting of ISMRM, Montreal, Canada, 3344, 2019 | 4 | 2019 |
| SuperMAP: superfast MR mapping with joint under-sampling using deep combined network H Li, M Yang, J Kim, C Zhang, R Liu, P Huang, SK Gaire, D Liang, ... Annual Meeting of the International Society for Magnetic Resonance in …, 2021 | 2 | 2021 |
| Deep learning MR relaxometry with joint spatial-temporal under-sampling H Li, M Yang, J Kim, R Liu, C Zhang, P Huang, SK Gaire, D Liang, X Li, ... Annual Meeting of the International Society for Magnetic Resonance in …, 2020 | 2 | 2020 |
| Accelerated 3D localization microscopy using blind sparse inpainting SK Gaire, C Zhang, H Li, P Huang, R Liu, H Wang, D Liang, L Ying 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019 | 2 | 2019 |
| Robust Highly-accelerated MR Fingerprinting Using Transformer-based Deep Learning P Huang, B Eck, R Liu, H Li, M Yang, J Kim, X Zhang, X Li, L Ying Proceedings of the International Society for Magnetic Resonance in Medicine …, 2024 | 1 | 2024 |
| Accelerating MRI Using Vision Transformer with Unpaired Unsupervised Training P Huang, H Li, R Liu, X Zhang, X Li, D Liang, L Ying Proceedings of the International Society for Magnetic Resonance in Medicine …, 2023 | 1 | 2023 |
| Unsupervised Deep Unrolled Reconstruction Powered by Regularization by Denoising P Huang, C Zhang, H Li, R Liu, X Zhang, X Li, D Liang, L Ying | 1 | 2022 |
| Joint 3D parameter mapping and motion correction using a kernel low rank method with offline training C Zhang, JH Kim, H Li, P Huang, R Liu, D Liang, X Zhang, X Li, L Ying Proc. Intl. Soc. Mag. Reson. Med.(ISMRM), 2020 | 1 | 2020 |
| Superfast diffusion tensor imaging and fiber tractography using deep learning H Li, Z Liang, C Zhang, R Liu, J Li, W Zhang, D Liang, B Shen, X Zhang, ... arXiv preprint ArXiv:2002.01031, 0 | 1 | |
| Dual-Functional Intelligent Spectrometer Using a Plasmonic Rainbow Chip D Tua, R Liu, W Yang, L Zhou, H Song, L Ying, Q Gan 2023 IEEE Photonics Conference (IPC), 1-2, 2023 | | 2023 |
| A plasmonic “rainbow” chip for intelligent spectrometer D Tua, R Liu, L Zhou, W Yang, H Song, L Ying, Q Gan CLEO: Science and Innovations, STh3R. 5, 2023 | | 2023 |
| SuperDTI: Superfast Deep-learned Diffusion Tensor Imaging H Li, Z Liang, C Zhang, R Liu, J Li, W Zhang, D Liang, B Shen, P Huang, ... Proc. Intl. Soc. Mag. Reson. Med 29, 1335, 2021 | | 2021 |
| Multi-vendor Multi-site accelerated high-resolution T1ρ mapping with retrospective and prospective compressed sensing reconstruction Z Zhang, J Kim, R Liu, R Latery, CS Winalski, M Yang, J Liu, Q Peng, ... | | |
