| Deep magnetic resonance image reconstruction: Inverse problems meet neural networks D Liang, J Cheng, Z Ke, L Ying IEEE Signal Processing Magazine 37 (1), 141-151, 2020 | 293 | 2020 |
| Model learning: Primal dual networks for fast MR imaging J Cheng, H Wang, L Ying, D Liang Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 84 | 2019 |
| Deep MRI reconstruction: unrolled optimization algorithms meet neural networks D Liang, J Cheng, Z Ke, L Ying arXiv preprint arXiv:1907.11711, 2019 | 70 | 2019 |
| Deep low-rank plus sparse network for dynamic MR imaging W Huang, Z Ke, ZX Cui, J Cheng, Z Qiu, S Jia, L Ying, Y Zhu, D Liang Medical Image Analysis 73, 102190, 2021 | 60 | 2021 |
| Learned low-rank priors in dynamic MR imaging Z Ke, W Huang, ZX Cui, J Cheng, S Jia, H Wang, X Liu, H Zheng, L Ying, ... IEEE Transactions on Medical Imaging 40 (12), 3698-3710, 2021 | 53 | 2021 |
| Learning data consistency and its application to dynamic MR imaging J Cheng, ZX Cui, W Huang, Z Ke, L Ying, H Wang, Y Zhu, D Liang IEEE Transactions on Medical Imaging 40 (11), 3140-3153, 2021 | 33 | 2021 |
| An unsupervised deep learning method for multi-coil cine MRI Z Ke, J Cheng, L Ying, H Zheng, Y Zhu, D Liang Physics in medicine & biology 65 (23), 235041, 2020 | 31 | 2020 |
| Self-score: Self-supervised learning on score-based models for mri reconstruction ZX Cui, C Cao, S Liu, Q Zhu, J Cheng, H Wang, Y Zhu, D Liang arXiv preprint arXiv:2209.00835, 2022 | 24 | 2022 |
| Deep manifold learning for dynamic MR imaging Z Ke, ZX Cui, W Huang, J Cheng, S Jia, L Ying, Y Zhu, D Liang IEEE Transactions on Computational Imaging 7, 1314-1327, 2021 | 21 | 2021 |
| Improved parallel image reconstruction using feature refinement J Cheng, S Jia, L Ying, Y Liu, S Wang, Y Zhu, Y Li, C Zou, X Liu, D Liang Magnetic Resonance in Medicine 80 (1), 211-223, 2018 | 15 | 2018 |
| K-UNN: k-space interpolation with untrained neural network ZX Cui, S Jia, C Cao, Q Zhu, C Liu, Z Qiu, Y Liu, J Cheng, H Wang, Y Zhu, ... Medical Image Analysis 88, 102877, 2023 | 14 | 2023 |
| Accelerating MR imaging via deep Chambolle-Pock network H Wang, J Cheng, S Jia, Z Qiu, C Shi, L Zou, S Su, Y Chang, Y Zhu, ... 2019 41st Annual International Conference of the IEEE Engineering in …, 2019 | 12 | 2019 |
| One-shot generative prior in hankel-k-space for parallel imaging reconstruction H Peng, C Jiang, J Cheng, M Zhang, S Wang, D Liang, Q Liu IEEE Transactions on Medical Imaging, 2023 | 11 | 2023 |
| Model-based deep medical imaging: the roadmap of generalizing iterative reconstruction model using deep learning J Cheng, H Wang, Y Zhu, Q Liu, Q Zhang, T Su, J Chen, Y Ge, Z Hu, X Liu, ... arXiv preprint arXiv:1906.08143, 2019 | 11 | 2019 |
| Equilibrated zeroth-order unrolled deep network for parallel MR imaging ZX Cui, S Jia, J Cheng, Q Zhu, Y Liu, K Zhao, Z Ke, W Huang, H Wang, ... IEEE Transactions on Medical Imaging, 2023 | 8 | 2023 |
| Physics-driven deep learning methods for fast quantitative magnetic resonance imaging: Performance improvements through integration with deep neural networks Y Zhu, J Cheng, ZX Cui, Q Zhu, L Ying, D Liang IEEE Signal Processing Magazine 40 (2), 116-128, 2023 | 8 | 2023 |
| Incorporating reference guided priors into calibrationless parallel imaging reconstruction Q Zhu, W Wang, J Cheng, X Peng Magnetic resonance imaging 57, 347-358, 2019 | 7 | 2019 |
| Model-based Deep MR Imaging: the roadmap of generalizing compressed sensing model using deep learning J Cheng, H Wang, Y Zhu, Q Liu, L Ying, D Liang arXiv preprint arXiv:1906.08143, 2019 | 7 | 2019 |
| Spirit-diffusion: Self-consistency driven diffusion model for accelerated mri ZX Cui, C Cao, J Cheng, S Jia, H Zheng, D Liang, Y Zhu arXiv preprint arXiv:2304.05060, 2023 | 6 | 2023 |
| SPIRiT-diffusion: SPIRiT-driven score-based generative modeling for vessel wall imaging C Cao, ZX Cui, J Cheng, S Jia, H Zheng, D Liang, Y Zhu arXiv preprint arXiv:2212.11274, 2022 | 5 | 2022 |
