| 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 | 78 | 2021 |
| High-frequency space diffusion model for accelerated mri C Cao, ZX Cui, Y Wang, S Liu, T Chen, H Zheng, D Liang, Y Zhu IEEE Transactions on Medical Imaging, 2024 | 76 | 2024 |
| 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 | 68 | 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 | 45 | 2021 |
| 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 | 38 | 2022 |
| A “Nonconvex+ Nonconvex” approach for image restoration with impulse noise removal ZX Cui, Q Fan Applied Mathematical Modelling 62, 254-271, 2018 | 26 | 2018 |
| 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 | 24 | 2021 |
| 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 | 21 | 2023 |
| A nonconvex nonsmooth regularization method for compressed sensing and low rank matrix completion ZX Cui, Q Fan Digital signal processing 62, 101-111, 2017 | 20 | 2017 |
| Generalized deep iterative reconstruction for sparse-view CT imaging T Su, Z Cui, J Yang, Y Zhang, J Liu, J Zhu, X Gao, S Fang, H Zheng, Y Ge, ... Physics in Medicine & Biology 67 (2), 025005, 2022 | 19 | 2022 |
| 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 | 17* | 2023 |
| Spirit-diffusion: Self-consistency driven diffusion model for accelerated mri ZX Cui, C Cao, Y Wang, S Jia, J Cheng, X Liu, H Zheng, D Liang, Y Zhu IEEE Transactions on Medical Imaging, 2024 | 15* | 2024 |
| Momentum methods for stochastic optimization over time-varying directed networks ZX Cui, Q Fan, C Jia Signal Processing 174, 107614, 2020 | 13 | 2020 |
| 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 | 11 | 2023 |
| Brain pet synthesis from mri using joint probability distribution of diffusion model at ultrahigh fields T Xie, C Cao, Z Cui, F Li, Z Wei, Y Zhu, Y Li, D Liang, Q Jin, G Chen, ... arXiv preprint arXiv:2211.08901, 2022 | 10 | 2022 |
| Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model Y Liu, L Ying, W Chen, ZX Cui, Q Zhu, X Liu, H Zheng, D Liang, Y Zhu Quantitative Imaging in Medicine and Surgery 11 (8), 3376, 2021 | 10 | 2021 |
| Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction Y Guan, C Yu, Z Cui, H Zhou, Q Liu IEEE Transactions on Medical Imaging, 2024 | 9 | 2024 |
| Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion ZX Cui, C Liu, X Fan, C Cao, J Cheng, Q Zhu, Y Liu, S Jia, H Wang, Y Zhu, ... IEEE Transactions on Medical Imaging, 2024 | 7* | 2024 |
| Deep unfolding as iterative regularization for imaging inverse problems ZX Cui, Q Zhu, J Cheng, B Zhang, D Liang Inverse Problems 40 (2), 025011, 2024 | 6 | 2024 |
| Synthesizing PET images from high‐field and ultra‐high‐field MR images using joint diffusion attention model T Xie, C Cao, Z Cui, Y Guo, C Wu, X Wang, Q Li, Z Hu, T Sun, Z Sang, ... Medical Physics 51 (8), 5250-5269, 2024 | 5 | 2024 |
Qingyong ZhuShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα siat.ac.cn
