姓名：梁栋
职称：研究员/所长、中心主任
学位：博士
邮箱： dong.liang@siat.ac.cn
研究领域：生物医学成像、快速磁共振成像、压缩感知理论、信号处理、机器学习
梁栋（LIANG DONG），研究员，国家杰出青年基金获得者。中国科学院深圳先进技术研究院医工所所长、医学人工智能研究中心主任。2006年3月于上海交通大学获得博士学位，后赴香港大学及美国威斯康星大学密尔沃基分校做博士后研究，2011年4月加入中国科学院深圳先进技术研究院。主要研究方向为快速结构和功能磁共振成像。从理论方法、技术应用到转移转化做出了系统性的成果，发展了多先验快速成像框架；攻克了自由呼吸心脏电影、三维高分辨血管壁成像等关键技术；与民族企业共同研制了我国首型3T快速成像产品及国际首个FDA认证的人工智能快速成像产品。权威期刊PMB将细节保真方法评为该刊2016年“最前沿并具影响力”亮点之一，从大数据中直接学习成像映射的工作被同行评价为“首次将深度学习用于磁共振成像”及“有潜力为成像科学带来革命”。
主持国家杰出青年科学基金，国家自然科学基金重点项目、国家重点研发计划课题等多个科研项目；发表SCI文章100余篇，授权发明专利43项。现担任中国科学院医学成像技术与装备工程实验室主任，《IEEE
Transactions on Medical Imaging》副主编、《Magnetic Resonance in
Medicine》编委；获2018年度王天眷波谱学奖、2018年度中国专利优秀奖（第一发明人）、2018年度深圳市技术发明一等奖（第一完成人）、2018年度广东省技术发明一等奖（第二完成人）、2019年度中国生物医学工程学会黄家驷生物医学工程科技进步一等奖（第二完成人）和2020年度国家科技进步一等奖（第五完成人）。
1、集成已知部分支集压缩感知和并行成像的动态对比增强磁共振成像重建方法研究，主持，国家级，2012-01--2014-12，结题
2、实时高分辨磁共振成像的理论与方法研究，参与，国家级, 2012-01--2016-12，结题
3、基于深度稀疏表达的快速磁共振成像研究，主持，国家级，2015-01--2018-12，结题
4、静态数字乳腺断层成像系统，主持，省级，2016-01--2017-12，结题
5、PET-MRI成像理论与关键技术研究，主持，省级, 2016-01--2018-12，结题
6、线圈研制及临床序列开发，主持，国家级, 2017-07--2020-12，结题
7、新型超快速多尺度磁共振成像与波谱及其高分辨重建，参与，国家级，2019-01--2022-12，结题
8、分布式超快核磁共振成像的数学理论与算法，主持，国家级，国自然数学天元基金，2021-01--2022-12，结题
9、快速磁共振成像，主持，国家级，国自然杰出青年基金，2021-01--2026-12，在研
10、基于学习模型的快速成像方法与应用研究，主持，国家级，国家重点研发计划课题，2021-12--2026-11，在研
11、低剂量CT成像关键技术及术中图像融合算法研究，主持，深圳，深圳市基础研究（重点项目），2021-10--2025-11，在研
12、耦合运动与不变性先验的消化道动态高分辨磁共振成像方法及应用，主持，国家级，国自然重点项目，2024-01--2028.12，在研
1.Liu C, Cui Z X, Jia S, et al. Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network[J]. Medical Physics, 2023.
2.Cui Z X, Jia S, Cheng J, et al. Equilibrated zeroth-order unrolled deep network for parallel mr imaging[J]. IEEE Transactions on Medical Imaging, 2023.
3.Cui Z X, Jia S, Cao C, et al. K-UNN: k-space interpolation with untrained neural network[J]. Medical Image Analysis, 2023, 88: 102877.
4.Peng H, Jiang C, Cheng J, et al. One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction[J]. IEEE Transactions on Medical Imaging, 2023.
5.Xu W, Jia S, Cui ZX, Zhu Q, Liu X, Liang D, Cheng J. Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging. Bioengineering (Basel). 2023
6.Zhou Y, Wang H, Liu C, Liao B, Li Y, Zhu Y, Hu Z, Liao J, Liang D. Recent advances in highly accelerated 3D MRI. Phys Med Biol. 2023 Jul 10;68(14).
7.Zhu Q, Cui ZX, Liu Y, Cheng J, Zhao K, Wang H, Zhu Y, Liang D. Characteristic-constrained accelerating MR T1rho mapping with blockwise infimal convolution of matrix elastic-net regularization. Med Phys. 2023 Apr;50(4):2224-2238
8.Shi C, Liu Y, Cheng G, Qi Y, Wang H, Liu X, Liang D, Zhu Y. High-efficiency 3D black-blood thoracic aorta imaging with patch-based low-rank tensor reconstruction. Quant Imaging Med Surg. 2023 Apr 1;13(4):2538-2555.
9.Q. Zhang et al., "Deep Generalized Learning Model for PET Image Reconstruction," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2023.3293836
10.Su T, Zhu J, Zhang X, Tan Y, Cui H, Zeng D, Guo J, Zheng H, Ma J, Liang D, Ge Y. Super resolution dual-energy cone-beam CT imaging with dual-layer flat-panel detector. IEEE Trans Med Imaging. 2023 Sep 27;PP. doi: 10.1109/TMI.2023.3319668.
11.Y Zhou, H Wang, Y Liu, D Liang*, and L Ying, Accelerating MR Parameter Mapping Using Nonlinear Compressive Manifold Learning and Regularized Pre-Imaging, IEEE, Trans. on Biomedical Engineering, 69(10): 2996-3007, 2022
12.C Liu, Y Zhou, C Shi, Z Cui, Y Liu, Y Guo, K Huang, C Zou, Y Zhu, X Liu, Y Li, H Zheng, D Liang *, and H Wang*, Design and Implementation of Low-cost Distributed Tabletop Magnetic Particle Imaging System, IEEE Trans. on Magnetic, 58(7): 5300115, 2022.
13.Z Ke#, W Huang#, Z Cui, J Cheng, S Jia, H Wang, X Liu, H Zheng, L Ying, Y Zhu*, D Liang*, Learned Low-rank Priors in Dynamic MR Imaging, IEEE Trans. on Medical Imaging, DOI: 10.1109/TMI.2021.3096218, 2021 （影响因子10.048）
14.J Cheng#, Z Cui#, W Huang, Z Ke, L Ying, H Wang, Y Zhu, D Liang*, Learning data consistency and its application to MR dynamic imaging, IEEE Transactions on Medical Imaging, 11: 3140-3153, 2021
15.W Huang#, Z Ke#, Z Cui, J Cheng, Z Qiu, S Jia, L Ying, Y Zhu, D Liang*, Deep low-Rank plus sparse network for dynamic MR imaging. Medical Image Analysis, 73:102190, 2021 （影响因子8.545）
16.C Quan, Cong and J Zhou, Y Zhu and Y Chen and S Wang, D Liang* and Q Liu*, Homotopic Gradients of Generative Density Priors for MR Image Reconstruction, IEEE Transactions on Medical Imaging, 10.1109/TMI.2021.3081677, 2021
17.S Wang, J Lv, Z He, D Liang*, Y Chen, M Zhang, Q Liu*, Denoising auto-encoding priors in undecimated wavelet domain for MR image reconstruction, Neurocomputing, 437: 325-338, 2021
18.Z Qiu#, S Jia#, S Su, Y Zhu, X Liu, H Zheng, Dong Liang*, H Wang, Highly accelerated parallel MRI using wave encoding and virtual conjugate coils, Magnetic Resonance in Medicine, DOI: 10.1002/mrm.28803, 2021 （影响因子4.668）
19.S Su#, Z Qiu#, C Luo, C Shi, L Wan, Y Zhu, Y Li, X Liu, H Zheng, D Liang*, H Wang*. Accelerated 3D bSSFP using A Modified Wave-CAIPI Technique with Truncated Wave Gradients, IEEE Transactions on Medical Imaging, 40:48-58, 2021 （影响因子10.048）
20.Y Zhu#, Y Liu# ,L Ying, Z Qiu, Q Liu, S Jia, H Wang, X Liu, H Zheng, D Liang*, A 4-minute solution for submillimeter whole-brain T1rho quantification, Magnetic Resonance in Medicine, 85: 3299-3307, 2021 （影响因子4.668）
21.Y Ge, P Liu, Y Ni, J Chen, J Yang, T Su, H Zhang, J Guo, H Zheng, Z Li* and D Liang*, Enhancing the X-ray differential phase contrast image quality with deep learning technique, IEEE Transactions on Biomedical Engineering, 68: 1751-1758, 2021
22.H Zhao#, Z Ke#, F Yang, K Li, N Chen, L Song, C Zheng*, D Liang*, C Liu*. Deep Learning Enables Superior Photoacoustic Imaging at Ultralow Laser Dosages, Advanced Science, DOI: 10.1002/advs.202003097, 2020 （影响因子15.840）
23.Q Zhang, Z Hu, C Jiang, H Zheng, Y Ge* and D Liang*. Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging, Physics in Medicine and Biology, 65(15): 5010, 2020 （影响因子3.609）9
24.S Jia, L Zhang, L Ren, Y Qi, J Ly, N Zhang, Y Li, X Liu, H Zheng, D Liang*, Y Chung, Joint intracranial and carotid vessel wall imaging in 5 minutes using compressed sensing accelerated DANTE-SPACE, European Radiology, 30:119–127, 2020 （影响因子5.315）
25.Q Liu, Q Yang, H Cheng, S Wang, M Zhang, D Liang*, Highly undersampled magnetic resonance imaging reconstruction using autoencoding prior, Magnetic Resonance in Medicine, 83:322-336, 2020
26.Y Ge, T Su, J Zhu, X Deng, Q Zhang, J Chen, Z Hu, H Zheng, D Liang*, ADAPTIVE-NET: deep computed tomography reconstruction network with analytical domain transformation knowledge, Quantitative Imaging in Medicine and Surgery, 2:415-427, 2020
27.Y Liu#, Q Liu#, M Zhang*, Q Yang, S Wang, D Liang*, IFR-Net: Iterative feature refinement network for compressed sensing MRI, IEEE Transactions on Computational Imaging, 6(1):434-446, 2020 （影响因子3.814）
28.H Wang#, Z Qiu#, S Su, S Jia, Y Li, X Liu, H Zheng, D Liang*, Parameter optimization framework on wave gradients of WaveCAIPI imaging, Magnetic Resonance in Medicine, 83:1659-1672, 2020 （影响因子4.668）
29.Y Zhu#, Y Liu#, L Ying, X Liu, H Zheng, D Liang*, Bio-SCOPE: Fast bi-exponentialT1ρ mapping of the brain using signal-compensated low-rank plus sparse matrix decomposition, Magnetic Resonance in Medicine, 83：2092-2106, 2020. （影响因子4.668）
30.S Li, B Qin, J Xiao, Q Liu*, Y Wang, D Liang*, Multi-channel and multi-model based autoencoding prior for grayscale image restoration, IEEE Transactions on Image Processing, 29:142-156, 2019
31.H Wang#, S Jia#, Y Chang, Y Zhu, C Zou, Y Li, X Liu, H Zheng, D Liang*, Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils, Physics in Medicine and Biology, 64:14NT01, 2019 （影响因子3.609）
32.S Wang#, Z Ke#, H Cheng, S Jia, L Ying, H Zheng*, D Liang*, DIMENSION: Dynamic MR imaging with both K-space and spatial prior knowledge obtained via multi-supervised network training, NMR in Biomedicine, DOI:10.1002/nbm.4131, 2019 （影响因子4.044）
33.Z Hu#, C Jiang#, F Sun, Q Zhang, Y Ge, Y Yang, X Liu, H Zheng, D Liang*, Artifact correction in low-dose dental CT imaging using wasserstein generative adversarial networks, Medical Physics, 46(4):1686-1696, 2019（影响因子4.071）
33.S Wang, Y Gao, Q Liu, L Ying, T Xiao, Y Liu, X Liu, H Zheng, D Liang*, Learning joint-sparse codes for calibration-free parallel MR imaging (LINDBERG), IEEE Transactions on Medical Imaging, 37(1): 251-261, 2018
34.B Xiong, Q Liu, J Xiong, S Li, S Wang, D Liang*, Field-of-Experts filters guided tensor completion, IEEE Transactions on Multimedia, 20(9):2316-2329, 2018
35.J Cheng, S Jia, L Ying, Y Liu, S Wang, Y Zhu, Y Li, C Zou, X Liu, D Liang*, Improved parallel image reconstruction using feature refinement, Magnetic Resonance in Medicine, 80 (1):211-223, 2018（影响因子4.668）
36.Y Zhu, X Peng, Y Wu, E Wu, L Ying, X Liu, H Zheng, D Liang*, Direct diffusion tensor estimation using model-based method with spatial and parametric constraints, Medical Physics, 44:570-580, 2017
37.Y Zhu#, Y Liu#, L Ying, X Peng, Y J Wang, X Liu, D Liang*, SCOPE: signal compensation for low-rank plus sparse matrix decomposition for fast parameter mapping, Physics in Medicine and Biology, 63 (18): 185009, 2018 （影响因子3.609）
38.X Peng, L Ying, Y Liu, J Yuan, X Liu, D Liang*, Accelerated exponential parameterization of T2 relaxation with model-driven low rank and sparsity priors (MORASA), Magnetic Resonance in Medicine, 76(6):1865-1878, 2016
39.S Wang, J Liu, Q Liu, L Ying, X Liu, H Zheng, D Liang*, Iterative feature refinement for accurate undersampled MR image reconstruction, Physics in Medicine and Biology, 61(9):3291-3316, Highlights of 2016, 2016.
40.Z Hu, Y Zhang, J Liu, J Ma, H Zheng, D Liang*, A feature refinement approach for statistical interior CT reconstruction, Physics in Medicine and Biology, 61(14):5311-5334, 2016
