生物医学工程
-
刘东
长聘研究员、博士生导师
刘东，中国科学技术大学苏州高等研究院长聘研究员，博士生导师，IEEE高级会员。主要从事数据反演、深度学习、功能成像技术等方面的研究。在IEEE TPAMI (2篇)、IEEE TMI (12篇)、IEEE TIM (8篇)、IEEE TBME (2篇)和IEEE TCI (4篇)等期刊上发表论文50余篇。曾获总装备部科技进步二等奖一项。
招收：物理、数学、计算机、电子类等专业积极主动的优秀学生。
工作单位：中国科学技术大学苏州高等研究院、中国科学技术大学生物医学工程学院
E-mail: dong2016(at)ustc.edu.cn；dong.liu(at)outlook.com
研究方向：
数据/模型驱动的智能影像技术开发
量子传感数据反演、人工/机器智能
在研课题:
深度功能成像技术研究
量子精密测量数据反演技术研究
基于AIGC的图像重建方法研究
主持的主要科研项目：
1.AI赋能高维度光学信息处理，科技委高技术项目，2024.12-2027.12，主持
2.基于深度神经网络的电阻抗图像重建方法研究，基金委面上项目，62371433，2024.01-2027.12，主持
3.基于参数化多相水平集方法的电阻抗成像算法研究，基金委面上项目61871356，2019.01-2022.12，结题，主持
4.面向固态量子传感的自旋系综测量与调控装置，国家重大科研仪器研制项目，51727808，2018-01至2022-12，结题，课题负责人
代表性研究论文：
1. B Tong, H Chen, S Guo and D Liu. Learned Regularization for Microwave Tomography. IEEE Transactions on Antennas & Propagation, In press, 2026.
2. C Wang, H Deng and D Liu. Physics-Driven Neural Compensation For Electrical Impedance Tomography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 48(3):3783-3800, 2026.
3. D. Liu, H Xia, C Wang, H Xiang, Y Huang and S Zhou. GSR: A Gaussian Splatting-Based Reconstruction Framework for EIT. IEEE Transactions on Medical Imaging, In press, 2026.
4. D. Liu, Y Wu, B Tong and J Deng. SDEIT: Semantic-Driven Electrical Impedance Tomography, Neural Networks, 197:108492, 2026
5. S Ashraf, Q Shan, W Ning and D Liu. Boosting PET reconstruction with Positional Encoding based deep image prior. Philosophical Transactions A, 383 (2305): 20240049, 2025.
6. B Tong, J Wang and D Liu. Diff-INR: Generative Regularization for Electrical Impedance Tomography, DOI: 10.48550/arXiv.2409.04494
7. J Wang, J Deng and D Liu. Deep prior embedding method for Electrical Impedance Tomography. Neural Networks, 188(2025): 107419
8. Z Liu, J Wang，Q Shan and D Liu. GraphEIT: Unsupervised Graph Neural Networks for Electrical Impedance Tomography. IEEE Transactions on Computational Imaging, 10:1559-1570, 2024.
9. H Xia, Q Shan, J Wang and D Liu. NAS powered deep image prior for electrical impedance tomography. IEEE Transactions on Computational Imaging, 10:1165-1174, 2024.
10. Q Shan, J Wang and D Liu. Deep Image Prior Based PET Reconstruction From Partial Data. IEEE Transactions on Radiation and Plasma Medical Sciences, 8(4):416-425,2024.
11. J Wang, Y Wang, J Deng and D Liu. Unsupervised Coordinate-Based Neural Network for Electrical Impedance Tomography. IEEE Transactions on Computational Imaging, 9:1213-1225, 2023.
12. D Liu, J Wang, Q Shan, D Smyl, J Deng and J F Du. DeepEIT: deep image prior enabled electrical impedance tomography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):9627-9638, 2023.
13. Y Song, Y Wang and D Liu. A nonlinear weighted anisotropic total variation regularization for electrical impedance tomography. IEEE Transactions on Instrumentation and Measurements, 71:4010713, 2022.
14. Z Lin, R Guo, K Zhang, M Li, F Yang, S Xu, D Liu and A Abubakar. Feature-Based Inversion Using Variational Autoencoder for Electrical Impedance Tomography. IEEE Transactions on Instrumentation and Measurement, 71:4504712, 2022.
15. D Gu, J Deng, D Smyl, D Liu and J F Du. Supershape Augmented Reconstruction Method Based on Boolean Operations in Electrical Impedance Tomography, IEEE Transactions on Instrumentation and Measurement, 70: 4507111, 2021.
16. D Liu and J F Du. Shape and topology optimization in electrical impedance tomography via moving morphable components method. Structural and Multidisciplinary Optimization, 64:585-598, 2021.
17. D Smyl, L Chen, L Li and D Liu. Non-cooperative finite element games, Applied Numerical Mathematics, 167: 273-280, 2021.
18. D Gu, D Liu, D Smyl, J Deng and J F Du. Supershape recovery from electrical impedance tomography data, IEEE Transactions on Instrumentation and Measurement, 70: 4503711, 2021.
19. L Chen, A Gallet, S Huang, D Liu and D Smyl. Probabilistic Cracking Prediction via Deep Learned Electrical Tomography. Structural Health Monitoring, DOI:10.1177/14759217211037236
20. D Smyl, T N Tallman, J A Black, A Hauptmann and D Liu. (2021). Learning and correcting non-Gaussian model errors. Journal of Computational Physics, 432: 110152, 2021.
21. D Smyl, T Tallman, D Liu and A Hauptmann. An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values, IEEE Signal Processing Letters, 28: 748-752, 2021.
22. D Liu, D Gu, D Syml, A Khambampati, J Deng and J F Du. Shape-driven EIT reconstruction using Fourier representations. IEEE Transactions on Medical Imaging, 40(2), 481-490, 2021.
23. W Tian, P Suo, D Liu, S Sun, J Sun, L Xu. Simultaneous shape and permittivity reconstruction in ECT with sparse representation: two-phase distribution imaging. IEEE Transactions on Instrumentation and Measurement. 70: 4500414, 2021.
24. D Liu, D Syml, D Gu and J F Du. Shape-driven difference electrical impedance tomography. IEEE Transactions on Medical Imaging, 39(12), 3801-3812, 2020.
25. D Liu, D Gu, D Syml, J Deng and J F Du. Multiphase conductivity imaging with Electrical Impedance Tomography and B-spline level set method. IEEE Transactions on Instrumentation and Measurement, 69(12), 9634-9644, 2020.
26. D Liu, D Gu, D Syml, J Deng and J F Du. Shape reconstruction using Boolean operations in electrical impedance tomography. IEEE Transactions on Medical Imaging, 39(9), 2954-2964, 2020.
27. D Syml and D Liu, Optimizing Electrode Positions in 2-D Electrical Impedance Tomography Using Deep Learning. IEEE Transactions on Instrumentation and Measurement, 69(9), 6030-6044, 2020.
28. D Liu, D Gu, D Syml, J Deng and J F Du. B-Spline Level Set Method for Shape Reconstruction in Electrical Impedance Tomography. IEEE Transactions on Medical Imaging, 39(6), 1917-1929, 2020.
29. D Liu, D Syml and J F Du. Nonstationary shape estimation in electrical impedance tomography using a parametric level-set-based extended Kalman filter approach. IEEE Transactions on Instrumentation and Measurement, 69(5), 1894-1907, 2020.
30. D Smyl and D Liu. Self-filtering electrical area sensors emerging from deep learning. Measurement Science and Technology, 31(065107), 2020
31. Z Li, J Zhang, D Liu and J F Du. CT Image-Guided Electrical Impedance Tomography for Medical Imaging. IEEE Transactions on Medical Imaging, 39(6), 1822-1832, 2020.
32. D Smyl and D Liu. Invisibility and indistinguishability in structural damage tomography. Measurement Science and Technology, 31(024001), 2020
33. D Smyl, S Bossuyt, W Ahmad, A Vavilov and D Liu. An overview of 38 least squares-based frameworks for structural damage tomography. Structural Health Monitoring, 19(1), 215-239, 2020
34. D Liu and J F Du. A moving morphable components based shape reconstruction framework for electrical impedance tomography. IEEE Transactions on Medical Imaging, 38(12), 2937-2948, 2019
35. D Smyl and D Liu. Less is often more: Applied inverse problems using hp-forward models. Journal of Computational Physics, 399(108949), 2019
36. D Smyl and D Liu. Damage Tomography as a State Estimation Problem: Crack Detection Using Conductive Area Sensors. IEEE Sensors Letters, 3(10), 2501604, 2019
37. D Liu, D Gu, D Syml, J Deng and J F Du. B-spline based sharp feature preserving shape reconstruction approach for electrical impedance tomography. IEEE Transactions on Medical Imaging, 38(11), 2533-2544, 2019
38. S Ren, K Sun, D Liu and F Dong. A Statistical Shape Constrained Reconstruction Framework for Electrical Impedance Tomography. IEEE Transactions on Medical Imaging, 38(10), 2400-2410, 2019
39. Z Wei, D Liu and X Chen. Dominant-Current Deep Learning Scheme for Electrical Impedance Tomography. IEEE Transactions on Biomedical Engineering, 66(9), 2546-2555, 2019
40. D Liu, D Syml and J F Du. A Parametric Level Set based Approach to Difference Imaging in Electrical Impedance Tomography. IEEE Transactions on Medical Imaging, 38(1), 145-155,2019
41. D Smyl, S Bossuyt and D Liu. OpenQSEI: a MATLAB package for Quasi Static Elasticity Imaging. SoftwareX 9, 73–76, 2019
42. D Liu, Y X Zhao, A Khambampati, A Seppanen and J F Du. A parametric level set method for imaging multi-phase conductivity using electrical impedance tomography. IEEE Transactions on Computational Imaging, 4(4), 552-561, 2018.
43. D Smyl, S Bossuyt, D Liu, Stacked elasticity imaging approach for visualizing defects in the presence of background inhomogeneity, Journal of Engineering Mechanics. 145(1), 06018006, 2018
44. D Liu, A K Khambampati and J F Du. A Parametric Level Set Method for Electrical Impedance Tomography. IEEE Transactions on Medical Imaging, 37(2), 451-460, 2018
45. D Smyl, A Kim, D Liu, and S Bossuyt. Coupled digital image correlation and quasi-static elasticity imaging of inhomogeneous orthotropic composite structures. Inverse Problems 34(12), 2018.
46. A K Khambampati, D Liu, S K Konki and K Y Kim. An Automatic Detection of the ROI Using Otsu Thresholding in Nonlinear Difference EIT Imaging. IEEE Sensors Journal, 18(12), 5133-5142, 2018
47. D Liu, E Kankare, A-M Laukkanen and P Alku. Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types. Biomedical Signal Processing and Control, 36, 183-193, 2017
48. X Qin, L Wang, D Liu, Y X Zhao, X Rong and J F Du. A 1.15 ps Bin Size and 3.5 ps Single-Shot Preci-sion Time-to-Digital-Converter with On-Board Offset Correction in an FPGA. IEEE Transactions on Nuclear Science, 64(12), 2951-2957, 2017.
49. D Liu , V Kolehmainen, S Siltanen, A-M Laukkanen and A Seppänen. Non-linear difference imaging approach to three-dimensional electrical impedance tomography in the presence of geometric modeling error. IEEE Transactions on Biomedical Engineering, 63(9):1956-1965, 2016.
50. A Rashid, S Kim, D Liu and K Y Kim. A Dynamic Oppositional Biogeography-Based Optimization Approach for Time-Varying Electrical Impedance Tomography. Physiological measurement, 37(6) 2016: 820, 2016.
51. Y-W Wang, H Tang, D Wu, D Liu, Y-F Liu, A-N Cao and H-F Wang. Enhanced bactericidal toxicity of silver nanoparticles by the antibiotic gentamicin. Environ. Sci.: Nano, 3(4), 788-798, 2016
52. D Liu, V Kolehmainen, S Siltanen and A Seppänen, A non-linear approach to difference imaging in EIT; assessment of the robustness in the presence of modelling errors. Inverse Problems 31. 035012, 2015. (highlight article)
53. D Liu, V Kolehmainen, S Siltanen, A-M Laukkanen and A Seppänen. Estimation of conductivity changes in a region of interest with electrical impedance tomography. Inverse Problems and Imaging 9(1). 211-229. 2015.
