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

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

电子邮箱

dong2016@ustc.edu.cn

dong.liu@outlook.com

主持的主要研项目

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)