刘东，中国科学技术大学研究员，博士生导师，ieee高级会员。主要从事数据反演、深度学习、医学电阻抗成像、正电子断层成像技术等方面的研究。在电磁反问题、无损医学成像算法以及其在生物医学工程中的应用等重要课题方面开展了大量研究。成功开发出对模型误差、噪声等具有高容差性的集动态与静态成像于一体的非线性差分成像方法。在ieee tpami (1篇)、ieee tmi (10篇)、ieee tim (6篇)、ieee tbme (2篇)和ieee tci (1篇)等期刊上发表论文40余篇。曾获总装备部科技进步二等奖一项。
招收：数学、计算机、物理、电子类等专业积极主动的优秀学生。 数据/模型驱动的智能与影像技术开发 应用于生物医学的数学物理模型开发 电磁计算、反演问题、人工/机器智能 无损电磁成像系统设计与应用开发 生物医学电磁成像技术 正电子发射断层成像技术 模型驱动的医学图像重建方法 数据驱动的高分辨医学图像重建
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2 | 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 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2022-12，723万元，结题，课题负责人 |
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, 2023. doi 10.1109/tpami.2023.3240565 q shan, j wang and d liu. deep image prior based pet reconstruction from partial data. ieee transactions on radiation and plasma medical sciences, 2023. doi 10.1109/trpms.2023.3280674 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. 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. 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. 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 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. 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. 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. 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. 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. 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. 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. 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. 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 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 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 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 d smyl and d liu. less is often more: applied inverse problems using hp-forward models. journal of computational physics, 399(108949), 2019 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 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 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. 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 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 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. 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) 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.
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