1.  智能显微计算成像：基于深度学习/压缩感知等光学显微信息获取与成像应用

2.  生物、医学光学：活体/离体快速三维成像技术、病理/目标成分的智能分析诊断等

   

070207-光学

智能显微计算成像

2013-09--2018-06   西安交通大学   工学博士

   

2020-11~现在, 中国科学院西安光学精密机械研究所, 副研究员
2018-07~2020-10,中国科学院西安光学精密机械研究所, 博士后

   

（1） 中国科学院“青年创新促进会”会员, 部委级, 2021

（ 1 ） 基于马尔科夫链蒙特卡洛多目标追踪的经颅超声脑血管造影超分辨成像方法, 发明专利, 2021, 第 2 作者, 专利号: 201711209041.1

（ 2 ） 基于深度学习的宽场彩色光切片显微成像方法, 发明专利, 2020, 第 1 作者, 专利号: 2020101172744

（ 3 ） 经颅低频超声线性调频脉冲逆转微泡成像方法, 发明专利, 2019, 第 2 作者, 专利号: 20172090400.5

（ 4 ） 基于特征空间分解的多重変迹快速自适应波束合成方法, 发明专利, 2019, 第 2 作者, 专利号: 201710209759.4

（ 5 ） 基于六轴机械臂的高强度聚焦超声治疗引导和控制系统及方法, 发明专利, 2018, 第 3 作者, 专利号: 201510158723.9

（ 6 ） 超声二维面阵的三维宽波束小区域快速空化成像方法, 发明专利, 2016, 第 2 作者, 专利号: 201510187962.7

（ 7 ） 三维超声引导的诊疗系统软件v 1.0, 发明专利, 2016, 第 3 作者, 专利号: 2016SR249265

（ 8 ） 一种基于压缩感知自适应波束合成的脉冲逆转谐波平面波快速造影成像方法, 发明专利, 2016, 第 3 作者, 专利号: 201510567122.3

（ 9 ） 压缩自适应波束合成的平面波超声成像和微泡成像的方法与系统, 发明专利, 2016, 第 2 作者, 专利号: 201510079932.4

   

[1] Peng, Tong, Li, Runze, Min, Junwei, Dan, Dan, Zhou, Meiling, Yu, Xianghua, Zhang, Chunmin, Bai, Chen, Yao, Baoli. Quantitative Phase Retrieval Through Scattering Medium via Compressive Sensing. IEEE PHOTONICS JOURNAL[J]. 2022, 14(1): http://dx.doi.org/10.1109/JPHOT.2021.3136509.
[2] Zhou, Meiling, Bai, Chen, Zhang, Yang, Li, Runze, Peng, Tong, Qian, Jia, Dan, Dan, Min, Junwei, Zhou, Yuan, Yao, Baoli. Deep-Learning-Based Rapid Imaging Through Scattering Media Beyond the Memory Effect. IEEE PHOTONICS TECHNOLOGY LETTERS[J]. 2022, 34(5): 295-298, http://dx.doi.org/10.1109/LPT.2022.3153665.
[3] Liu, Chao, Yu, Xianghua, Bai, Chen, Li, Xing, Zhou, Yuan, Yan, Shaohui, Min, Junwei, Dan, Dan, Li, Runze, Gu, Shuangyu, Yao, Baoli. Axial resolution enhancement for planar Airy beam light-sheet microscopy via the complementary beam subtraction method. APPLIED OPTICS[J]. 2021, 60(32): 10239-10245, http://dx.doi.org/10.1364/AO.441070.
[4] Liu, Chao, Bai, Chen, Yu, Xianghua, Yan, Shaohui, Zhou, Yuan, Li, Xing, Min, Junwei, Yang, Yanlong, Dan, Dan, Yao, Baoli. Extended field of view of light-sheet fluorescence microscopy by scanning multiple focus-shifted Gaussian beam arrays. OPTICS EXPRESS[J]. 2021, 29(4): 6158-6168, http://dx.doi.org/10.1364/OE.418707.
[5] Bai, Chen, Peng, Tong, Min, Junwei, Li, Runze, Zhou, Yuan, Yao, Baoli. Dual-wavelength in-line digital holography with untrained deep neural networks. 光子学研究:英文版[J]. 2021, 9(12): 2501-2510, http://lib.cqvip.com/Qikan/Article/Detail?id=7106739968.
[6] Bai, Chen, Qian, Jia, Dang, Shipei, Peng, Tong, Min, Junwei, Lei, Ming, Dan, Dan, Yao, Baoli. Full-color optically-sectioned imaging by wide-field microscopy via deep-learning. BIOMEDICAL OPTICS EXPRESS[J]. 2020, 11(5): 2619-2632, https://www.webofscience.com/wos/woscc/full-record/WOS:000532568000024.
[7] 于湘华, 刘超, 柏晨, 杨延龙, 彭彤, 但旦, 闵俊伟, 姚保利. 光片荧光显微成像技术及应用进展. 激光与光电子学进展. 2020, 57(10): 9-23, http://lib.cqvip.com/Qikan/Article/Detail?id=7102131733.
[8] Bai, Chen, Yu, Xianghua, Peng, Tong, Liu, Chao, Min, Junwei, Dan, Dan, Yao, Baoli. 3D Imaging Restoration of Spinning-Disk Confocal Microscopy Via Deep Learning. IEEE PHOTONICS TECHNOLOGY LETTERS[J]. 2020, 32(18): 1131-1134, http://dx.doi.org/10.1109/LPT.2020.3014317.
[9] Bai, Chen, Liu, Chao, Yu, Xianghua, Peng, Tong, Min, Junwei, Yan, Shaohui, Dan, Dan, Yao, Baoli. Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning. IEEE PHOTONICS TECHNOLOGY LETTERS[J]. 2019, 31(22): 1803-1806, http://dx.doi.org/10.1109/LPT.2019.2948030.
[10] Bai, Chen, Zhou, Meiling, Min, Junwei, Dang, Shipei, Yu, Xianghua, Zhang, Peng, Peng, Tong, Yao, Baoli. Robust contrast-transfer-function phase retrieval via flexible deep learning networks: publisher's note (vol 44, pg 5141, 2019). OPTICS LETTERSnull. 2019, 44(22): 5561-5561, [11] Bai, Chen, Zhou, Meiling, Min, Junwei, Dang, Shipei, Yu, Xianghua, Zhang, Peng, Peng, Tong, Yao, Baoli. Robust contrast-transfer-function phase retrieval via flexible deep learning networks. OPTICS LETTERS[J]. 2019, 44(21): 5141-5144, http://dx.doi.org/10.1364/OL.44.005141.
[12] Bai, Chen, Liu, Chao, Jia, Hao, Peng, Tong, Min, Junwei, Lei, Ming, Yu, Xianghua, Yao, Baoli. Compressed Blind Deconvolution and Denoising for Complementary Beam Subtraction Light-Sheet Fluorescence Microscopy. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING[J]. 2019, 66(10): 2979-2989, https://www.webofscience.com/wos/woscc/full-record/WOS:000487192000030.
[13] Zhong Hui, Wan Mingxi, Bai Chen, Zhang Xinyu, Qiao Xiaoyang, Sang Yuchao. Ultrasound Transcranial Imaging Based on Fast Coherent-Time-Delay and Correlative Pixel-Based Beamforming. 2018 IEEE International Ultrasonics Symposium, IUS 2018null. 2018, 2018-October: http://ir.opt.ac.cn/handle/181661/31239.
[14] Bai, Chen, Ji, Meiling, Bouakaz, Ayache, Zong, Yujin, Wan, Mingxi. Design and Characterization of an Acoustically and Structurally Matched 3-D-Printed Model for Transcranial Ultrasound Imaging. IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL[J]. 2018, 65(5): 741-748, https://www.webofscience.com/wos/woscc/full-record/WOS:000431693800007.
[15] 柏晨. Ultrasound Transcranial Imaging with Coherent Compounding of Diverging Chirp Waves and Low Frequencies. 2018 IEEE International Ultrasonics Symposium (IUS). 2018, [16] Bai, Chen, Xu, Shanshan, Duan, Junbo, Jing, Bowen, Yang, Miao, Wan, Mingxi. Pulse-Inversion Subharmonic Ultrafast Active Cavitation Imaging in Tissue Using Fast Eigenspace-Based Adaptive Beamforming and Cavitation Deconvolution. IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL[J]. 2017, 64(8): 1175-1193, [17] Ding, Ting, Hu, Hong, Bai, Chen, Guo, Shifang, Yang, Miao, Wang, Supin, Wan, Mingxi. Spatial-temporal three-dimensional ultrasound plane-by-plane active cavitation mapping for high-intensity focused ultrasound in free field and pulsatile flow. ULTRASONICS[J]. 2016, 69: 166-181, http://dx.doi.org/10.1016/j.ultras.2016.04.010.

（1） Cavitation Imaging in Tissues. In: M. Wan, F. Feng, and G. T. Haar (Eds.). Cavitation in Biomedicine: Principles and Techniques., Springer, 2015-12, 第 3 作者
（2） Beamfroming and control. In: M. Wan, Y. Feng, and S. Lu (Eds.). Computational Biomedical Ultrasound., Science Press, 2018-12, 第 1 作者

   

（ 1 ） 中科院青年创新促进会-智能光学显微成像研究, 负责人, 中国科学院计划, 2021-04--2024-12
（ 2 ） 国家自然科学基金青年项目-基于深度学习/压缩感知网络的大视场三维散射计算成像研究, 负责人, 国家任务, 2020-01--2022-12
（ 3 ） 陕西省重点研发计划-基于深度学习的三维大视场计算显微成像系统研究, 负责人, 地方任务, 2020-01--2021-12
（ 4 ） 西安市博士后创新基地资助-基于计算成像的三维光学显微成像系统研究, 负责人, 地方任务, 2020-01--2021-12
（ 5 ） 中国博士后科学基金面上资助-深层卷积压缩感知框架下的三维彩色散射计算成像研究, 负责人, 国家任务, 2019-11--2020-12
（ 6 ） 计算成像技术及其在光学显微成像中的应用研究, 负责人, 研究所自选, 2019-08--2020-12
（ 7 ） 经颅三维动态超声微泡与空化成像技术及诊疗应用, 参与, 国家任务, 2016-01--2018-12
（ 8 ） 国家自然科学基金面上项目-基于物理增强深度神经网络的高分辨三维定量相位显微成像研究, 负责人, 国家任务, 2023-01--2026-12

（1）Quantitative phase retrieval through scattering media   Chen Bai, Tong Peng, Runze Li, Junwei Min, Xianghua Yu, and Baoli Yao   2021-11-14
（2）基于压缩感知和深度学习的智能光学显微成像   云影天光   柏晨   2021-10-12
（3）计算成像技术在光学显微成像中的应用研究   光学前沿在线：光学成像与显示   2020-06-19
（4）Robust phase retrieval from single-distance coherent diffraction images by combining compressive sensing and deep learning approaches   2019-10-20
（5）Ultrasound Transcranial Imaging based on Fast Coherent-time-delay and Correlative Pixel-based Beamforming   2018-10-22
（6）Ultrasound Transcranial Imaging based on Fast Coherent-time-delay and Correlative Pixel-based Beamforming   2018-10-22