Y. Xue*, S. Cheng, Y. Li, L. Tian, “Towards reliable deep learning based phase microscopy”, SPIE Photonics West BIOS: Quantitative Phase Imaging, CA, Feb. 2020.
Tahir*, J. Zhu, S. Kura, X. Cheng, R. Damseh, F. Lesage, S. Sakadžic, D. Boas, L. Tian, “A generalizable deep-learning approach to anatomical modeling of brain vasculature”, SPIE Photonics West BIOS: Neural Imaging and Sensing, San Francisco, CA, Feb. 2020.
A. Matlock*, J. Li, L. Tian, “Computational illumination for high-throughput intensity diffraction tomography of dynamic biological samples”, SPIE Photonics West BIOS: Quantitative Phase Imaging, CA, Feb. 2020.
Lei Tian, “Deep learning based computational microscopy in scattering media”, SPIE Photonics West BIOS: Adaptive Optics and Wavefront Control for Biological Systems conference, San Francisco, CA, Feb. 2020. (Invited)
Lei Tian, “Towards Scalable and Reliable Deep learning based Phase Microscopy using Intensity-only Measurements”, SPIE Photonics West BIOS: Quantitative Phase Imaging conference, San Francisco, CA, Feb. 2020. (Invited)
“High-speed in vitro intensity diffraction tomography,” Adv. Photon. 1(6) 066004, doi 10.1117/1.AP.1.6.066004)
Lei Tian joined the editorial board of IEEE Transactions on Computational Imaging as an Associate Editor.
Joe defends his MS thesis on "Pupil engineering in a miniaturized fluorescent microscopy platform using binary diffractive optics". Congratulations!
Learning speckle correlations for imaging through scattering
Time: 10:30 AM - 10:50 AM
Author(s): Yunzhe Li, Yujia Xue, Lei Tian, Boston Univ. (United States)
Multiplexed Intensity Diffraction Tomography (mIDT) for Dynamic, Label-Free Volumetric Biological Imaging
A Matlock, J Yi, L Tian - Novel Techniques in Microscopy, 2019
A Deep Learning Approach to 3D Segmentation of Brain Vasculature
W Tahir, J Zhu, S Kura, X Cheng, D Boas, L Tian - Optics and the Brain, 2019
This CAREER program aims to develop novel optical imaging devices that fully utilize multiple scattering to enable high-resolution imaging in highly scattering media. Scattering in complex media is a fundamental subject that continuously attracts theoretical and experimental endeavors, since it impacts many important applications. To date, solving the inverse-scattering problem remains difficult due to the many degrees of freedom in multiple scattering, and incomplete information limited by the measurement condition. To overcome these limitations, this program will focus on (a) novel physical models and inverse multiple scattering algorithms in both transmission and reflection using intensity-only tomographic measurement; and (b) innovative spatial coherence and multispectral illumination engineering solutions. The anticipated outcomes include: i) new multiple scattering-based theory, algorithms, and devices that can lead to high-resolution 3D imaging of highly scattering objects; ii) theoretical and experimental advancement to 3D phase retrieval in both transmission and reflection tomography; iii) new device design principles for adaptive coherence engineering to harness the multiple scattering information. Broadly, the program will establish fundamentally new understandings of label-free, scatter-based imaging and develop new generation of computational imaging sensors and devices by jointly designing optics and algorithms to break the conventional limits.
Read more here.
Alex Matlock, Anne Sentenac, Ji Yi, Lei Tian, “Intensity-only reflection quantitative phase imaging for biological sample characterization”, SPIE Photonics West BIOS: Quantitative Phase Imaging conference, San Francisco, CA, Feb. 2019.
Yujia Xue, Yunzhe Li, Lei Tian, “A deep learning approach to high space-bandwidth product phase microscopy with coded illumination”, SPIE Photonics West BIOS: Quantitative Phase Imaging conference, San Francisco, CA, Feb. 2019.
Yunzhe Li, Yujia Xue, Lei Tian, “A one-for-all deep learning approach for imaging through diffusers”, SPIE Photonics West BIOS: Adaptive Optics and Wavefront Control for Biological Systems conference, San Francisco, CA, Feb. 2019.
Lei Tian, Yujia Xue, Yunzhe Li, Shiyi Cheng, “Deep learning computational microscopy”, Computational Imaging workshop, the Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University, Mar. 2019. *Invited