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Title:Gradient light interference microscopy for imaging strongly scattering samples
Author(s):Kandel, Mikhail Eugene
Director of Research:Popescu, Gabriel
Doctoral Committee Chair(s):Popescu, Gabriel
Doctoral Committee Member(s):Anastasio, Mark; Kong, Hyunjoon; Song, Pengfei
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Microscopy, Label-Free Imaging, Quantitative Phase Imaging, QPI, Phase Contrast, Differential Interference Contrast, DIC, Gradient Light Interference Microscopy, Phase Imaging with Computational Specificity, PICS, Deep Convolutional Neural Networks, CNN, Assisted Reproductive Technology, ART, Interferometry
Abstract:A growing interest in three-dimensional cellular systems has raised new challenges for light microscopy. The fundamental difficulty is the tendency for the optical field to scramble when interacting with turbid media, leading to contrast images. In this work, we outline the development of an instrument that uses broadband optical fields in conjunction with phase-shifting interferometry to extract high-resolution and high-contrast structures from otherwise cloudy images. We construct our system from a differential interference contrast microscope, demonstrating our new modality in transmission and reflection geometries. We call this modality Gradient Light Interference Microscopy (GLIM) as the image measures the gradient of the object’s scattering potential. To facilitate complex experiments, we develop a high-throughput acquisition software and propose several ways to analyze this new kind of data using deep convolutional neural networks. This new proposal, termed phase imaging with computational specificity (PICS), allows for non-destructive yet chemically motivated annotation of microscopy images. The results presented in this dissertation provide templates that are readily extendible to other quantitative phase imaging modalities.
Issue Date:2020-07-29
Type:Thesis
URI:http://hdl.handle.net/2142/109314
Rights Information:© 2020 Mikhail E. Kandel
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12


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