Gradient light interference microscopy for imaging strongly scattering samples

Kandel, Mikhail Eugene

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https://hdl.handle.net/2142/109314 Copy

Description

Title

Gradient light interference microscopy for imaging strongly scattering samples

Author(s)

Kandel, Mikhail Eugene

Issue Date

2020-07-29

Director of Research (if dissertation) or Advisor (if thesis)

Popescu, Gabriel

Doctoral Committee Chair(s)

Popescu, Gabriel

Committee Member(s)

• Anastasio, Mark
• Kong, Hyunjoon
• Song, Pengfei

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Date of Ingest

2021-03-05T21:33:12Z

Keyword(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.

Graduation Semester

2020-12

Type of Resource

Thesis

Permalink

http://hdl.handle.net/2142/109314

Copyright and License Information

© 2020 Mikhail E. Kandel

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