Advancing Biological BCARS Imaging: Simulation-Based Optimization, and Machine Learning Analysis
Author(s)
Dixon, Jessica
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
This dissertation describes the development of a Broadband Coherent Anti-Stokes Raman Scattering (BCARS) microscope system, intended for imaging biological samples, such as single cells and tissue slices. Additionally, several algorithms and computational methods are presented to aid in the extraction and processing of the BCARS data and to generate simulated data to evaluate these approaches reliably. This work is divided into six chapters. Chapter 1 describes the theory of spontaneous and coherent Raman spectroscopy and describes several applications of these approaches. Chapter 2 demonstrates the use of several machine learning techniques to distinguish antibiotic resistance from Raman spectra of bacteria and extract the significant spectral features used to make this distinction by the models. Chapter 3 outlines the design, performance, and specifications of the BCARS microscope. Chapter 4 describes the creation and use of an experimentally based simulated tissue image dataset designed to evaluate noise and background removal methods applied to BCARS data. Chapter 5 presents the initial approach to using BCARS microscopy in conjunction with immunofluorescence labeling to analyze fixed prostate cancer cells. Chapter 6 summarizes the findings and concludes the dissertation.
Sponsor
Date
2025-04-25
Extent
Resource Type
Text
Resource Subtype
Dissertation