Leveraging Spectra to Improve the Resolution and Usability of Mid-Infrared Biomedical Microscopy: A Case Study in Bone Marrow Grading

Histological imaging is the current gold standard for disease diagnosis and prognosis. The current histological methods rely on techniques, such as chemical staining and pathological analysis, that are non-quantitative, subjective, and limited to specific biomarkers. Over the past decade, research has shown that spectroscopic imaging, when augmented with modern histology, may provide more precise measurements by overcoming variations in sample preparation and pathological expertise. The current mid-IR instrumentation though practical in a research environment, it is unsuitable for many clinical applications. Limitations of spectroscopic imaging include lengthy acquisition times, unmanageable data sizes, and diffraction limited spatial resolution. The goal of this dissertation is to study the application of mid-infrared (mid-IR) biomedical microscopy to improve usability in a clinical environment for histology. This is demonstrated by results for clinical analysis of bone marrow biopsies. This dissertation has three parts. In the first part, I explore methods for optimal feature selection as it is critical for understanding chemical differences between tissue types as well as leveraging new discrete-frequency imaging systems. In the second part, I explore the potential for improving the spatial resolution of IR imaging systems by designing an image-fusion method based on the curvelet transform. Finally, I demonstrate the clinical viability of IR imaging for bone marrow grading. Through this research, I propose a roadmap and a set of requirements for efficient translation of infrared spectroscopic imaging into a clinical setting for bone marrow grading. The final results demonstrate that mid-infrared spectroscopic imaging has the potential to serve as a quantitative diagnostic tool for treatment tracking and disease progression with a case study of bone marrow fibrosis.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Houston

Year dc:date.issued

2019

Author dc:creator

• Mankar, Rupali Dagadu

Advisor dc:contributor.advisor

• Mayerich, David

Committee members dc:contributor.committeemember

• Reddy, Rohith K.
• Prasad, Saurabh
• Eriksen, Jason
• Bueso-Ramos, Carlos E.

• Spectroscopy
• FTIR
• Feature selection
• Curveletes
• Bone marrow
• Machine learning

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/5826

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/5826