Quantitative optical elasticity imaging for biomechanics

Authors
Hugenberg, Nicholas
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Other Contributors
Oberai, Assad
Corr, David T.
Hicken, Jason
Picu, Catalin R.
Issue Date
2019-08
Keywords
Mechanical engineering
Degree
PhD
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
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Abstract
The field of elasticity imaging, or elastography, has undergone rapid development over the last three decades, evolving from using ultrasound for simple qualitative measurements of tissue stiffness to characterizing a wide variety of biomechanical properties. This growing capacity to analyze biological systems has driven parallel investigations into the origins of biological processes; indicators in large-scale structures, like the stiffening of the liver during liver disease and the production of hard tumors in cancer, show that mechanical interactions play an important role in disease pathology and in regulating the healthy functioning of the body. For a better understanding of these phenomena, we need a comprehensive view of biomechanical properties across many length scales, from organs all the way down to individual cells. Mechanical measurement techniques like ultrasound are limited in resolution and cannot operate over the whole of this range; for this flexibility, we turn to the family of optical measurement techniques.
We first study linear elastic properties in nanofibrous scaffolds using video elastography, at a scale of millimeters. By implementing de-noising methods we are able to increase the reliability of data gleaned from this technique, and find the non-uniform distribution of modulus across the scaffolds. Progressing down to the micrometer scale, we explore the use of Optical Coherence Tomography (OCT) for elastography, and both conduct a novel study of the modulus of stem cells and develop, implement and test a formulation to determine the nonlinear stiffening rate of tissue using synthetic data. Finally, we drop down another order of magnitude in scale using Traction Microscopy (TM) for a source identification problem, reconstructing the tractions applied by a cell to its surroundings. Synthetic data is used here to allow us to study the error induced by using incorrect models for the complex extra-cellular matrix. The cumulative results of these studies represent a novel contribution to the field of optical elasticity imaging.
This thesis aims to advance the state of the art in optical elastography by studying a variety of measurement techniques and enhancing their robustness and accuracy, and by increasing the scope of material properties we can quantify. We perform four separate studies to accomplish these goals, beginning with large scales, linear material properties, and simple constitutive models and progressing to very small scales, working with nonlinear properties and models that are motivated by tissue microstructure.
Description
August 2019
School of Engineering
Department
Dept. of Mechanical, Aerospace, and Nuclear Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.