Gold nanoparticles (GNPs) have recently attracted considerable interest for use in radiation therapy due to their unique physical and biological properties. Of interest, GNPs (and other high-atomic-number materials) have been used to enhance radiation dose in tumors by taking advantage of increased photoelectric absorption. This physical phenomenon is well-understood on a macroscopic scale. However, biological outcomes often depend on the intratumoral and even intracellular distribution of GNPs, among other factors. Therefore, there exists a need to precisely visualize and accurately quantify GNP distributions. By virtue of the photoelectric effect, x-ray fluorescence (XRF) photons (characteristic x-rays) from gold can be induced and detected, not only allowing the distribution of GNPs within biological samples to be determined but also providing a unique molecular imaging option in conjunction with bioconjugated GNPs. This work proposes the use of this imaging modality, known as XRF imaging, to develop experimental imaging techniques for detecting and quantifying sparse distributions of GNPs in preclinical settings, such as within small-animal-sized objects, tissue samples, and superficial tumors. By imaging realistic GNP distributions, computational methods can then be used to understand radiation dose enhancement on an intratumoral scale and perhaps even down to the nanoscopic, subcellular realm, elucidating observed biological outcomes (e.g., radiosensitization of tumors) from the bottom-up. Ultimately, this work will result in experimental and computational tools for developing a better understanding of GNP-mediated dose enhancement and associated radiosensitization within the scope of GNP-aided radiation therapy.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/54877 |
| Date | 27 May 2016 |
| Creators | Manohar, Nivedh Harshan |
| Contributors | Cho, Sang H., Wang, Chris K. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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