Author ORCID Identifier

0000-0001-9788-7987

Date of Graduation

5-2026

Document Type

Dissertation (PhD)

Program Affiliation

Medical Physics

Degree Name

Doctor of Philosophy (PhD)

Advisor/Committee Chair

Clifton D. Fuller MD, PhD

Committee Member

Alex Dresner PhD

Committee Member

Suprateek Kundu PhD

Committee Member

Dan Ma PhD

Committee Member

R. Jason Stafford PhD

Committee Member

Ergys Subashi PhD

Abstract

Magnetic resonance imaging (MRI) has gradually grown more important in the field of radiation oncology. This paradigm shift has grown from initial experiments with MRI of cancerous lesions in the early 1970s, to the integration of MRI images for radiation therapy treatment planning (i.e., MRI-Simulation) in the early 1980s, to the combined MRI linear accelerator (MR-Linac) becoming commercially available in 2018 which can simultaneously image a tumor and treat it with radiation. However, due to its novelty in radiation oncology, its full potential for the integration of quantitative MRI-based biomarkers has yet to be fully realized.

Therefore, this dissertation will provide a framework for this integration from the initial technical developments, to the subsequent optimization, to the ultimate clinical implementation in viii the radiation oncology workflow. This work will be presented in three distinct aims: 1) the technical development of quantitative MRI techniques in radiation oncology, 2) the technical optimization of these techniques for site-specific applications, and 3) their clinical implementation and potential use cases.

Several techniques will be explored for quantitative T1, T2, PD, R2*, proton density fat fraction (PDFF), and triglyceride composition mapping. Most of the techniques presented will acquire multiple of these quantitative parameters simultaneously due to the unique time constraints in radiation oncology, especially on the integrated MR-Linac.

Keywords

MRI, quantitative, radiation therapy, MR-Linac, image-guided, cancer, multiparametric, optimization, clinical, implementation

Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.