Technical Development, Optimization, and Clinical Implementation of Quantitative Magnetic Resonance Imaging Methods for Radiation Oncology

Lucas Bailey McCullum, BS, BA (Advisor: Clifton D. Fuller, MD, PhD)

             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 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.

Advisory Committee:

• Clifton D. Fuller, MD, PhD, Chair
• M. Alex Dresner, PhD
• Dan Ma, PhD
• Suprateek Kundu, PhD
• R. Jason Stafford, PhD
• Ergys D. Subashi, PhD

Join via Zoom (Please contact Mr. McCullum for his Zoom meeting info.)