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Real-time organ motion management in MRI-guided radiotherapy
Mirzapourrezaei, Seyedali
Mirzapourrezaei, Seyedali
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dissertation
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2019-12
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Abstract
Radiotherapy is one of the most commonly used modalities for cancer treatment. The goal of
radiotherapy is to deliver a therapeutic dose of radiation to the clinical target volume while sparing
the surrounding healthy tissue to the largest extent possible. However, internal organ motion during
radiation delivery may lead to underdosing of cancer cells or overdosing of normal tissue, potentially
causing treatment failure or normal-tissue toxicity. Organ motion is of particular concern
in the treatment of lung and abdominal cancers, where respiratory-induced motion causes large
tumor displacement and organ deformation. A new generation of radiotherapy devices is equipped
with on-board MRI scanners to visualize the internal organ motion of patients in real time during
radiation delivery. The cine MRI acquired during radiation delivery provides an opportunity to
learn the patient-specific anatomical trajectory, which, in turn, can be used to estimate the delivered
dose, predict the future trajectory, and update the RT plan accordingly. Despite the use of
different mathematical modeling and optimization approaches for intrafraction motion management
in the literature, the previously developed approaches are not designed to take advantage of
the real-time anatomical information to continuously monitor the dose delivery and, if warranted,
correct for any potential dose discrepancies. In this dissertation, we develop a closed-loop control
framework in which the deposited radiation dose is continuously estimated using real-time MRI
information. This yields a feedback signal that employ the real-time MRI information to update
the treatment plan during radiation delivery. We develop motion predictive models that employ
the real-time MRI information to predict the short-term trajectory of the relevant anatomy during
radiation delivery. Finally, we develop a dynamic (re-)optimization framework to continuously
update the plan for the remainder of the treatment session based on the cumulative dose delivered
so far. The performance of the proposed methods were tested on de-identified clinical cancer cases
previously treated with MRI-guided radiotherapy at a collaborating cancer center.
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Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems and Manufacturing Engineering
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Wichita State University
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Copyright 2019 by Seyedali Mirzapourrezaei
All Rights Reserved