Real-time organ motion management in MRI-guided radiotherapy
type: CreativeWork
description: 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 th…
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 th…
author: Mirzapourrezaei, Seyedali
date published: 2019-12
date modified: 2026-07-24T06:06:34Z
provider: Wichita State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Wichita State University