Uncertainties in Modeling, Prediction, and Actions in Response to Variations in Patient Geometric Models.

Abstract

External beam radiation therapy is an effective method for treating cancer in many body sites. Highly conformal plans can be created to provide good target coverage while sparing the surrounding normal tissue. A fundamental problem in delivering these conformal plans is the inter- and intra-fractional variations in patient geometry, which result in deviation of the delivered dose from the planned dose, thus reducing the probability of tumor control or increasing the risk of normal tissue toxicity. To address this problem, various motion management strategies have been implemented in the clinic, and several others are under investigation. While the technique employed for management of geometric variation can change depending on the type and source of the variation (set up error, respiratory-induced motion and deformation, or tumor shrinkage or tissue loss in response to treatment) as well as other clinical factors, all these techniques have one thing in common and that is the fact that they are not perfect. This work investigates the uncertainties associated with the measurement and management of motion and deformation, and evaluates the impact of these uncertainties on the accuracy of geometry and dose tracking for treatment adaptation. This research quantified the magnitude and distribution of error in deformable image registration for aligning image volumes acquired at different breathing states. It further explored the potential of reducing the registration error in deforming lung geometry, by applying a method from multivariate statistics (principal component analysis) to identify the significant modes of variation in this geometry. It also demonstrated the potential for tracking respiratory induced deformation in various regions in the lung, using a few surrogates such as implanted markers. In addition to the evaluation of registration error for thoracic geometry affected by respiratory motion, this work also investigates the accuracy of deformable image registration in tracking the geometric changes observed in response to treatment (e.g. weight loss, tumor shrinkage, or swelling) in head and neck cancer patients. It explores the impact of registration error on tracking geometric and dosimetric changes over the course of treatment, and demonstrates the implications of these uncertainties for plan modification and adaptation.Ph.D.Nuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/63745/1/rkashani_1.pd