Defense Date


Document Type


Degree Name

Doctor of Philosophy


Medical Physics

First Advisor

Geoffrey D. Hugo


To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed “blocks”, are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.


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Date of Submission

May 2013