Defense Date

2016

Document Type

Thesis

Degree Name

Doctor of Philosophy

Department

Medical Physics

First Advisor

Geoffrey Hugo

Abstract

Respiration introduces large tumor motion in the thoracic region which influences treatment outcome for lung cancer patients. Tumor motion management techniques require characterization of temporal tumor motions because tumor motion varies patient to patient, day to day and cycle to cycle. This work develops a markerless algorithm to estimate 3 dimensional (3D) lung-tumor trajectories on free breathing cone beam computed tomography (CBCT) projections, which are 2 dimensional (2D) sequential images rotating about an axis and are used to reconstruct 3D CBCT images.

A gold standard tumor trajectory is required to guide the algorithm development and estimate the tumor detection accuracy for markerless tracking algorithms. However, a sufficient strategy to validate markerless tracking algorithms is lacking. A validation framework is developed based on fiducial markers. Markers are segmented and marker trajectories are xiv obtained. The displacement of the tumor to the marker is calculated and added to the segmented marker trajectory to generate reference tumor trajectory. Markerless tumor trajectory estimation (MLTM) algorithm is developed and improved to acquire tumor trajectory with clinical acceptable accuracy for locally advanced lung tumors. The development is separate into two parts. The first part considers none tumor deformation. It investigates shape and appearance of the template, moreover, a constraint method is introduced to narrow down the template matching searching region for more precise matching results. The second part is to accommodate tumor deformation near the end of the treatment. The accuracy of MLTM is calculated and compared against 4D CBCT, which is the current standard of care.

In summary, a validation framework based on fiducial markers is successfully built. MLTM is successfully developed with or without the consideration of tumor deformation with promising accuracy. MLTM outperforms 4D CBCT in temporal tumor trajectory estimation.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

8-5-2016

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