Quantification of Geometric Uncertainties in Image Guided Radiotherapy
Abstract
The aim of this thesis is to determine if the geometric uncertainties that are introduced into the image guided radiotherapy (IGRT) process by Cone Beam CT (CBCT) based IGRT equipment are sufficiently small that they do not pose a significant risk of geometrical error in treatment delivery. This was performed by quantifying and \ninvestigating the geometric uncertainties introduced by; (1) calibration of the image geometry, (2) correction of patient position performed by automatic treatment couch \nsystems and (3) automatic image registration of the localisation image with a reference image. In addition, the feasibility of providing user feedback on the likelihood of \naccurate image registration was investigated. A method was developed using supervised machine learning based on the shape of the image registration algorithm's similarity metric surface. \n \nThe geometric uncertainties introduced by image calibration and couch positioning were both shown to be less than 1 mm and therefore do not contribute significantly to \nthe overall uncertainties in the IGRT process. Image registration performance for image guidance based on the bony anatomy of the skull was shown to be reproducible, accurate and robust with errors typically less than 1 mm. Moreover, image registration performance did not deteriorate significantly as imaging dose was reduced. For image guidance based on the soft tissues of the prostate, image registration performance was satisfactory for some CBCT images…
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