Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.790278
Title: Toward adaptive radiotherapy
Author: Correia Veloso Da Veiga, C. I.
ISNI:       0000 0004 8503 9536
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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Abstract:
Intensity Modulated Radiotherapy (IMRT) and proton therapy are the state-of-art external radiotherapy modalities. To make the most of such precise delivery, accurate knowledge of the patient anatomy and biology during treatment is necessary, as unaccounted variations can compromise the outcome of the treatment. Treatment modification to account for deviations from the planning stage is a framework known as adaptive radiotherapy (ART). To fully utilise the information extracted from different modalities and/or at different time-points it is required to accurately align the imaging data. In this work the feasibility of cone-beam computed tomography (CBCT) and deformable image registration (DIR) for ART was evaluated in the context of head and neck (HN) and lung malignancies, and for IMRT and proton therapy applications. This included the geometric validation of deformations for multiple DIR algorithms, estimating the uncertainty in dose recalculation of a CBCT-based deformed CT (dCT), and the uncertainty in dose summation resulting from the properties of the underlying deformations. The dCT method was shown to be a good interim solution to repeat CT and a superior alternative to simpler direct usage of CBCT for dose calculation; proton therapy treatments were more sensitive to registration errors than IMRT. The ability to co-register multimodal and multitemporal data of the HN was also explored; the results found were promising and the limitations of current algorithms and data acquisition protocols were identified. The use of novel artificial cancer masses as a novel platform for the study of imaging during radiotherapy was explored in this study. The artificial cancer mass model was extended to generate magnetic resonance imaging (MRI)-friendly samples. The tumoroids were imageable in standard T1 and T2 MRI acquisitions, and the relaxometric properties were measured. The main limitation of the current tumour model was the poor reproducibility and controllability of the properties of the samples.
Supervisor: Royle, G. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.790278  DOI: Not available
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