Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763211
Title: Dual-energy computed tomography for proton radiotherapy
Author: Bär, Esther
ISNI:       0000 0004 7660 6631
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Abstract:
Proton therapy is an advanced form of cancer treatment. To use this precise technique to its full potential, a better understanding of the uncertainties involved in the treatment process and their mitigation is necessary. This thesis aims at the investigation, understanding, and improvement of three sources of range uncertainties: (i) CT imaging and conversion to tissue, (ii) mean excitation energies and (iii) lateral inhomogeneities in the irradiated object. To predict the range of the proton beam within the patient, knowledge of the tissues in the beam's path is required. Clinically, the required tissue characteristics are estimated using a single-energy CT (SECT) scan of the patient. In this work, the potential of dual-energy CT (DECT) to improve the estimation of tissue characteristics for proton therapy planning and the related uncertainty is shown in two steps. In a first study, several DECT approaches are compared in a theoretical study and their performance in realistic situations is evaluated. In a second study, DECT-predicted tissue characteristics are validated in an experimental setup using animal tissues. To understand the influence of the mean excitation energy (I-value) on the range uncertainty, elemental I-values for particle therapy planning are revisited and an uncertainty budget is established. This enables the estimation of stopping power and range uncertainties arising from I-values. The largest source of range uncertainties in particle therapy planning arises from lateral inhomogeneities in the irradiated body, introducing range degradation. Conventional dose calculation algorithms use ray tracing to calculate the beam range, leading to severe range uncertainties. Monte Carlo (MC) was demonstrated to reduce these uncertainties by accurately simulating particle transport in the patient geometry. In this work, the uncertainties arising from lateral inhomogeneities are investigated with both, ray tracing and MC techniques.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.763211  DOI: Not available
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