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Title: Respiratory motion modelling and predictive tracking for adaptive radiotherapy
Author: Abdelhamid, S.
Awarding Body: Coventry University
Current Institution: Coventry University
Date of Award: 2010
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External beam radiation therapy (EBRT) is the most common form of radiation therapy (RT) that uses controlled energy sources to eradicate a predefined tumour volume, known as the planning target volume (PTV), whilst at the same time attempting to minimise the dose delivered to the surrounding healthy tissues. Tumours in the thoracic and abdomen regions are susceptible to motion caused mainly by the patient respiration and movement that may occur during the treatment preparation and delivery. Usually, an adaptive approach termed adaptive radiation therapy (ART), which involves feedback from imaging devices to detect organ/surrogate motion, is considered. The feasibility of such techniques is subject to two main problems. First, the exact position of the tumour has to be estimated/detected in real-time and second, the delay that can arise from the tumour position acquisition and the motion tracking compensation. The research work described in this thesis is part of the European project entitled ‘Methods and advanced equipment for simulation and treatment in radiation oncology’ (MAESTRO), see Appendix A. The thesis presents both theoretical and experimental work to model and predict the respiratory surrogate motion. Based on a widely investigated clinical internal and external respiratory surrogate motion data, two new approaches to model respiratory surrogate motion were developed. The first considers the lung as a bilinear model that replicates the motion in response to a virtual input signal that can be seen as a signal generated by the nervous system. This model and a statistical model of the respiratory period and duty cycle were used to generate a set of realistic respiratory data of varying difficulties. The aim of the latter was to overcome the lack of test data for a researcher to evaluate their algorithms. The second approach was based on an online polynomial function that was found to adequately replicate the breathing cycles of regular and irregular data, using the same number of parameters as a benchmark sinusoidal model.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Radiotherapy, radiation therapy,oncology