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. The two developed models were extended to predict the surrogate motion by means of a sliding window polynomial function and an adaptive mode of the bilinear model. The new prediction algorithms for long and short time motion prediction developed by the author were compared to some predictors based neural network (NN) and Kalman Filter (KF), both established by MAESTRO co-workers, and using the same data. It was found that while the sliding polynomial model performs well over a short-time prediction (0.2s), the adaptive approach of the bilinear model performs better over a long-time prediction (up to 0.6s). The modelling and prediction of the respiratory surrogate motion were also performed by considering input-output measurement. For this purpose, a novel experiment designed by the author involving Spirometry and external surrogate markers has been carried out. A comparison between the performance of a set candidate linear and bilinear models has shown that a second order linear model gives the best results. The impact of using a virtual input signal instead of physical input has revealed that using a virtual input gives better results than using a physical input. A new approach was applied to the predictive tracking of respiratory surrogate motion. The work was carried out by building a non-linear model of the patient support system (PSS) to achieve a good simulation model for the latter. The combination of the prediction and control to compensate for respiratory motion during the radiotherapy treatment was also considered. Finally, the overall MAESTRO aim was achieved by introducing a new strategy to accommodate for tumour/organ motion. The latter was assessed in clinical environment at the University Hospitals Coventry and Warwickshire (UHCW).
|Date of Award||2010|
|Sponsors||Framework 6 European integrated project Methods and Advanced Equipment for Simulation and Treatment in Radiation Oncology (MAESTRO).|
|Supervisor||Keith Burnham (Supervisor), Olivier Haas (Supervisor) & John A. Mills (Supervisor)|