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Title: Computational techniques for fast Monte Carlo validation of proton therapy treatment plans
Author: Green, Andrew
ISNI:       0000 0004 6496 4714
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2017
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Proton therapy is an established radiotherapy technique for the treatment of complex cancers. However, problems exist in the planning of treatments where the use of inaccurate dose modelling may lead to treatments being delivered which are not optimal. Most of the problems with dose modelling tools used in proton therapy treatment planning lie in their treatment of processes such as multiple Coulomb scattering, therefore a technique that accurately models such effects is preferable. Monte Carlo simulation alleviates many of the problems in current dose models but, at present, well-validated full-physics Monte Carlo simulations require more time than is practical in clinical use. Using the well-known and well-validated Monte Carlo toolkit Geant4, an application-called PTMC-has been developed for the simulation of proton therapy treatment plans. Using PTMC, several techniques to improve throughput were developed and evaluated, including changes to the tracking algorithm in Geant4 and application of large scale parallelism using novel computing architectures such as the Intel Xeon Phi co-processor. In order to quantify any differences in the dose-distributions simulated when applying these changes, a new dose comparison tool was also developed which is more suited than current techniques for use with Monte Carlo simulated dose distributions. Using an implementation of the Woodcock algorithm developed in this work, it is possible to track protons through a water phantom up to eight times faster than using the PRESTA algorithm present in Geant4, with negligible loss of accuracy. When applied to a patient simulation, the Woodcock algorithm increases throughput by up to thirty percent, though step limitation was necessary to preserve simulation accuracy. Parallelism was implemented on an Intel Xeon Phi co-processor card, where PTMC was tested with up to 244 concurrent threads. Difficulties imposed by the limited RAM available were overcome through the modification of the Geant4 toolkit and through the use of a novel dose collation technique. Using a single Xeon Phi co-processor, it is possible to validate a proton therapy treatment plan in two hours; with two co-processors that simulation time is halved. For the treatment plan tested, two Xeon Phi co-processors were roughly equivalent to a single 48-core AMD Opteron machine. The relative costs of Xeon Phi co-processors and traditional machines have also been investigated; at present the Intel Xeon Phi co-processor is not cost competitive with standard hardware, costing around twice as much as an AMD machine with comparable performance. Distributed parallelism was also implemented through the use of the Google Compute Engine (GCE). A tool has been developed-called PYPE-which allows users to launch large clusters in the GCE to perform arbitrary compute-intensive work. PYPE was used with PTMC to perform rapid treatment plan validation in the GCE. Using a large cluster, it is possible to validate a proton therapy treatment plan in ten minutes at a cost of roughly $10; the same plan computed locally on a 24-thread Intel Xeon machine required five hours. As an example calculation using PYPE and PTMC, a robustness study is undertaken for a proton therapy treatment plan; this robustness study shows the usefulness of Monte Carlo when computing dose distributions for robustness studies, and the utility of the PYPE tool to make numerous full physics Monte Carlo simulations quickly. Using the tools developed in this work, a complete treatment plan robustness study can be performed in around 26 hours for a cost of less than $500, while using full-physics Monte Carlo for dose distribution calculations.
Supervisor: Owen, Hywel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Proton Therapy ; Monte Carlo ; Computational Techniques