Treatment planning for manifold multi-ion particle therapy

Alabd, Roumani

Treatment planning for manifold multi-ion particle therapy

Alabd, Roumani

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Publication Type:: Thesis
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Alabd, Roumani
dc.date.accessioned	2023-07-13T02:21:13Z
dc.date.available	2023-07-13T02:21:13Z
dc.date.issued	2023
dc.identifier.uri	http://hdl.handle.net/10453/171475
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Conventional charged particle radiation therapy systems utilise a single charged particle species to deliver a therapeutic radiation dose to the target - most commonly protons or carbon ions. While such radiation therapy is highly effective for the treatment of many cancers, there is growing interest in using combinations of two or three ion species for cancer therapy, due to the ability to control both the dose distribution and the linear energy transfer of the radiation throughout the target. However, to date, there has been little research into the potential for going beyond a very limited number of ion species in a single treatment plan due to the limitations of current medical accelerators, and there are presently no treatment planning systems capable of producing a treatment plan integrating a diversity of ion species. This work presents a new open source treatment planning system for manifold multi-ion particle therapy based on a hybrid Monte Carlo and linear optimisation approach. The developed TPS utilises a library of Monte Carlo simulation data for up to eight individual ion species (H, He, Li, C, O, Ne, Si and Fe) of many different energies in a single target material (polymethyl methacrylate). The library is then adapted for the pencil beam geometry and energy spread of a specific accelerator beamline; next, a raster grid of beam positions is constructed to cover the target volume, and the weighting of individual energy components of each ion beam required at each position is determined by linear optimisation such that the desired spatial distribution of physical dose is achieved in a heterogeneous target. The TPS will optimise the parameters of the ion source (which could be a conventional synchrotron source or a laser-driven ion source) to produce the desired dose distributions in the target, while accounting for tumour hypoxia/necrosis and organs at risk.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/171475/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2023 Roumani Alabd
dc.rights	au.edu.uts.lib/cph
dc.title	Treatment planning for manifold multi-ion particle therapy	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Conventional charged particle radiation therapy systems utilise a single charged particle species to deliver a therapeutic radiation dose to the target - most commonly protons or carbon ions. While such radiation therapy is highly effective for the treatment of many cancers, there is growing interest in using combinations of two or three ion species for cancer therapy, due to the ability to control both the dose distribution and the linear energy transfer of the radiation throughout the target. However, to date, there has been little research into the potential for going beyond a very limited number of ion species in a single treatment plan due to the limitations of current medical accelerators, and there are presently no treatment planning systems capable of producing a treatment plan integrating a diversity of ion species. This work presents a new open source treatment planning system for manifold multi-ion particle therapy based on a hybrid Monte Carlo and linear optimisation approach. The developed TPS utilises a library of Monte Carlo simulation data for up to eight individual ion species (H, He, Li, C, O, Ne, Si and Fe) of many different energies in a single target material (polymethyl methacrylate). The library is then adapted for the pencil beam geometry and energy spread of a specific accelerator beamline; next, a raster grid of beam positions is constructed to cover the target volume, and the weighting of individual energy components of each ion beam required at each position is determined by linear optimisation such that the desired spatial distribution of physical dose is achieved in a heterogeneous target. The TPS will optimise the parameters of the ion source (which could be a conventional synchrotron source or a laser-driven ion source) to produce the desired dose distributions in the target, while accounting for tumour hypoxia/necrosis and organs at risk.

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http://hdl.handle.net/10453/171475