A Monte Carlo-based treatment planning tool for proton therapy.

Details

Serval ID
serval:BIB_1AF558D58482
Type
Article: article from journal or magazin.
Collection
Publications
Title
A Monte Carlo-based treatment planning tool for proton therapy.
Journal
Phys. Med. Biol.
Author(s)
Mairani A, Böhlen T T, Schiavi A, Tessonnier T, Molinelli S, Brons S, Battistoni G, Parodi K, Patera V
ISSN
1361-6560
Publication state
Published
Issued date
2013
Peer-reviewed
Oui
Volume
58
Number
8
Pages
2471-90
Language
english
Abstract
In the field of radiotherapy, Monte Carlo (MC) particle transport calculations are recognized for their superior accuracy in predicting dose and fluence distributions in patient geometries compared to analytical algorithms which are generally used for treatment planning due to their shorter execution times. In this work, a newly developed MC-based treatment planning (MCTP) tool for proton therapy is proposed to support treatment planning studies and research applications. It allows for single-field and simultaneous multiple-field optimization in realistic treatment scenarios and is based on the MC code FLUKA. Relative biological effectiveness (RBE)-weighted dose is optimized either with the common approach using a constant RBE of 1.1 or using a variable RBE according to radiobiological input tables. A validated reimplementation of the local effect model was used in this work to generate radiobiological input tables. Examples of treatment plans in water phantoms and in patient-CT geometries together with an experimental dosimetric validation of the plans are presented for clinical treatment parameters as used at the Italian National Center for Oncological Hadron Therapy. To conclude, a versatile MCTP tool for proton therapy was developed and validated for realistic patient treatment scenarios against dosimetric measurements and commercial analytical TP calculations. It is aimed to be used in future for research and to support treatment planning at state-of-the-art ion beam therapy facilities.
Keywords
Humans, Neoplasms, Algorithms, Radiotherapy Planning, Relative Biological Effectiveness, Monte Carlo Method, Computer-Assisted, Imaging, Phantoms, Computer-Assisted: methods, Neoplasms: radiotherapy, Proton Therapy, Proton Therapy: methods, Water
Pubmed
Create date
15/03/2023 9:08
Last modification date
17/03/2023 6:52
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