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The Importance of Monte Carlo (Geant4) Physics Settings for Clinical Proton Therapy Treatment Plans

C Winterhalter1,2*, M Taylor1,2, D Boersma3,4, A Elia4, S Guatelli5, R Mackay1,2, K Kirkby1,2, L Maigne6, V Ivanchenko7,8, A Resch9, D Sarrut10, P Sitch2, M Vidal11, L Grevillot4, A Aitkenhead1,2, (1) Division of Cancer Sciences, University of Manchester, Manchester, UK, (2) The Christie NHS Foundation Trust, Manchester, UK, (3) ACMIT Gmbh, Wiener Neustadt, Austria, (4) EBG MedAustron GmbH, Wiener Neustadt, Austria, (5) Centre For Medical Radiation Physics, University of Wollongong, Australia, (6) Laboratoire de Physique de Clermont, Campus Universitaire des Cezeaux, Aubiere Cedex, France, (7) CERN, Geneva, Switzerland, (8) Tomsk State University, Russia, (9) Medical University of Vienna, Department of Radiation Oncology and Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria, (10) Universite de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Centre Leon Berard, France, (11) Centre Antoine Lacassagne, Universite Cote dAzur Federation Claude Lalanne, Nice, France

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose:
To quantify the influence of Monte Carlo physics settings on dose results and calculation times for clinical proton therapy.

Methods:
Proton dose distributions were simulated using GATE-RTionV1.0 (GateV8.1/Geant4V10.3.3). The following simulation settings were investigated: Physics builder (QGSP_BIC_EMY, QGSP_BIC_EMZ, QGSP_BIC_HP_EMZ, all recommended for proton transport), and production cuts (0.1mm-10mm, limiting which secondary particles are explicitly produced). Simulations were scored on a 2mmx2mmx2mm dose grid. Simulation results were compared to a large number of relative (200 measurement planes, PTW-Octavius-1500-XDR array) and absolute (74 measurement points, PTW-31021-Semiflex-3D-ion chamber) dose measurements taken in a solid water phantom. Additionally, simulated dose was evaluated within the patient CT for a wide range of cases, consisting of a paranasal, a neck, a brain-stem, a base-of-skull and two spine treatments including metal implants.

Results:
Different physics lists, which include different electromagnetic physics models, lead to dose differences of up to 1% in solid water. Production cuts only marginally influence simulation results in solid water. As such, simulation settings do not substantially influence agreement to measurements, with an average relative 2%/2mm gamma agreement of 97% and an average absolute dose offset of 1% for all settings. In patient CT, dose changes due to physics lists are observed when additional material (Lexan range shifter) is inserted upstream of the patient to lower the proton energy and after heterogeneities (maximum changes within 3%). Production cuts cause dose differences of up to 4%, especially at interfaces between air and tissue. Calculation times vary by a factor of 1.4 for different physics settings and factors of 4.7/2.2 (solid water/patient CT) for different production cuts.

Conclusion:
Monte Carlo simulation parameters are a trade-off between dose accuracy and simulation time. Speed optimised settings can decrease calculation times by a factor of 5, but could lead to dose differences of up to 4%.

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Funding Support, Disclosures, and Conflict of Interest: This work was funded by the Science and Technology Facilities Council (STFC) Advanced Radiotherapy Network, grant number ST/N002423/1 and the Engineering and Physical Sciences Council, grant number EP/R023220/1. Supported by the NIHR Manchester Biomedical Research Council.

Keywords

Monte Carlo, Protons, Dose

Taxonomy

TH- External Beam- Particle/high LET therapy: Proton therapy – computational dosimetry-Monte Carlo

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