Carla Winterhalter1,2, Adam Aitkenhead3,4, David Oxley1, Jenny Richardson3, Damien C Weber1,5,6, Ranald I MacKay3,4, Antony J Lomax1,2, Sairos Safai1. 1. Centre for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland. 2. Department of Physics, ETH Zürich, Switzerland. 3. Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK. 4. Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK. 5. Department of Radiation Oncology, University Hospital of Bern, Bern, Switzerland. 6. Department of Radiation Oncology, University Hospital of Zürich, Zürich, Switzerland.
Abstract
OBJECTIVE: Monte Carlo (MC) simulations substantially improve the accuracy of predicted doses. This study aims to determine and quantify the uncertainties of setting up such a MC system. METHODS: Doses simulated with two Geant4-based MC calculation codes, but independently tuned to the same beam data, have been compared. Different methods of MC modelling of a pre-absorber have been employed, either modifying the beam source parameters (descriptive) or adding the pre-absorber as a physical component (physical). RESULTS: After the independent beam modelling of both systems in water (resulting in excellent range agreement) range differences of up to 3.6/4.8 mm (1.5% of total range) in bone/brain-like tissues were found, which resulted from the use of different mean water ionisation potentials during the energy tuning process. When repeating using a common definition of water, ranges in bone/brain agreed within 0.1 mm and gamma-analysis (global 1%,1mm) showed excellent agreement (>93%) for all patient fields. However, due to a lack of modelling of proton fluence loss in the descriptive pre-absorber, differences of 7% in absolute dose between the pre-absorber definitions were found. CONCLUSION: This study quantifies the influence of using different water ionisation potentials during the MC beam modelling process. Furthermore, when using a descriptive pre-absorber model, additional Faraday cup or ionisation chamber measurements with pre-absorber are necessary. ADVANCES IN KNOWLEDGE: This is the first study quantifying the uncertainties caused by the MC beam modelling process for proton pencil beam scanning, and a more detailed beam modelling process for MC simulations is proposed to minimise the influence of critical parameters.
OBJECTIVE: Monte Carlo (MC) simulations substantially improve the accuracy of predicted doses. This study aims to determine and quantify the uncertainties of setting up such a MC system. METHODS: Doses simulated with two Geant4-based MC calculation codes, but independently tuned to the same beam data, have been compared. Different methods of MC modelling of a pre-absorber have been employed, either modifying the beam source parameters (descriptive) or adding the pre-absorber as a physical component (physical). RESULTS: After the independent beam modelling of both systems in water (resulting in excellent range agreement) range differences of up to 3.6/4.8 mm (1.5% of total range) in bone/brain-like tissues were found, which resulted from the use of different mean water ionisation potentials during the energy tuning process. When repeating using a common definition of water, ranges in bone/brain agreed within 0.1 mm and gamma-analysis (global 1%,1mm) showed excellent agreement (>93%) for all patient fields. However, due to a lack of modelling of proton fluence loss in the descriptive pre-absorber, differences of 7% in absolute dose between the pre-absorber definitions were found. CONCLUSION: This study quantifies the influence of using different water ionisation potentials during the MC beam modelling process. Furthermore, when using a descriptive pre-absorber model, additional Faraday cup or ionisation chamber measurements with pre-absorber are necessary. ADVANCES IN KNOWLEDGE: This is the first study quantifying the uncertainties caused by the MC beam modelling process for proton pencil beam scanning, and a more detailed beam modelling process for MC simulations is proposed to minimise the influence of critical parameters.
Authors: S Jan; G Santin; D Strul; S Staelens; K Assié; D Autret; S Avner; R Barbier; M Bardiès; P M Bloomfield; D Brasse; V Breton; P Bruyndonckx; I Buvat; A F Chatziioannou; Y Choi; Y H Chung; C Comtat; D Donnarieix; L Ferrer; S J Glick; C J Groiselle; D Guez; P F Honore; S Kerhoas-Cavata; A S Kirov; V Kohli; M Koole; M Krieguer; D J van der Laan; F Lamare; G Largeron; C Lartizien; D Lazaro; M C Maas; L Maigne; F Mayet; F Melot; C Merheb; E Pennacchio; J Perez; U Pietrzyk; F R Rannou; M Rey; D R Schaart; C R Schmidtlein; L Simon; T Y Song; J M Vieira; D Visvikis; R Van de Walle; E Wieërs; C Morel Journal: Phys Med Biol Date: 2004-10-07 Impact factor: 3.609
Authors: C Winterhalter; E Fura; Y Tian; A Aitkenhead; A Bolsi; M Dieterle; A Fredh; G Meier; D Oxley; D Siewert; D C Weber; A Lomax; S Safai Journal: Phys Med Biol Date: 2018-08-23 Impact factor: 3.609
Authors: Lamberto Widesott; Stefano Lorentini; Francesco Fracchiolla; Paolo Farace; Marco Schwarz Journal: Phys Med Biol Date: 2018-07-16 Impact factor: 3.609
Authors: Adam H Aitkenhead; Peter Sitch; Jenny C Richardson; Carla Winterhalter; Imran Patel; Ranald I Mackay Journal: Br J Radiol Date: 2020-07-29 Impact factor: 3.039