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Validation of Geant4 for silicon microdosimetry in heavy ion therapy

Abstract

Microdosimetry is a particularly powerful method to estimate the relative biological effectiveness (RBE) of any mixed radiation field. This is particularly convenient for therapeutic heavy ion therapy (HIT) beams, referring to ions larger than protons, where the RBE of the beam can vary significantly along the Bragg curve. Additionally, due to the sharp dose gradients at the end of the Bragg peak (BP), or spread out BP, to make accurate measurements and estimations of the biological properties of a beam a high spatial resolution is required, less than a millimetre. This requirement makes silicon microdosimetry particularly attractive due to the thicknesses of the sensitive volumes commonly being ∼10 µm or less. Monte Carlo (MC) codes are widely used to study the complex mixed HIT radiation field as well as to model the response
of novel microdosimeter detectors when irradiated with HIT beams. Therefore it is essential to validate MC codes against experimental measurements.
This work compares measurements performed with a silicon microdosimeter in monoenergetic 12C , 14N and 16O ion beams of therapeutic energies, against simulation results
calculated with the Geant4 toolkit. Experimental and simulation results were compared in terms of microdosimetric spectra (dose lineal energy, d(y)), the dose mean lineal energy, yD and the RBE10, as estimated by the microdosimetric kinetic model (MKM). Overall Geant4
showed reasonable agreement with experimental measurements. Before the distal edge of the BP, simulation and experiment agreed within ∼10% for yD and ∼2% for RBE10. Downstream of the BP less agreement was observed between simulation and experiment, particularly for the
12C and 16O beams. Simulation results downstream of the BP had lower values of yD and RBE10 compared to the experiment due to a higher contribution from lighter fragments compared to heavier fragments.
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Category

Academic article

Client

  • Research Council of Norway (RCN) / 219991

Language

English

Author(s)

  • David Bolst
  • Susanna Gautelli
  • Linh T. Tran
  • Jeremy Davis
  • Lachlan Chartier
  • Dale A. Prokopovich
  • Alex Pogossov
  • Mark I. Reinhard
  • Marco Petasecca
  • Michael L.F. Lerch
  • Naruhiro Matsufuji
  • Marco Povoli
  • Angela Kok
  • Anand Summanwar
  • Michael Jackson
  • Anatoly B. Rosenfeld

Affiliation

  • University of Wollongong
  • Australian Nuclear Science and Technology Organisation
  • Japan
  • SINTEF Digital / Smart Sensors and Microsystems
  • University of New South Wales

Year

2019

Published in

Physics in Medicine and Biology

ISSN

0031-9155

Volume

65

Issue

4

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