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MO-A-213AB-06: Validation of Nuclear Reaction Models to Simulate Proton Therapy Range Verification Using Prompt

J Verburg1, H Shih1, J Seco1

  • 1Massachusetts General Hospital and Harvard Medical School, Boston, MA.

Medical Physics
|May 19, 2017
PubMed
Summary

Nuclear reaction models significantly impact prompt gamma-ray imaging simulations for proton therapy. Current models show large uncertainties, necessitating improvements for accurate proton range verification.

Keywords:
CalciumCarbonGamma raysMedical imagingMonte Carlo methodsNuclear medicine imagingNuclear reaction modelsNuclear reactionsProton therapyProtons

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Area of Science:

  • Medical Physics
  • Nuclear Physics
  • Computational Science

Background:

  • Proton therapy utilizes proton beams for cancer treatment.
  • Prompt gamma-ray imaging is a promising technique for verifying proton range.
  • Accurate simulation of nuclear reactions is crucial for prompt gamma-ray imaging.

Purpose of the Study:

  • To assess the impact of different nuclear reaction models on prompt gamma-ray imaging simulations.
  • To evaluate the accuracy of various simulation tools for proton therapy range verification.

Main Methods:

  • Compared four nuclear reaction models (GEANT4, MCNPX, TALYS, EMPIRE) for simulating gamma emission.
  • Validated models against experimental cross-sections for proton-induced reactions on C, O, N, Ca.
  • Analyzed gamma production along proton beam paths in simulated tissues.

Main Results:

  • Model differences in cross-sections varied from negligible to an order of magnitude.
  • Dedicated nuclear reaction codes (TALYS, EMPIRE) showed better agreement with experimental data.
  • Simulations for a 150 MeV proton beam indicated variations up to a factor of 4 in gamma emission near the Bragg peak.

Conclusions:

  • Nuclear reaction models require improvement for accurate prompt gamma-ray simulations in proton therapy.
  • Current models exhibit significant uncertainties in gamma yield and Bragg peak correlation.
  • GEANT4 and MCNPX showed limited predictive power for this application.