RadField3D: a data generator and data format for deep learning in radiation-protection dosimetry for medical applications.

Journal: Journal of radiological protection : official journal of the Society for Radiological Protection
PMID: 40334671

Abstract

In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating three-dimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable data format with a Python application programming interface for easy integration into neural network research, that we call RadFiled3D. Both developments are intended to be used to research alternative radiation simulation methods using deep learning. All data used for our validation (measured and simulated), along with our source codes, are published in separate repositories.https://github.com/Centrasis/RadField3DSimulationhttps://github.com/Centrasis/RadFiled3D.

Authors

  • Felix Lehner
    Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Germany.
  • Pasquale Lombardo
    Belgian Nuclear Research Centre (SCK CEN), Boeretang, Mol, Belgium.
  • Susana Castillo
    Institute for Computer Graphics, Technische Universität Braunschweig, Braunschweig, Germany.
  • Oliver Hupe
    Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Germany.
  • Marcus Magnor
    Institute for Computer Graphics, Technische Universität Braunschweig, Braunschweig, Germany.

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