Computer Graphics
TU Braunschweig

RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications


RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications

In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating threedimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable data format with a Python API 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.


Author(s):Felix Lehner, Pasquale Lombardo, Susana Castillo, Oliver Hupe, Marcus Magnor
Published:December 2024
Type:Misc
Howpublished:arXiv preprint


@misc{lehner2024radfield3d,
  title = {RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications},
  author = {Lehner, Felix and Lombardo, Pasquale and Castillo, Susana  and Hupe, Oliver and Magnor, Marcus},
  howpublished = {arXiv preprint},
  month = {Dec},
  year = {2024}
}

Authors