RadField3D

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

authored by

Felix Lehner, Pasquale Lombardo, Susana Castillo, Oliver Hupe, Marcus Magnor

Abstract

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. github.com/Centrasis/RadField3DSimulation github.com/Centrasis/RadFiled3D

Organisation(s)

PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines

External Organisation(s)