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

Felix Lehner, Pasquale Lombardo, Susana Castillo, Oliver Hupe, Marcus Magnor
12/18/2024
cs.LGphysics.comp-ph

No official code found. Best implementation: Centrasis/RadFiled3D (3 stars, MIT, last commit 2026-04-28). 1 more below.

Abstract

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.

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Code Implementations(2)

Centrasis/RadFiled3DCommunity100%
MIT

A file format for three dimensional radiation fields as presented in "RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications"

33C++, CMakeDec 13, 20245 months agoMIT
Centrasis/RadField3DSimulationCommunity100%
MIT

A Monte-Carlo Radiation Transport Simulation Application for calculating spatially resolved radiation fields as described in "RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications"

12C++, CMakeDec 13, 20246 months agoMIT

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Cite this paper

@article{lehner2024radfieldd,
  title  = {RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications},
  author = {Felix Lehner and Pasquale Lombardo and Susana Castillo and Oliver Hupe and Marcus Magnor},
  year   = {2024},
  eprint = {2412.13852},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.13852v1}
}

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