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 |
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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
Felix Lehner
ResearcherPasquale Lombardo
ExternalSusana Castillo
Senior ResearcherOliver Hupe
ExternalMarcus Magnor
Director, Chair