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

arXiv: https://arxiv.org/abs/2412.13852 · HTML: https://mlsanity.com/paper/2412.13852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 33 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm](https://mlsanity.com/paper/2011.14959/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning-Based Estimation of Spatially Resolved Scatter Radiation Fields in Interventional Radiology](https://mlsanity.com/paper/2512.17654/) (2025)

## Abstract (arXiv)

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.