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

2024/12/18 by Lehner, Felix, Lombardo, Pasquale, Castillo, Susana +2 · 1 citation
#Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2412.13852

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.

Cited by

• Learning-Based Estimation of Spatially Resolved Scatter Radiation Fields in Interventional Radiology

Related

• Learning-Based Estimation of Spatially Resolved Scatter Radiation Fields in Interventional Radiology
• A new dose calculation system implemented in image domain -- A multi-institutional study
• Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm
• Deep learning for accelerating Monte Carlo radiation transport simulation in intensity-modulated radiation therapy
• Boosting radiotherapy dose calculation accuracy with deep learning