开源3D辐射场数据生成工具，助力医学辐射防护深度学习研究
RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications
- 基于Geant4的蒙特卡洛模拟生成3D辐射场数据
- 提供可快速解析的Python可读数据格式
- 适合辐射剂量学与深度学习交叉研究者使用
本研究提出开源的Geant4基蒙特卡洛仿真应用RadField3D，用于生成医学辐射防护剂量学中的三维辐射场数据集。同时引入一种高效、机器可读的数据格式RadField3D，配备Python API，便于集成至神经网络研究中。两项成果旨在推动深度学习在辐射模拟替代方法中的应用研究。
原文摘要 · Abstract (English)
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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