蒙特卡罗方法
计算机科学
计算
人工神经网络
放射治疗计划
算法
集合(抽象数据类型)
网格
中子俘获
可靠性(半导体)
数据集
中子
放射治疗
物理
人工智能
数学
医学
统计
功率(物理)
几何学
量子力学
内科学
程序设计语言
作者
Yongquan Wang,Junliang Du,Huan Lin,Xingcai Guan,Lu Zhang,Jinyang Li,Long Gu
摘要
In boron neutron capture therapy (BNCT)-a form of binary radiotherapy-the primary challenge in treatment planning systems for dose calculations arises from the time-consuming nature of the Monte Carlo (MC) method. Recent progress, including the use of neural networks (NN), has been made to accelerate BNCT dose calculations. However, this approach may result in significant dose errors in both the tumor and the skin, with the latter being a critical organ in BNCT. Furthermore, owing to the lack of physical processes in purely NN-based approaches, their reliability for clinical dose calculations in BNCT is questionable.
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