Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia.
Phys Med Biol. 2018 Nov 9;63(22):225004. doi: 10.1088/1361-6560/aae938.
Convolutional neural network (CNN) type artificial intelligences were trained to estimate the Cerenkov radiation present in the temporal response of a LINAC irradiated scintillator-fiber optic dosimeter. The CNN estimate of Cerenkov radiation is subtracted from the combined scintillation and Cerenkov radiation temporal response of the irradiated scintillator-fiber optic dosimeter, giving the sole scintillation signal, which is proportional to the scintillator dose. The CNN measured scintillator dose was compared to the background subtraction measured scintillator dose and ionisation chamber measured dose. The dose discrepancy of the CNN measured dose was on average 1.4% with respect to the ionisation chamber measured dose, matching the 1.4% average dose discrepancy of the background subtraction measured dose with respect to the ionisation chamber measured dose. The developed CNNs had an average time of 3 ms to calculate scintillator dose, permitting the CNNs presented to be applicable for dosimetry in real time.
卷积神经网络 (CNN) 型人工智能被训练来估计 LINAC 辐照闪烁体光纤剂量计的时间响应中存在的切伦科夫辐射。从辐照闪烁体光纤剂量计的闪烁和切伦科夫辐射的组合时间响应中减去 CNN 估计的切伦科夫辐射，得到仅与闪烁体剂量成正比的闪烁信号。将 CNN 测量的闪烁体剂量与背景扣除测量的闪烁体剂量和电离室测量的剂量进行比较。与电离室测量的剂量相比，CNN 测量的剂量的剂量差异平均为 1.4%，与背景扣除测量的剂量的平均剂量差异为 1.4%，与电离室测量的剂量相比。开发的 CNN 平均计算闪烁体剂量的时间为 3ms，允许提出的 CNN 适用于实时剂量测定。
