Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia.
Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia.
Phys Med. 2018 Oct;54:131-136. doi: 10.1016/j.ejmp.2018.10.007. Epub 2018 Oct 10.
The irradiation of scintillator-fiber optic dosimeters by clinical LINACs results in the measurement of scintillation and Cerenkov radiation. In scintillator-fiber optic dosimetry, the scintillation and Cerenkov radiation responses are separated to determine the dose deposited in the scintillator volume. Artificial neural networks (ANNs) were trained and applied in a novel single probe method for the temporal separation of scintillation and Cerenkov radiation. Six dose profiles were measured using the ANN, with the dose profiles compared to those measured using background subtraction and an ionisation chamber. The average dose discrepancy of the ANN measured dose was 2.2% with respect to the ionisation chamber dose and 1.2% with respect to the background subtraction measured dose, while the average dose discrepancy of the background subtraction dose was 1.6% with respect to the ionisation chamber dose. The ANNs performance was degraded when compared with background subtraction, arising from an inaccurate model used to synthesise ANN training data.
临床 LINAC 对闪烁光纤剂量计的辐照会导致闪烁和切伦科夫辐射的测量。在闪烁光纤剂量测量中，通过分离闪烁和切伦科夫辐射响应来确定闪烁体体积中沉积的剂量。人工神经网络 (ANN) 被训练并应用于一种新颖的单探头方法中，用于闪烁和切伦科夫辐射的时间分离。使用 ANN 测量了六个剂量分布，将剂量分布与使用背景扣除和电离室测量的剂量分布进行了比较。ANN 测量的剂量与电离室剂量的平均剂量差异为 2.2%，与背景扣除测量的剂量的平均剂量差异为 1.2%，而背景扣除剂量的平均剂量差异为 1.6%与电离室剂量。与背景扣除相比，ANN 的性能有所下降，这是由于用于合成 ANN 训练数据的模型不准确所致。
