Temporal separation of Cerenkov radiation and scintillation using a clinical LINAC and artificial intelligence

Abstract

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.