Temporal separation of Cerenkov radiation and scintillation using artificial neural networks in Clinical LINACs

•The neural network estimates the shape of the Cerenkov radiation response well.•The neural network was most successful for in field regions of the dose profiles.•The temporal Cerenkov radiation response is dependent on the beam energy.•Neural networks were designed with the consideration of beam en...

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Bibliographic Details
Published in:Physica medica 2018-10, Vol.54, p.131-136
Main Authors: Madden, Levi, Archer, James, Li, Enbang, Wilkinson, Dean, Rosenfeld, Anatoly
Format: Article
Language:English
Subjects:
Artificial neural network
Cerenkov radiation
Optical fiber
Scintillator
X-ray dosimetry
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Summary:•The neural network estimates the shape of the Cerenkov radiation response well.•The neural network was most successful for in field regions of the dose profiles.•The temporal Cerenkov radiation response is dependent on the beam energy.•Neural networks were designed with the consideration of beam energy dependence. 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.
ISSN:1120-1797
1724-191X
DOI:10.1016/j.ejmp.2018.10.007