Journal
PHYSICS IN MEDICINE AND BIOLOGY
Volume 63, Issue 22, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1361-6560/aae938
Keywords
artificial intelligence; megavoltage x-ray beam; scintillation dosimetry; fiber optic dosimeters
Funding
- UOWs Global Challenges Program
- Australian Government Research Training Program Scholarship
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available