2018
DOI: 10.1088/1361-6560/aae938
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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 compar… Show more

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Cited by 9 publications
(5 citation statements)
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References 21 publications
(31 reference statements)
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“…The tips of the PSD and reference probes were coated in black paint to prevent visible light from entering either of the probes. For measurements with the PSD, an optical fiber housing was used to hold the PSD and reference probes consisting of a 1 cm thick sheet of perspex with a 2.2 mm wide, 4.4 mm deep housing groove machined into the perspex, matching the housing used by and Madden et al 2018aMadden et al , 2018b. The housing groove was filled with ultrasound gel (Aquasonic 100 by Parker Laboratories, INC) with the PSD and reference probe placed in the housing groove to eliminate air gaps between the optical fibers and perspex in the housing groove.…”
Section: Methodsmentioning
confidence: 99%
“…The tips of the PSD and reference probes were coated in black paint to prevent visible light from entering either of the probes. For measurements with the PSD, an optical fiber housing was used to hold the PSD and reference probes consisting of a 1 cm thick sheet of perspex with a 2.2 mm wide, 4.4 mm deep housing groove machined into the perspex, matching the housing used by and Madden et al 2018aMadden et al , 2018b. The housing groove was filled with ultrasound gel (Aquasonic 100 by Parker Laboratories, INC) with the PSD and reference probe placed in the housing groove to eliminate air gaps between the optical fibers and perspex in the housing groove.…”
Section: Methodsmentioning
confidence: 99%
“…It is worth mentioning that some recent research on artificial intelligence (AI) methods and machine learning methods are used to improve situational awareness, accuracy of data analysis, and control of fiber radiation systems [29,30]. Meanwhile, the neural network type AI can be trained to estimate the radiation in the temporal response, permitting the fiber radiation sensors to be applicable for dosimetry in real time [31].…”
Section: Fundamentals Of Optical Fiber-based Radiation Sensingmentioning
confidence: 99%
“… 3 , 4 They characterized their plastic scintillator dosimeter and measured basic beam data in radiotherapy, such as the percentage depth dose (PDD). From then on, research on a scintillation dosimeter had shown a steady rise with a focus on the scintillator material, 5 , 6 development of the photodetector, 7 , 8 , 9 innovation on the optical guide, 10 , 11 elimination of the stem effect, 12 , 13 , 14 and the dimension of measurements. 15 , 16 Plastic scintillator dosimeters have been used to tackle complex and advanced dosimetry challenges in radiotherapy, such as the small field, 16 particle therapy, 17 magnetic influence, 18 and ultra‐high dose rate.…”
Section: Introductionmentioning
confidence: 99%