A novel approach to neutron dosimetry. Issue 11 (14th October 2016)

• Record Type: Journal Article
• Authors: Balmer, Matthew J. I.; Gamage, Kelum A. A.; Taylor, Graeme C.
• Is Part Of: Medical physics. Volume 43:Issue 11(2016)
• Page Start: 5981
• Page End: 5990
• Publication Date: 2016-10-14
• Subjects: dosimetry -- liquid scintillation detectors -- neural nets -- phantoms -- radioisotopes -- scintillation; Dose‐volume analysis -- Neural engineering; Scintigraphy -- the detector being a gas; effective dose -- ANN -- directional neutron survey -- pattern recognition; Neutrons -- Dosimetry -- Neutron sources -- Neutron radiation effects -- Photomultipliers -- Scintillation detectors -- Testing procedures -- Artificial neural networks -- Computer simulation -- Gamma rays; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4964456
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Having been overlooked for many years, research is now starting to take into account the directional distribution of neutron workplace fields. Existing neutron dosimetry instrumentation does not account for this directional distribution, resulting in conservative estimates of dose in neutron workplace fields (by around a factor of 2, although this is heavily dependent on the type of field). This conservatism could influence epidemiological studies on the health effects of radiation exposure. This paper reports on the development of an instrument which can estimate the effective dose of a neutron field, accounting for both the direction and the energy distribution. Methods: A 6 Li‐loaded scintillator was used to perform neutron assays at a number of locations in a 20 × 20 × 17.5 cm 3 water phantom. The variation in thermal and fast neutron response to different energies and field directions was exploited. The modeled response of the instrument to various neutron fields was used to train an artificial neural network (ANN) to learn the effective dose and ambient dose equivalent of these fields. All experimental data published in this work were measured at the National Physical Laboratory (UK). Results: Experimental results were obtained for a number of radionuclide source based neutron fields to test the performance of the system. The results of experimental neutron assays at 25 locations in a water phantom were fed into the trained ANN. A correlation between neutron counting rates in the phantom and neutron fluence rates was experimentally found to provide dose rate estimates. A radionuclide source behind shadow cone was used to create a more complex field in terms of energy and direction. For all fields, the resulting estimates of effective dose rate were within 45% or better of their calculated values, regardless of energy distribution or direction for measurement times greater than 25 min. Conclusions: This work presents a novel, real‐time, approach to workplace neutron dosimetry. It is believed that in the research presented in this paper, for the first time, a single instrument has been able to estimate effective dose.