Uncertainty evaluation for organ dose assessment with optically stimulated luminescence measurements on mobile phone resistors after a radiological incident

A radiological incident may result in members of the public being exposed to high doses of ionizing radiation. Rapid triage and a rough dose assessment is then an important component of the process of identifying people requiring immediate medical attention, supporting clinical observations and widening the scope to categorize those individuals who are potentially exposed but are pre-symptomatic. Optically stimulated luminescence on mobile phone resistors is well established in this context. Previous studies have shown good dosimetric properties and dose reconstruction protocols were optimized in the MULTIBIODOSE project (Bassinet et al., 2014). Evaluation of the full uncertainty budget and the detection limit is of fundamental importance for any personal dosimetry technique. In this work it was investigated how different factors influence the organ dose assessment with optically stimulated luminescence on mobile phone resistors, how one can correct for these, and how much they contribute to the uncertainty. The investigated factors include calibration, zero dose signal, reproducibility, dose response, fading, red light exposure, storage temperature, energy response and conversion from resistor dose to organ dose depending also on exposure scenario and mobile phone position. The uncertainty and detection limit were found to depend strongly on the delay between incident exposure and measurement and knowledge of the incident scenario. The uncertainty also depends strongly on the dose level. Detection limits of the order of 100 mGy were found, which is more than sufficient for triage. For doses around the detection limit the uncertainties related to the zero dose signal and reproducibility are dominant, while for higher doses of the order of grays the uncertainties related to fading and conversion from resistor to organ doses are dominant. For the higher doses the uncertainties are around 50% (k = 2) for known incident scenarios and up to more than 100% (k = 2) for unknown scenarios. Based on the results from this work recommendations are given to further decrease the uncertainty and detection limit for this technique.