Electron paramagnetic resonance, thermoluminescence, and optically stimulated luminescence, with biological tissues and inert materials are well established physical methods for retrospective dosimetry in acute accidental exposures. The objective of this article is to provide a view of the questions still open, the current challenges and the needed solutions. As research on emergency response methods is encountering increasing difficulties in terms of financial and human resources in many countries, it is essential to identify the research priorities and pay attention to cost-effective research paths. The intention of the paper is to stimulate discussion in the scientific community and to encourage collaboration among laboratories toward goals that address the real needs in retrospective dosimetry for acute exposures. 

Reflections on the future developments of research in retrospective physical dosimetry

Measured spectra in a high count rate environment are difficult to analyze because of the spectral distortions caused by the pulse pile-up effect. This study proposes a deep learning-based method for separating and predicting the true pulse height of a signal with pulse pile-up events for application to radiation measurement and spectroscopy with a scintillation detector. To train the deep learning model, pulse signals simulating scintillation pulses were prepared by using a predefined mathematical model with parameters determined by analysis of the scintillation pulse measured from a NaI(Tl) detector. To simulate realistic scintillation pulses, Gaussian noises corresponding to thermal and shot noises were added to the signals. The trained model was validated with signals measured from two gamma-ray sources, $^{\mathbf {22}}$ Na and $^{\mathbf {137}}$ Cs. The model was then evaluated using two performance indicators, restoration and separation rates, which represent how much the net count is restored in the region of interest (ROI) and the separation accuracy depending on the time interval. As a result, the deep learning model was confirmed to correctly estimate the pulse heights in a high pile-up environment up to certain restoration and separation rates.

Deep Learning-Based Pulse Height Estimation for Separation of Pile-Up Pulses From NaI(Tl) Detector

Dose-rate monitoring in workspaces plays an important role in protecting workers from radiation. Operational quantities are utilized to assess occupational exposures of workers. Typical detector materials for measuring these quantities include ion chambers or high-density materials. However, plastic scintillators are rarely used because of the absence of photo-peaks. We developed a deep learning–based method that can predict the ambient dose equivalent (H*(10))—a representative operational quantity—from measured spectra of plastic scintillation detectors to overcome their drawbacks in terms of spectroscopic dosimetry applications. To train the deep learning model, numerous gamma spectra with arbitrary energies of gamma rays and their H*(10) were calculated by Monte Carlo simulations and used as the dataset. Several neural network models were implemented by the Bayesian-based hyperparameter optimizations, and an ensemble model was used as the final model to enhance accuracy and generalization ability. The performance of the ensemble model was verified using simulated and measured spectra for representative radioisotopes. Furthermore, we confirmed that dose-rate prediction errors of the model were within acceptable uncertainty ranges suggested by the IAEA safety guide and that energy responses of the model satisfied IEC requirements. 

Deep learning-based spectrum-dose prediction for a plastic scintillation detector

The study presents a novel approach to analysing the thermoluminescence (TL) glow curves (GCs) of CaSO4:Dy-based personnel monitoring dosimeters using machine learning (ML). This study demonstrates the qualitative and quantitative impact of different types of anomalies on the TL signal and trains ML algorithms to estimate correction factors (CFs) to account for these anomalies. The results show a good degree of agreement between the predicted and actual CFs, with a coefficient of determination greater than 0.95, a root mean square error less than 0.025, and a mean absolute error less than 0.015. The use of ML algorithms leads to a significant two-fold reduction in the coefficient of variation of TL counts from anomalous GCs. This study proposes a promising approach to address anomalies caused by dosimeter, reader, and handling-related factors. Furthermore, it accounts for non-radiation-induced TL at low dose levels towards improving the dosimetric accuracy in personnel monitoring.

/pdf/a-machine-learning-approach-for-correcting-glow-curve-2o5h14ej.pdf

A machine learning approach for correcting glow curve anomalies in CaSO4:Dy-based TLD dosimeters used in personnel monitoring

Personal dosemeters using thermoluminescence detectors can provide information about the irradiation event beyond the pure dose estimation, which is valuable for improving radiation protection measures. In the presented study, the glow curves of the novel TL-DOS dosemeters developed by the Materialprüfungsamt NRW in cooperation with the TU Dortmund University are analysed using deep learning approaches to predict the irradiation date of a single-dose irradiation of 10 mGy within a monitoring interval of 41 d. In contrast of previous work, the glow curves are measured using the current routine read-out process by pre-heating the detectors before the read-out. The irradiation dates are predicted with an accuracy of 2-5 d by the deep learning algorithm. Furthermore, the importance of the input features is evaluated using Shapley values to increase the interpretability of the neural network.

Deep TL: progress of a machine learning aided personal dose monitoring system.

The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a single irradiation with 12 mSv within a monitoring interval of 42 days from glow curves of novel TL-DOS personal dosimeters developed by the Materialprufungsamt NRW in cooperation with TU Dortmund University. Using a deep convolutional network, the irradiation date can be predicted from raw time-resolved glow curve data with an uncertainty of roughly 1-2 days on a 68% confidence level without the need for a prior transformation into temperature space and a subsequent glow curve deconvolution. This corresponds to a significant improvement in prediction accuracy compared to a prior publication, which yielded a prediction uncertainty of 2-4 days using features obtained from a glow curve deconvolution as input to a neural network.

No more glowing in the dark: how deep learning improves exposure date estimation in thermoluminescence dosimetry.

No more glowing in the dark: How deep learning improves exposure date estimation in thermoluminescence dosimetry

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No more glowing in the dark: how deep learning improves exposure date estimation in thermoluminescence dosimetry.

No more glowing in the dark: How deep learning improves exposure date estimation in thermoluminescence dosimetry