Feasibility of Using Neural Networks to Unfold the Response of Multi-Element TLD for Mixed Field Dosimetry

Significant advances have been made in recent years to improve calibration methodology and dose calculation algorithm in the fields of TL dosimetry. This process was accelerated in the past decade particularly in the Republic of Korea by the need to meet mandatory national accreditation requirements. The objective of this study is to develop a new algorithm to replace the simplistic decision tree algorithms by the more sophisticated neural networks in hopes of achieving a higher degree of accuracy and precision in personnel dosimetry system. The original hypothesis of this work is that the spectral information of an X and γ-ray fields may be obtained by the analysis of the response of a multi-element system. In this study, a feed forward neural network using the error back-propagation method with Bayesian optimization was designed for the response unfolding procedure. The response functions of the single element to photons were calculated by application of a computational Monte-Carlo model for an energy range from 10keV to 2MeV with different spectral proportions. The training of the artificial neural network was based on the computation of responses of a four-element system for the back-propagation method. The validation of the proposed algorithm was investigated by unfolding the 10 computed responses for arbitrary mixed gamma fields and the spectra resulting from the unfolding procedure agree well with the original spectra.