Lux Ex Machina – an application of machine learning in glow curve deconvolution

• Applicant: Professor Dr. Kevin Kröninger
• Subject Area: Medical Physics, Biomedical Technology
• Term: from 2019 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 430692585

Personal dosimetry is an important aspect of radiation protection, in particular in medical fields such as radiology or nuclear medicine. It is also a relevant field of research as current technologies for dose monitoring, in particular film badge dosimeters, are pushed to their limits. Viable alternatives are e.g. based on the well-known phenomenon of thermoluminescence (TL). The TL-DOS dosimetry system, jointly developed by the Materialprüfungsamt NRW, Dortmund, and the TU Dortmund University, uses a thin-layer of LiF:Mg,Ti as thermoluminescent material and fast heating procedures. The system can be used to reliably estimate irradiation doses by counting the number of photons emitted during the heating process and a proper calibration.The aim of the project proposed here is to maximise the information obtained from the time-resolved photon signal, the glow curve, by simulating the fundamental TL processes and by using modern and powerful machine learning algorithms. The information strived for is the irradiation dose, the time between irradiation and read-out, the number of irradiations within one duty cycle and the type of radiation seen by the dosimeter. Based on simulation data and data obtained from well-defined irradiation campaigns with photons and neutrons, the machine learning algorithms will be trained, tested and validated, and the full potential of glow curve deconvolution will be explored.

• DFG Programme: Research Grants
• Co-Investigator: Dr. Jörg Walbersloh