Sub-millimeter precise photon interaction position determination in large monolithic scintillators via convolutional neural network algorithms

• Published in: Physics in medicine & biology Vol. 66; no. 13; pp. 135017 - 135026
• Main Authors: Kawula, M, Binder, T M, Liprandi, S, Viegas, R, Parodi, K, Thirolf, P G
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 02.07.2021
• Subjects: Algorithms; beam range monitoring; Compton camera; hadron therapy; monolithic scintillator; neural networks; Neural Networks, Computer; Photons; radiation detection; Radionuclide Imaging; Scintillation Counting; spatial resolution; radiation detection; beam range monitoring; Compton camera; neural networks; spatial resolution; monolithic scintillator; hadron therapy
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/ac06e2

In this work, we present the development and application of a convolutional neural network (CNN)-based algorithm to precisely determine the interaction position of γ -quanta in large monolithic scintillators. Those are used as an absorber component of a Compton camera (CC) system under development for ion beam range verification via prompt-gamma imaging. We examined two scintillation crystals: LaBr 3 :Ce and CeBr 3 . Each crystal had dimensions of 50.8 mm × 50.8 mm × 30 mm and was coupled to a 64-fold segmented multi-anode photomultiplier tube (PMT) with an 8 × 8 pixel arrangement. We determined the spatial resolution for three photon energies of 662, 1.17 and 1.33 MeV obtained from 2D detector scans with tightly collimated 137 Cs and 60 Co photon sources. With the new algorithm we achieved a spatial resolution for the CeBr3 crystal below 1.11(8) mm and below 0.98(7) mm for the LaBr3:Ce detector for all investigated energies between 662 keV and 1.33 MeV. We thereby improved the performance by more than a factor of 2.5 compared to the previously used categorical average pattern algorithm, which is a variation of the well-established k-nearest neighbor algorithm. The trained CNN has a low memory footprint and enables the reconstruction of up to 10 4 events per second with only one GPU. Those improvements are crucial on the way to future clinical in vivo applicability of the CC for ion beam range verification.