Sub-millimeter precise photon interaction position determination in large monolithic scintillators via convolutional...

In this work, we present the development and application of a convolutional neural network (CNN)-based algorithm to precisely determine the interaction position ofγ-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: LaBr₃:Ce and CeBr₃. 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 collimated¹³⁷Cs and⁶⁰Co 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 10⁴events per second with only one GPU. Those improvements are crucial on the way to future clinicalin vivoapplicability of the CC for ion beam range verification.