Simulation of Lu$_2$SiO$_{5-z}$:Y$^{3+}$:Ce$^{3+}$:Ca$^{2+}$ monolithic detectors for positron emission tomography

V. S. Tskhai, M. V. Belov, V. A. Kozlov, M. V. Zavertyaev

P. N. Lebedev Physical Institute, Russian Academy of Sciences, Moscow

Abstract: We evaluate the performance of monolithic positron emission tomography (PET) detector elements depending on its scintillator crystal plate thickness (6 and 12 mm) and the surface finish (rough or polished) using a neural network to reconstruct events. A GEANT4 PET detector model was used for this study. It consisted of a LYSO crystal with a 57.6 $\times$ 57.6 mm$^2$ face and a 64-channel Sensl ARRAYC-60035-64P-PCB photomultiplier. Separate runs were made with varying crystal parameters – thickness (6 and 12 mm) and surface finish (rough and polished) resulting in four separate event pools. A feed-forward neural network was used to reconstruct the point of 511 keV $\gamma$ interaction. The number of layers and neurons per layer were varied. The best resolution was achieved with a 6 mm thick detector with a rough finish with an average of 0.57 $\pm$ 0.01 mm for the XY plane and an average 0.89 $\pm$ 0.01 mm for the Z coordinate (depth of interaction), and a dR of 1.19 $\pm$ 0.01 mm.

Keywords: scintillating crystals, $\gamma$ radiation, positron emission tomography, neural networks.

Received: 10.06.2025
Revised: 21.07.2025
Accepted: 23.07.2025

DOI: 10.21883/0000000000

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