Resolution estimation in different monolithic PET detectors using neural networks

• Published in: Physica medica Vol. 106; p. 102527
• Main Authors: Belov, M.V., Kozlov, V.A., Tskhay, V.S., Zavertyaev, M.V.
• Format: Journal Article
• Language: English
• Published: Italy Elsevier Ltd 01.02.2023
• Subjects: Detector elements; Gamma Rays; Imaging; Monolithic; Neural networks; Neural Networks, Computer; Nuclear physics; PET; Positron-Emission Tomography; Scintillation Counting; Tomography; Neural networks; Imaging; Tomography; PET; Nuclear physics; Detector elements; Monolithic
• ISSN: 1120-1797; 1724-191X
• DOI: 10.1016/j.ejmp.2023.102527

•Neural network application for event reconstruction can approach the best physically possible precision.•A neural network reconstruction algorithm gives a good estimation of expected performance.•Comparing two realistic detector models with neural network reconstruction allows to evaluate the potential benefits with an unbiased algorithm.•Larger thinner crystal plates provide the best overall resolution, smaller plates reduce the maximal possible error. We use neural networks to evaluate and compare the spatial resolution of two different simulated monolithic PET detector elements. The effects of mixing events with single photoeffect interactions and multiple Compton scatterings are also studied. Two PET detector models were used in this study. The first one consisted of a LYSO crystal plate with 19.25 × 19.25 × 12 mm3 dimensions and 256-channel photomultiplier with parameters modeled after a Hamamatsu S-13615-1050N-16 SiPM. The second model used a larger LYSO crystal (57.6 × 57.6 × 12 mm3) and a 64-channel Sensl ARRAYC-60035-64P-PCB photomultiplier. A feed-forward neural network was used to reconstruct the point of 511 keV gamma interaction. The number of layers and the number of neurons per layer were varied. The best resolution was achieved with the 57.6 × 57.6 mm2 detector model, with an average of 0.74 ± 0.01 mm for the XY plane and an average 1.01 ± 0.01 mm for the Z coordinate (depth of interaction). Neural networks can be a powerful tool that can help to determine the optimal parameters for a design of an experimental device. This study demonstrates how neural networks can be used to evaluate the performance of two detector variants while not being dependent on specific signal and noise functions.