A neural network-based algorithm for simultaneous event positioning and timestamping in monolithic scintillators

Objective Monolithic scintillator crystals coupled to silicon photomultiplier (SiPM) arrays are promising detectors for PET applications, offering spatial resolution around 1 mm and depth-of-interaction information. However, their timing resolution has always been inferior to that of pixellated crys...

Full description

Saved in:
Bibliographic Details
Published in:Physics in medicine & biology 2022-07, Vol.67 (13), p.135001
Main Authors: Carra, Pietro, Giuseppina Bisogni, Maria, Ciarrocchi, Esther, Morrocchi, Matteo, Sportelli, Giancarlo, Rosso, Valeria, Belcari, Nicola
Format: Article
Language:English
Subjects:
artificial intelligence
event positioning
event timestamping
monolithic scintillators
neural networks
PET detectors
silicon photomultipliers
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Objective Monolithic scintillator crystals coupled to silicon photomultiplier (SiPM) arrays are promising detectors for PET applications, offering spatial resolution around 1 mm and depth-of-interaction information. However, their timing resolution has always been inferior to that of pixellated crystals, while the best results on spatial resolution have been obtained with algorithms that cannot operate in real-time in a PET detector. In this study, we explore the capabilities of monolithic crystals with respect to spatial and timing resolution, presenting new algorithms that overcome the mentioned problems. Approach Our algorithms were tested first using a simulation framework, then on experimentally acquired data. We tested an event timestamping algorithm based on neural networks which was then integrated into a second neural network for simultaneous estimation of the event position and timestamp. Both algorithms are implemented in a low-cost field-programmable gate array that can be integrated in the detector and can process more than 1 million events per second in real-time. Results Testing the neural network for the simultaneous estimation of the event position and timestamp on experimental data we obtain 0.78 mm 2-D FWHM on the (x,y) plane, 1.2 mm depth-of-interaction FWHM and 156 ps coincidence time resolution on a 25.6 mm x 25.6 mm x 8 mm LYSO monolith read-out by 64 3 mm x 3 mm Hamamatsu SiPMs. Significance Our results show that monolithic crystals combined with artificial intelligence can rival pixellated crystals performance for time-of-flight PET applications, while having better spatial resolution and DOI resolution. Thanks to the use of very light neural networks, event characterization can be done on-line directly in the detector, solving the issues of scalability and computational complexity that up to now were preventing the use of monolithic crystals in clinical PET scanners.
ISSN:0031-9155
1361-6560
DOI:10.1088/1361-6560/ac72f2