Artificial neural networks for positioning of gamma interactions in monolithic PET detectors.
Compton scatter
monolithic PET detector
neural networks
optical simulation
spatial resolution
Journal
Physics in medicine and biology
ISSN: 1361-6560
Titre abrégé: Phys Med Biol
Pays: England
ID NLM: 0401220
Informations de publication
Date de publication:
23 03 2021
23 03 2021
Historique:
received:
24
11
2020
accepted:
04
03
2021
pubmed:
5
3
2021
medline:
26
3
2022
entrez:
4
3
2021
Statut:
epublish
Résumé
To detect gamma rays with good spatial, timing and energy resolution while maintaining high sensitivity we need accurate and efficient algorithms to estimate the first gamma interaction position from the measured light distribution. Furthermore, monolithic detectors are investigated as an alternative to pixelated detectors due to increased sensitivity, resolution and intrinsic DOI encoding. Monolithic detectors, however, are challenging because of complicated calibration setups and edge effects. In this work, we evaluate the use of neural networks to estimate the 3D first (Compton or photoelectric) interaction position. Using optical simulation data of a 50 × 50 × 16 mm
Identifiants
pubmed: 33662940
doi: 10.1088/1361-6560/abebfc
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2021 Institute of Physics and Engineering in Medicine.