@inproceedings{dcaa5214811b4e759f25184114078d3c,
title = "Compton Decomposition and Recovery in a Prism-PET Detector Module",
abstract = "Identifying the correct Line-of-Response (LOR) in positron emission tomography (PET) requires accurate localization of the first interaction between incident gamma ray and detector. Improving the accuracy of this localization typically entails offsetting losses in detector efficiency, complexity, or cost. In this paper, we propose a solution that can localize scattered gammas without losses in detector efficiency, utilizing Prism-PET - a single-sided detector module with pixelated light guide. Using Monte Carlo simulations of gamma-detector interactions, we train a Convolutional Neural Network to predict the first interaction position of gammas incident on a detector block.",
keywords = "Compton scattering, Light guide, PET, Prism",
author = "Petersen, \{Eric W.\} and Andy LaBella and Adrian Howansky and Wei Zhao and Goldan, \{Amir H.\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE; 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020 ; Conference date: 31-10-2020 Through 07-11-2020",
year = "2020",
doi = "10.1109/NSS/MIC42677.2020.9507898",
language = "English",
series = "2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020",
}