1. Compton Decomposition and Recovery in a Prism-PET Detector Module
- Author
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Andy LaBella, Wei Zhao, Adrian Howansky, Amir H. Goldan, and Eric Petersen
- Subjects
Physics ,Physics::Instrumentation and Detectors ,Image quality ,business.industry ,Astrophysics::High Energy Astrophysical Phenomena ,Detector ,Monte Carlo method ,Gamma ray ,Convolutional neural network ,Optics ,Position (vector) ,High Energy Physics::Experiment ,Prism ,business ,Block (data storage) - 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.
- Published
- 2020
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