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Abstract

The Jagiellonian Positron Emission Tomograph (J-PET) collaboration is developing a prototype time of flight (TOF)-positron emission tomograph (PET) detector based on long polymer scintillators. This novel approach exploits the excellent time properties of the plastic scintillators, which permit very precise time measurements. The very fast field programmable gate array (FPGA)-based front-end electronics and the data acquisition system, as well as low- and high-level reconstruction algorithms were specially developed to be used with the J-PET scanner. The TOF-PET data processing and reconstruction are time and resource demanding operations, especially in the case of a large acceptance detector that works in triggerless data acquisition mode. In this article, we discuss the parallel computing methods applied to optimize the data processing for the J-PET detector. We begin with general concepts of parallel computing and then we discuss several applications of those techniques in the J-PET data processing.

DOI: https://doi.org/10.1515/nuka-2015-0134 | Journal eISSN: 1508-5791 | Journal ISSN: 0029-5922
Language: English
Page range: 745 - 748
Submitted on: Jul 6, 2015
Accepted on: Aug 25, 2015
Published on: Dec 1, 2015
Published by: Institute of Nuclear Chemistry and Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2015 Wojciech Krzemień, Mateusz Bała, Tomasz Bednarski, Piotr Białas, Eryk Czerwiński, Aleksander Gajos, Marek Gorgol, Bożena Jasińska, Daria Kamińska, Łukasz Kapłon, Grzegorz Korcyl, Paweł Kowalski, Tomasz Kozik, Ewelina Kubicz, Szymon Niedźwiecki, Marek Pałka, Lech Raczyński, Zbigniew Rudy, Oleksandr Rundel, Neha Gupta Sharma, Michał Silarski, Artur Słomski, Karol Stola, Adam Strzelecki, Damian Trybek, Anna Wieczorek, Wojciech Wiślicki, Marcin Zieliński, Bożena Zgardzińska, Paweł Moskal, published by Institute of Nuclear Chemistry and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.