Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors

Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors

Abstract Background The aim of this study was to evaluate and compare PET image reconstruction algorithms on novel digital silicon photomultiplier PET/CT in patients with newly diagnosed and histopathologically confirmed lung cancer. A total of 45 patients undergoing 18F-FDG PET/CT for initial lung...

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Bibliographic Details
Main Authors: Michael Messerli, Paul Stolzmann, Michèle Egger-Sigg, Josephine Trinckauf, Stefano D’Aguanno, Irene A. Burger, Gustav K. von Schulthess, Philipp A. Kaufmann, Martin W. Huellner
Format: Article
Language:English
Published: SpringerOpen 2018-09-01
Series:EJNMMI Physics
Subjects:
Positron-emission tomography
Lung cancer
Image reconstruction
PET/CT
Image quality enhancement
Online Access:http://link.springer.com/article/10.1186/s40658-018-0223-x

Internet

http://link.springer.com/article/10.1186/s40658-018-0223-x

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