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Title

An ordered-subsets proximal preconditioned gradient algorithm for edge-preserving PET image reconstruction

Authors
Rahmim, Arman
Published in Medical Physics. 2013, vol. 40, no. 5, p. 052503
Abstract In iterative positron emission tomography (PET) image reconstruction, the statistical variability of the PET data precorrected for random coincidences or acquired in sufficiently high count rates can be properly approximated by a Gaussian distribution, which can lead to a penalized weighted least-squares (PWLS) cost function. In this study, the authors propose a proximal preconditioned gradient algorithm accelerated with ordered subsets (PPG-OS) for the optimization of the PWLS cost function and develop a framework to incorporate boundary side information into edge-preserving total variation (TV) and Huber regularizations.
Keywords AlgorithmsBrain/radionuclide imagingFluorodeoxyglucose F18/diagnostic useHumansImage Processing, Computer-Assisted/methodsPositron-Emission Tomography/methods
Identifiers
PMID: 23635293
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Article (Published version) (1.5 MB) - document accessible for UNIGE members only Limited access to UNIGE
Structures
Research group Imagerie Médicale (TEP et TEMP) (542)
Project FNS: SNF 31003A-149957
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(ISO format)
MEHRANIAN, Abolfazl et al. An ordered-subsets proximal preconditioned gradient algorithm for edge-preserving PET image reconstruction. In: Medical Physics, 2013, vol. 40, n° 5, p. 052503. https://archive-ouverte.unige.ch/unige:40054

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Deposited on : 2014-09-09

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