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Scientific article
English

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

Published inMedical physics, vol. 40, no. 5, 052503
Publication date2013
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
  • Algorithms
  • Brain/radionuclide imaging
  • Fluorodeoxyglucose F18/diagnostic use
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Positron-Emission Tomography/methods
Citation (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. doi: 10.1118/1.4801898
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Article (Published version)
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ISSN of the journal0094-2405
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Technical informations

Creation08/05/2014 8:43:00 PM
First validation08/05/2014 8:43:00 PM
Update time03/14/2023 9:44:48 PM
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