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

Mehranian, Abolfazl; Rahmim, Arman; Ay, Mohammad Reza; Kotasidis, Fotis; Zaidi, Habib

doi:10.1118/1.4801898

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Title		An ordered-subsets proximal preconditioned gradient algorithm for edge-preserving PET image reconstruction
Authors		Mehranian, Abolfazl Rahmim, Arman Ay, Mohammad Reza Kotasidis, Fotis Zaidi, Habib
Published in		Medical physics. 2013, vol. 40, no. 5, 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		Algorithms — Brain/radionuclide imaging — Fluorodeoxyglucose F18/diagnostic use — Humans — Image Processing, Computer-Assisted/methods — Positron-Emission Tomography/methods
Identifiers		DOI: 10.1118/1.4801898 PMID: 23635293
Full text		Article (Published version) (1.5 MB) - Limited access to UNIGE
Structures		Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale
Research group		Imagerie Médicale (TEP et TEMP) (542)
Project		Swiss National Science Foundation: 31003A_149957
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 https://archive-ouverte.unige.ch/unige:40054