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