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• W2127854932 abstract "The maximum-likelihood (ML) expectation-maximization (EM) [ML-EM] algorithm is being widely used for image reconstruction in positron emission tomography. The algorithm is strictly valid if the data are Poisson distributed. However, it is also often applied to processed sinograms that do not meet this requirement. This may sometimes lead to suboptimal results: streak artifacts appear and the algorithm converges toward a lower likelihood value. As a remedy, the authors propose two simple pixel-by-pixel methods [noise equivalent counts (NEC)-scaling and NEC-shifting] in order to transform arbitrary sinogram noise into noise which is approximately Poisson distributed (the first and second moments of the distribution match those of the Poisson distribution). The convergence speed associated with both transformation methods is compared, and the NEC-scaling method is validated with both simulations and clinical data. These new methods extend the ML-EM algorithm to a general purpose nonnegative reconstruction algorithm." @default.
• W2127854932 created "2016-06-24" @default.
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• W2127854932 date "2001-05-01" @default.
• W2127854932 modified "2023-09-26" @default.
• W2127854932 title "Maximum-likelihood expectation-maximization reconstruction of sinograms with arbitrary noise distribution using NEC-transformations" @default.
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• W2127854932 doi "https://doi.org/10.1109/42.925290" @default.
• W2127854932 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/11403196" @default.
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