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Analysis of Qualitative Algorithms in Iterative Reconstruction of PET Data
Abstract
This paper makes a comparison between the slow globally convergent list-mode COSEM and the fast but nonconvergent classical list-mode OSEM methods and produce a way to get a fast and effective COSEM called the enhanced COSEM(ECOSEM). The former globally convergent algorithm is based on the ordered sets expectation maximization algorithm for binned data but has several extensions that make it suitable for two planar detector tomography that are rotating but experience certain limitations. The image estimate measured from the PET scanner is incrementally updated for each coincidence event measured. The events obtained are used as soon as possible, that improves the current image estimate, and therefore the convergence speed towards the maximum-likelihood is accelerated. We provide a simulated result to overcome those limitations and to reconstruct the image with maximum-likelihood.
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