Abstract
The maximum likelihood expectation maximization (ML-EM) is an attractive approach for image reconstruction in positron emission tomography (PET) since it considers the Poisson character of the emission data and it converges to the maximum likelihood solution. However, the algorithm possesses a very slow rate of convergence, and in practical applications it has to be terminated well before approaching the maximum likelihood solution. Recently, a row action maximum likelihood algorithm (RAMLA) was proposed for maximizing the Poisson likelihood at a considerably faster rate of convergence. We examine the performance of this new algorithm for fully 3D PET reconstruction since it enables us to study, within a reasonable amount of computer time, the images produced at much higher likelihood than would be produced by the standard ML-EM within the same period of time.
This work was supported by the National Institutes of Health under Grants HL28438 and CA-54356. S.Matej is on leave from the Inst. of Measurement Science, SAS, Bratislava, Slovak Republic. J.A. Browne is now with ARACOR, 425 Lakeside Drive, Sunnyvale, CA 94086. E-mail address of the first author is matej@mipg.upenn.edu.
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Matej, S., Browne, J.A. (1996). Performance of a Fast Maximum Likelihood Algorithm for Fully 3D PET Reconstruction. In: Grangeat, P., Amans, JL. (eds) Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. Computational Imaging and Vision, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8749-5_21
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DOI: https://doi.org/10.1007/978-94-015-8749-5_21
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