Block-iterative Fisher scoring algorithms for maximum penalized likelihood image reconstruction in emission tomography | Macquarie University ResearchOnline

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Title: Block-iterative Fisher scoring algorithms for maximum penalized likelihood image reconstruction in emission tomography
Related: IEEE transactions on medical imaging, Vol. 27, Issue 8, (2008), p.1130-1142
DOI: 10.1109/TMI.2008.918355
Publisher: IEEE
Date: 2008
FoR/RFCD Code(s): 080200 Computation Theory and Mathematics
Author/Creator: Ma, Jun
Author/Creator: Hudson, Harold
Description: This paper introduces and evaluates a block-iterative Fisher scoring (BFS) algorithm. The algorithm provides regularized estimation in tomographic models of projection data with Poisson variability. Regularization is achieved by penalized likelihood with a general quadratic penalty. Local convergence of the block-iterative algorithm is proven under conditions that do not require iteration dependent relaxation. We show that, when the algorithm converges, it converges to the unconstrained maximum penalized likelihood (MPL) solution.
Description: 13 page(s)
Subject Keyword: 080200 Computation Theory and Mathematics
Subject Keyword: Block-iterative Fisher scoring
Subject Keyword: convex optimization
Subject Keyword: penalized likelihood
Subject Keyword: emission tomography
Subject Keyword: OS-EM
Subject Keyword: BSREM
Subject Keyword: OS-SPS
Resource Type: journal article
Organisation: Macquarie University. Dept. of Statistics

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