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Proceedings Paper

Alternating minimization multigrid algorithms for transmission tomography
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Paper Abstract

The problem of image formation for X-ray transmission tomography is formulated as a statistical inverse problem. The maximum likelihood estimate of the attenuation function is sought. Using convex optimization methods, maximizing the loglikelihood functional is equivalent to a double minimization of I-divergence, one of the minimizations being over the attenuation function. Restricting the minimization over the attenuation function to a coarse grid component forms the basis for a multigrid algorithm that is guaranteed to monotonically decrease the I-divergence at every iteration on every scale.

Paper Details

Date Published: 21 May 2004
PDF: 6 pages
Proc. SPIE 5299, Computational Imaging II, (21 May 2004); doi: 10.1117/12.537508
Show Author Affiliations
Joseph A. O'Sullivan, Washington Univ. (United States)
Jasenka Benac, Washington Univ. (United States)


Published in SPIE Proceedings Vol. 5299:
Computational Imaging II
Charles A. Bouman; Eric L. Miller, Editor(s)

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