Alternating minimization multigrid algorithms for transmission tomography

Joseph A. O'Sullivan; Jasenka Benac

doi:10.1117/12.537508

21 May 2004 Alternating minimization multigrid algorithms for transmission tomography

Joseph A. O'Sullivan, Jasenka Benac

Proceedings Volume 5299, Computational Imaging II; (2004) https://doi.org/10.1117/12.537508
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

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.

Citation Download Citation

Joseph A. O'Sullivan and Jasenka Benac "Alternating minimization multigrid algorithms for transmission tomography", Proc. SPIE 5299, Computational Imaging II, (21 May 2004); https://doi.org/10.1117/12.537508

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