NDLI: Comparison of optimization techniques for regularized statistical reconstruction in X-ray tomography

Content Provider	IEEE Xplore Digital Library
Author	Hamelin, B. Goussard, Y. Dussault, J.-P.
Copyright Year	2010
Description	Author affiliation: Département d'informatique, Université de Sherbrooke, Québec, Canada (Dussault, J.-P.) \|\| Institut de génie biomédical, École Polytechnique de Montréal, Québec, Canada (Hamelin, B.; Goussard, Y.)
Abstract	Numerical efficiency and convergence are matters of importance for regularized statistical reconstruction in X-ray tomography. We propose a performance comparison of four numerical methods that fall into two categories: first, variants of the SPS framework, a modern take on expectation-maximization-type algorithms, that benefit from acceleration through ordered subset strategies and were developed specifically for tomographic reconstruction; second, Hessian-free general-purpose nonlinear solvers with bound constraints, used to minimize directly the regularized objective function. The comparison is established on a common target for the noise-to-resolution trade-off of the reconstructed images. The experiments show that while the ordered-subsets separable paraboloidal surrogate iteration variant is the fastest to reach the target, its nonconvergent nature precludes the use of a rigorous stopping rule. Conversely, the other three methods are convergent and can be stopped using a common criterion related to the noise-to-resolution target. Among convergent techniques, general purpose solvers achieve the highest efficiency.
Starting Page	87
Ending Page	91
File Size	1127904
Page Count	5
File Format	PDF
ISBN	9781424472475
ISSN	2154512X
e-ISBN	9781424472499
DOI	10.1109/IPTA.2010.5586755
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution numerical methods Noise Reconstruction algorithms expectation-maximization Image reconstruction Optimization Convergence nonlinear optimization ordered subsets Computed tomography regularized statistical reconstruction X-ray tomography
Content Type	Text
Resource Type	Article

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