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Proceedings Paper

Acceleration of ML iterative algorithms for CT by the use of fast start images
Author(s): Kevin M. Brown; Stanislav Zabic; Thomas Koehler
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Paper Abstract

This report develops a new strategy for the acceleration of a maximum likelihood (ML) iterative reconstruction algorithm for CT, by selecting a starting image which is closer to the solution of the ML algorithm than the commonly used filtered backprojection image. The starting image is obtained by passing both the acquired projection data and the reconstructed volume though a novel de-noising algorithm which uses the same image penalty function as the ML reconstruction. Clinical examples suggest that a savings of 5-10 iterations of the separable paraboloidal surrogates algorithm per volume is possible when using this new acceleration strategy.

Paper Details

Date Published: 3 March 2012
PDF: 7 pages
Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831339 (3 March 2012); doi: 10.1117/12.911412
Show Author Affiliations
Kevin M. Brown, Philips Healthcare (United States)
Stanislav Zabic, Philips Healthcare (United States)
Thomas Koehler, Philips Technologie GmbH (Germany)


Published in SPIE Proceedings Vol. 8313:
Medical Imaging 2012: Physics of Medical Imaging
Norbert J. Pelc; Robert M. Nishikawa; Bruce R. Whiting, Editor(s)

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