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Proceedings Paper

CT reconstruction via denoising approximate message passing
Author(s): Alessandro Perelli; Michael A. Lexa; Ali Can; Mike E. Davies
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Paper Abstract

In this paper, we adapt and apply a compressed sensing based reconstruction algorithm to the problem of computed tomography reconstruction for luggage inspection. Specifically, we propose a variant of the denoising generalized approximate message passing (D-GAMP) algorithm and compare its performance to the performance of traditional filtered back projection and to a penalized weighted least squares (PWLS) based reconstruction method. D-GAMP is an iterative algorithm that at each iteration estimates the conditional probability of the image given the measurements and employs a non-linear "denoising" function which implicitly imposes an image prior. Results on real baggage show that D-GAMP is well-suited to limited-view acquisitions.

Paper Details

Date Published: 12 May 2016
PDF: 8 pages
Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470O (12 May 2016); doi: 10.1117/12.2224147
Show Author Affiliations
Alessandro Perelli, The Univ. of Edinburgh (United Kingdom)
Michael A. Lexa, GE Global Research Ctr. (United States)
Ali Can, GE Global Research Ctr. (United States)
Mike E. Davies, The Univ. of Edinburgh (United Kingdom)


Published in SPIE Proceedings Vol. 9847:
Anomaly Detection and Imaging with X-Rays (ADIX)
Amit Ashok; Mark A. Neifeld; Michael E. Gehm, Editor(s)

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