CT reconstruction via denoising approximate message passing

Alessandro Perelli; Michael A. Lexa; Ali Can; Mike E. Davies

doi:10.1117/12.2224147

12 May 2016 CT reconstruction via denoising approximate message passing

Alessandro Perelli, Michael A. Lexa, Ali Can, Mike E. Davies

Proceedings Volume 9847, Anomaly Detection and Imaging with X-Rays (ADIX); 98470O (2016) https://doi.org/10.1117/12.2224147
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Alessandro Perelli, Michael A. Lexa, Ali Can, and Mike E. Davies "CT reconstruction via denoising approximate message passing", Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470O (12 May 2016); https://doi.org/10.1117/12.2224147

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