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Proceedings Paper

Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT
Author(s): Eri Haneda; Jiajia Luo; Ali Can; Sathish Ramani; Lin Fu; Bruno De Man
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Paper Abstract

In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2

A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

Paper Details

Date Published: 12 May 2016
PDF: 11 pages
Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470J (12 May 2016); doi: 10.1117/12.2222323
Show Author Affiliations
Eri Haneda, GE Global Research Ctr. (United States)
Jiajia Luo, GE Global Research Ctr. (United States)
Ali Can, GE Global Research Ctr. (United States)
Sathish Ramani, GE Global Research Ctr. (United States)
Lin Fu, GE Global Research Ctr. (United States)
Bruno De Man, GE Global Research Ctr. (United States)


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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