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Deep learning based sparse view x-ray CT reconstruction for checked baggage screening
Author(s): Sagar Mandava; Amit Ashok; Ali Bilgin
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Paper Abstract

X-ray computed tomography is widely used in security applications. With growing interest in view-limited systems, which have increased throughput, there is a significant interest in constrained image reconstruction techniques that allows high fidelity reconstruction from limited data. These image reconstruction techniques are commonly characterized by their intense computational requirements making their deployment in real-time imaging applications challenging. Recent success of deep learning techniques in various signal and image processing applications has sparked an interest in using these techniques for image reconstruction problems. In this work, we explore the use of deep learning techniques for reconstruction of baggage CT data and compare these techniques to constrained reconstruction methods.

Paper Details

Date Published: 27 April 2018
PDF: 8 pages
Proc. SPIE 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III, 1063204 (27 April 2018); doi: 10.1117/12.2309509
Show Author Affiliations
Sagar Mandava, The Univ. of Arizona (United States)
Amit Ashok, The Univ. of Arizona (United States)
College of Optical Sciences, The Univ. of Arizona (United States)
Ali Bilgin, The Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 10632:
Anomaly Detection and Imaging with X-Rays (ADIX) III
Amit Ashok; Joel A. Greenberg; Michael E. Gehm; Mark A. Neifeld, Editor(s)

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