Deep learning based sparse view x-ray CT reconstruction for checked baggage screening

Sagar Mandava; Amit Ashok; Ali Bilgin

doi:10.1117/12.2309509

Deep learning based sparse view x-ray CT reconstruction for checked baggage screening

Sagar Mandava, Amit Ashok, Ali Bilgin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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.

Original language	English (US)
Title of host publication	Anomaly Detection and Imaging with X-Rays (ADIX) III
Editors	Michael E. Gehm, Joel A. Greenberg, Amit Ashok, Mark A. Neifeld
Publisher	SPIE
ISBN (Electronic)	9781510617759
DOIs	https://doi.org/10.1117/12.2309509
State	Published - 2018
Event	Anomaly Detection and Imaging with X-Rays (ADIX) III 2018 - Orlando, United States Duration: Apr 17 2018 → Apr 18 2018

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	10632
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Other

Other	Anomaly Detection and Imaging with X-Rays (ADIX) III 2018
Country/Territory	United States
City	Orlando
Period	4/17/18 → 4/18/18

Keywords

CT reconstruction
Compressed sensing
Constrained reconstruction
Deep learning
Security screening
Sparse view
Transportation security
X-ray computed tomography

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2309509

Cite this

Mandava, S., Ashok, A., & Bilgin, A. (2018). Deep learning based sparse view x-ray CT reconstruction for checked baggage screening. In M. E. Gehm, J. A. Greenberg, A. Ashok, & M. A. Neifeld (Eds.), Anomaly Detection and Imaging with X-Rays (ADIX) III Article 1063204 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10632). SPIE. https://doi.org/10.1117/12.2309509

Deep learning based sparse view x-ray CT reconstruction for checked baggage screening. / Mandava, Sagar; Ashok, Amit ; Bilgin, Ali.
Anomaly Detection and Imaging with X-Rays (ADIX) III. ed. / Michael E. Gehm; Joel A. Greenberg; Amit Ashok; Mark A. Neifeld. SPIE, 2018. 1063204 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10632).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mandava, S, Ashok, A & Bilgin, A 2018, Deep learning based sparse view x-ray CT reconstruction for checked baggage screening. in ME Gehm, JA Greenberg, A Ashok & MA Neifeld (eds), Anomaly Detection and Imaging with X-Rays (ADIX) III., 1063204, Proceedings of SPIE - The International Society for Optical Engineering, vol. 10632, SPIE, Anomaly Detection and Imaging with X-Rays (ADIX) III 2018, Orlando, United States, 4/17/18. https://doi.org/10.1117/12.2309509

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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.",

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Deep learning based sparse view x-ray CT reconstruction for checked baggage screening

Abstract

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Keywords

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