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

Sagar Mandava; Amit Ashok; Ali Bilgin

doi:10.1117/12.2309509

27 April 2018 Deep learning based sparse view x-ray CT reconstruction for checked baggage screening

Sagar Mandava, Amit Ashok, Ali Bilgin

Proceedings Volume 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III; 1063204 (2018) https://doi.org/10.1117/12.2309509
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

Citation Download Citation

Sagar Mandava, Amit Ashok, and Ali Bilgin "Deep learning based sparse view x-ray CT reconstruction for checked baggage screening", Proc. SPIE 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III, 1063204 (27 April 2018); https://doi.org/10.1117/12.2309509

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