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 proceedingConference contribution


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 languageEnglish (US)
Title of host publicationAnomaly Detection and Imaging with X-Rays (ADIX) III
EditorsMichael E. Gehm, Joel A. Greenberg, Amit Ashok, Mark A. Neifeld
ISBN (Electronic)9781510617759
StatePublished - 2018
EventAnomaly Detection and Imaging with X-Rays (ADIX) III 2018 - Orlando, United States
Duration: Apr 17 2018Apr 18 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherAnomaly Detection and Imaging with X-Rays (ADIX) III 2018
Country/TerritoryUnited States


  • 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


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