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Abstract: Some Investigations on Robustness of Deep Learning in Limited Angle Tomography

  • Yixing HuangEmail author
  • Tobias Würfl
  • Katharina Breininger
  • Ling Liu
  • Günter Lauritsch
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

In computed tomography, image reconstruction from an insufficient angular range of projection data is called limited angle tomography. Due to missing data, reconstructed images suffer from artifacts, which cause boundary distortion, edge blurring, and intensity biases. Recently, deep learning methods have been applied very successfully to this problem in simulation studies.

Literatur

  1. 1.
    Huang Y, Würfl T, Breininger K, et al. Some investigations on robustness of deep learning in limited angle tomography. Proc MICCAI. 2018; p. 145–153.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Yixing Huang
    • 1
    Email author
  • Tobias Würfl
    • 1
  • Katharina Breininger
    • 1
  • Ling Liu
    • 1
  • Günter Lauritsch
    • 2
  • Andreas Maier
    • 1
    • 3
  1. 1.Friedrich-Alexander Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.Siemens Healthcare GmbHForchheimDeutschland
  3. 3.Erlangen Graduate School in Advanced Optical Technologies (SAOT)ErlangenDeutschland

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