ScatterNet: A convolutional neural network for cone-beam CT intensity correction.

Journal: Medical physics
Published Date: Oct 8, 2018

Abstract

PURPOSE: To demonstrate a proof-of-concept for fast cone-beam CT (CBCT) intensity correction in projection space by the use of deep learning.

Authors

  • David C Hansen
    Department of Medical Physics, Aarhus University Hospital, Aarhus, 8200, Denmark.
  • Guillaume Landry
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Florian Kamp
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Minglun Li
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Claus Belka
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Katia Parodi
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Christopher Kurz
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.

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