Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network.

Journal: European radiology
Published Date: Feb 25, 2021

Abstract

OBJECTIVES: To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks.

Authors

  • Johannes Haubold
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany. Johannes.haubold@uk-essen.de.
  • René Hosch
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany.
  • Lale Umutlu
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, Departments of.
  • Axel Wetter
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
  • Patrizia Haubold
    Department of Diagnostic and Interventional Radiology, Kliniken Essen-Mitte, Essen, Germany.
  • Alexander Radbruch
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Michael Forsting
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
  • Felix Nensa
    Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany.
  • Sven Koitka
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.

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