Potential of a machine-learning model for dose optimization in CT quality assurance.

Journal: European radiology
Published Date: Jul 1, 2019

Abstract

OBJECTIVES: To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study.

Authors

  • Axel Meineke
    Cerner HS Deutschland GmbH, 13629, Berlin, Germany.
  • Christian Rubbert
    Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany. christian.rubbert@med.uni-duesseldorf.de.
  • Lino M Sawicki
    Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany.
  • Christoph Thomas
    Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany.
  • Yan Klosterkemper
    Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany.
  • Elisabeth Appel
    Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany.
  • Julian Caspers
    Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany.
  • Oliver T Bethge
    Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany.
  • Patric Kröpil
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
  • Gerald Antoch
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
  • Johannes Boos
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.

Keywords

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