Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study.

Journal: European radiology
Published Date: Feb 16, 2022

Abstract

OBJECTIVES: To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST).

Authors

  • Florian Michallek
    Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
  • Ulrich Genske
    Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
  • Stefan Markus Niehues
    Department of Radiology, Charité - Universitätsmedizin Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
  • Bernd Hamm
    Department of Diagnostic and Interventional Radiology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
  • Paul Jahnke
    Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany. paul.jahnke@charite.de.

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