Deep learning reconstructed T2-weighted Dixon imaging of the spine: Impact on acquisition time and image quality.

Journal: European journal of radiology
PMID:

Abstract

PURPOSE: To assess the image quality and impact on acquisition time of a novel deep learning based T2 Dixon sequence (T2) of the spine.

Authors

  • Zeynep Berkarda
    Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Germany.
  • Simon Wiedemann
  • Caroline Wilpert
    Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Germany.
  • Ralph Strecker
    EMEA Scientific Partnerships, Siemens Healthcare GmbH, Erlangen, Germany.
  • Gregor Koerzdoerfer
    MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany.
  • Dominik Nickel
    MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
  • Fabian Bamberg
    Department of Diagnostic and Interventional Radiology, University Medical Center Tübingen, Tübingen, Germany.
  • Matthias Benndorf
    Department of Radiology, Faculty of Medicine, University of Freiburg Medical Centre, Freiburg, Germany.
  • Thomas Mayrhofer
    Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston.
  • Maximilian Frederik Russe
    Department of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany.
  • Jakob Weiss
    Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
  • Thierno D Diallo
    Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.