Deep learning-guided weighted averaging for signal dropout compensation in DWI of the liver.

Journal: Magnetic resonance in medicine
Published Date: Aug 2, 2022

Abstract

PURPOSE: To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion.

Authors

  • Fasil Gadjimuradov
    Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Thomas Benkert
    Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Strasse 6, 97080 Würzburg, Germany (J.F.H., S.V., C.M., L.M.P., T.A.B., H.K., A.M.W.); and Department of Application Development, Siemens Healthcare, Erlangen, Germany (T.B., J.P.).
  • Marcel Dominik Nickel
    MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany.
  • Tobit Führes
    Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Marc Saake
    Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Andreas Maier
    Pattern Recognition Lab, University Erlangen-Nürnberg, Erlangen, Germany.

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