Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T.

Abstract

Authors

• Judith Herrmann
  Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, 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.).
• Andreas Brendlin
  Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany.
• Sebastian Gassenmaier
  Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
• Thomas Hölldobler
  Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany.
• Simon Maennlin
  Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe- Seyler- Str. 3, 72076, Tübingen, Germany. Simon.Maennlin@med.uni-tuebingen.de.
• Haidara Almansour
  From the Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen.
• Andreas Lingg
  From the Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Tuebingen.
• Elisabeth Weiland
  MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
• Saif Afat
  Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany.

Keywords

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