Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI.

Abstract

Authors

• Stephanie Tina Sauer
  Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
• Sara Aniki Christner
  Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
• Anna-Maria Lois
  Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
• Piotr Woźnicki
  Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
• Carolin Curtaz
  Department of Obstetrics and Gynecology, University Hospital Würzburg, Würzburg, Germany.
• Andreas Steven Kunz
  Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
• Elisabeth Weiland
  MR Application Predevelopment, Siemens Healthcare GmbH, 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.).
• Thorsten Alexander Bley
  Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
• Bettina Baeßler
  Department of Radiology, University Hospital of Cologne, Cologne, Germany.
• Jan-Peter Grunz
  Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany. Electronic address: Grunz_J@ukw.de.

Keywords

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