Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest.

Journal: La Radiologia medica
Published Date: Jan 7, 2023

Abstract

OBJECTIVES: A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters.

Authors

  • Simon Maennlin
    Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe- Seyler- Str. 3, 72076, Tübingen, Germany. Simon.Maennlin@med.uni-tuebingen.de.
  • Daniel Wessling
    From the Departments of Diagnostic and Interventional Radiology.
  • Judith Herrmann
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany.
  • Haidara Almansour
    From the Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen.
  • Dominik Nickel
    MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
  • Stephan Kannengiesser
    MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany.
  • Saif Afat
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany.
  • Sebastian Gassenmaier
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.

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