Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold.

Journal: Investigative radiology
Published Date: May 1, 2021

Abstract

OBJECTIVE: The aim of this study was to evaluate the feasibility of a single breath-hold fast half-Fourier single-shot turbo spin echo (HASTE) sequence using a deep learning reconstruction (HASTEDL) for T2-weighted magnetic resonance imaging of the abdomen as compared with 2 standard T2-weighted imaging sequences (HASTE and BLADE).

Authors

  • Judith Herrmann
    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.
  • Dominik Nickel
    MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
  • Simon Arberet
    Digital Technology & Innovation, Siemens Medical Solutions USA, Inc., Princeton, NJ, USA.
  • Saif Afat
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany.
  • Andreas Lingg
    From the Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Tuebingen.
  • Matthias Kündel
    From the Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Tuebingen.
  • Ahmed E Othman
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany; Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany. Electronic address: ahmed.e.othman@googlemail.com.

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