Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver.

Journal: Zeitschrift fur medizinische Physik
Published Date: Aug 4, 2023

Abstract

PURPOSE: This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm in order to enhance the image quality of diffusion-weighted liver images and, in particular, to reduce the pulsation-induced signal loss occurring predominantly in the left liver lobe.

Authors

  • 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.
  • Jennifer Lorenz
    Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Hannes Seuss
    Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Department of Radiology, Klinikum Forchheim - Fränkische Schweiz, Forchheim, Germany.
  • Sebastian Bickelhaupt
    Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Michael Uder
    Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Frederik Bernd Laun
    Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

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