From Dose Reduction to Contrast Maximization: Can Deep Learning Amplify the Impact of Contrast Media on Brain Magnetic Resonance Image Quality? A Reader Study.

Journal: Investigative radiology
Published Date: Apr 20, 2022

Abstract

OBJECTIVES: The aim of this study was to evaluate a deep learning method designed to increase the contrast-to-noise ratio in contrast-enhanced gradient echo T1-weighted brain magnetic resonance imaging (MRI) acquisitions. The processed images are quantitatively evaluated in terms of lesion detection performance.

Authors

  • Alexandre Bône
    Guerbet Research, Villepinte.
  • Samy Ammari
    Service de Radiologie, Gustave-Roussy, Université Paris-Saclay, Villejuif, France.
  • Yves Menu
    From the Imaging Department, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif.
  • Corinne Balleyguier
    Department of Radiology, Gustave Roussy Cancer Campus, Villejuif, France; Université Paris-Saclay, Paris, France.
  • Eric Moulton
    Guerbet Research, Villepinte.
  • Emilie Chouzenoux
    Center for Visual Computing, CentraleSupelec, INRIA Saclay, Gif-sur-Yvette, 91190, France.
  • Andreas Volk
  • Gabriel C T E Garcia
    Imaging Department, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif.
  • François Nicolas
    Guerbet Research, Villepinte.
  • Philippe Robert
    Centre mémoire de ressources et de recherche, CHU Nice, 10 rue Molière, 06100 Nice, France; Pôle Réhabilitation autonomie vieillissement, CHU de Nice, 4 avenue de la Reine-Victoria, 06003 Nice, France; Centre d'innovation et d'usages en santé, université de Nice Sophia-Antipolis, CHU de Nice, 98 boulevard Édouard-Herriot, 06000 Nice, France.
  • Marc-Michel Rohe
  • Nathalie Lassau
    Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, Université Paris-Saclay. 94800 Villejuif, France.

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