Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study.

Journal: European radiology experimental
Published Date: Jan 9, 2023

Abstract

BACKGROUND: To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform.

Authors

  • Djamel Dabli
    Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.
  • Maeliss Loisy
    IMAGINE, UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, Nîmes, France.
  • Julien Frandon
    Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, Univ Montpellier, EA 2415, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France.
  • Fabien de Oliveira
    IMAGINE, UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, Nîmes, France.
  • Azhar Mohamad Meerun
    Saint-Eloi University Hospital, Montpellier, France.
  • Boris Guiu
    Department of RadiologySt-Eloi University HospitalMontpellierFrance.
  • Jean-Paul Beregi
    DRIM France IA, 75013 Paris, France; Collège des Enseignants en Radiologie de France (CERF), 75013 Paris, France; Medical Imaging Group Nîmes, Nîmes University Hospital, 34000 Nîmes, France.
  • Joël Greffier
    Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, Univ Montpellier, EA 2415, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France. joel.greffier@chu-nimes.fr.

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