Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data.

Journal: Diagnostic and interventional imaging
Published Date: Sep 5, 2021

Abstract

PURPOSE: The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications.

Authors

  • 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.
  • 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.
  • Salim Si-Mohamed
    Department of Radiology, Hospices Civils de Lyon, 69500 Lyon, France.
  • Djamel Dabli
    Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.
  • Aymeric Hamard
    Department of Medical Imaging, CHU Nimes, Medical Imaging Group Nimes, Univ Montpellier, EA 2415, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France.
  • Asmaa Belaouni
    Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.
  • Philippe Akessoul
    Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.
  • Francis Besse
    Department of Radiology Centre Cardiologique Nord, 93200 Saint Denis, 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.

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