Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study.

Journal: European radiology
Published Date: Jul 21, 2022

Abstract

OBJECTIVES: To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for 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.
  • Quentin Durand
    Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, Bd. Prof Robert Debré, 30029, Nîmes Cedex 9, 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.
  • Salim Si-Mohamed
    Department of Radiology, Hospices Civils de Lyon, 69500 Lyon, France.
  • Maeliss Loisy
    IMAGINE, UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, Nîmes, France.
  • Fabien de Oliveira
    IMAGINE, UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, Nîmes, France.
  • 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.
  • Djamel Dabli
    Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.

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