Impact of a deep-learning image reconstruction algorithm on image quality and detection of solid lung lesions.

Journal: Research in diagnostic and interventional imaging
Published Date: May 27, 2025

Abstract

PURPOSE: To compare the impact of a deep-learning image reconstruction algorithm (Precise Image) with an iterative reconstruction algorithm on image quality and detection of solid lung lesions in chest CT images.

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.
  • Maxime Pastor
    IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France.
  • 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.
  • Renaud Sales
    Imagine UR UM 103, université de Montpellier, Department of Medical Imaging, centre hospitalier universitaire de Nîmes, Nîmes, France.
  • Chris Serrand
    Department of Biostatistics, Epidemiology, Public Health and Innovation in Methodology, Nîmes University Hospital, Univ. Montpellier, 30900 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.
  • 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.

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