Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction.

Journal: European journal of radiology
Published Date: Jun 3, 2021

Abstract

OBJECTIVES: To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction.

Authors

  • D Racine
    Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Grand-Pré 1, 1007 Lausanne, Switzerland. Electronic address: damien.racine@chuv.ch.
  • H G Brat
    Institut de Radiologie de Sion, Groupe 3R, Rue du scex, 2, 1950 Sion, Switzerland.
  • B Dufour
    Institut de Radiologie de Sion, Groupe 3R, Rue du scex, 2, 1950 Sion, Switzerland.
  • J M Steity
    Centre d'imagerie de la Riviera, Groupe 3R, Rue des Moulins 5B, 1800 Vevey, Switzerland.
  • M Hussenot
    GE Medical Systems (Schweiz) AG, Europa-Strasse 31, 8152 Glattbrugg, Switzerland.
  • B Rizk
    Centre d'Imagerie de Fribourg, Groupe 3R, Rue du Centre 10, 1752 Villars-sur-Glâne, Switzerland. Electronic address: benrizk@gmx.net.
  • D Fournier
    Institut de Radiologie de Sion, Groupe 3R, Rue du scex, 2, 1950 Sion, Switzerland.
  • F Zanca
    Palindromo Consulting, W. de Croylaan, 51 3000 Leuven Belgium. Electronic address: Federica.Zanca@Palindromo.Consulting.

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