Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jun 20, 2020

Abstract

PURPOSE: We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in comparison with filtered back-projection (FBP) and a partial model-based iterative reconstruction (ASiR-V).

Authors

  • Damien Racine
    Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Grand-Pré 1, 1007 Lausanne, Switzerland.
  • Fabio Becce
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, Lausanne 1011, Switzerland.
  • Anais Viry
    Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Grand-Pré 1, 1007 Lausanne, Switzerland.
  • Pascal Monnin
    Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Grand-Pré 1, 1007 Lausanne, Switzerland.
  • Brian Thomsen
  • Francis R Verdun
    Institute of Radiation Physics, Lausanne University Hospital, Lausanne, Switzerland.
  • David C Rotzinger
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Bugnon 46, 1011 Lausanne, Switzerland. Electronic address: david.rotzinger@chuv.ch.

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