Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.

Journal: European radiology
Published Date: Dec 10, 2019

Abstract

OBJECTIVES: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction.

Authors

  • Moritz Schwyzer
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland.
  • Katharina Martini
    Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
  • Dominik C Benz
    Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland; and.
  • Irene A Burger
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland.
  • Daniela A Ferraro
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland.
  • Ken Kudura
    Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland.
  • Valerie Treyer
    Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland.
  • Gustav K von Schulthess
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland.
  • Philipp A Kaufmann
    Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland; and.
  • Martin W Huellner
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland.
  • Michael Messerli
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland. Electronic address: michael.messerli@usz.ch.

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