Is AI the way forward for reducing metal artifacts in CT? Development of a generic deep learning-based method and initial evaluation in patients with sacroiliac joint implants.

Journal: European journal of radiology
Published Date: Apr 20, 2023

Abstract

PURPOSE: To develop a deep learning-based metal artifact reduction technique (dl-MAR) and quantitatively compare metal artifacts on dl-MAR-corrected CT-images, orthopedic metal artifact reduction (O-MAR)-corrected CT-images and uncorrected CT-images after sacroiliac (SI) joint fusion.

Authors

  • Mark Selles
    Department of Radiology, Isala, 8025 AB Zwolle, the Netherlands; Department of Radiology & Nuclear medicine, Amsterdam University Medical Centre, 1105 AZ Amsterdam, the Netherlands; Amsterdam Movement Sciences, 1081 BT Amsterdam, the Netherlands. Electronic address: m.selles@isala.nl.
  • Derk J Slotman
    Department of Radiology, Isala Hospital, Zwolle, The Netherlands. d.j.slotman@isala.nl.
  • Jochen A C van Osch
    Department of Medical Physics, Isala, P.O. Box 10400, 8000 GK Zwolle, The Netherlands.
  • Ingrid M Nijholt
    Department of Radiology and Nuclear Medicine, Isala, P.O. Box 10400, 8000 GK Zwolle, The Netherlands.
  • Ruud H H Wellenberg
    Department of Radiology & Nuclear medicine, Amsterdam University Medical Centre, 1105 AZ Amsterdam, the Netherlands; Amsterdam Movement Sciences, 1081 BT Amsterdam, the Netherlands.
  • Mario Maas
    Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, The Netherlands.
  • Martijn F Boomsma
    Department of Radiology and Nuclear Medicine, Isala, P.O. Box 10400, 8000 GK Zwolle, The Netherlands.

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