Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms.

Journal: European radiology
Published Date: Mar 10, 2021

Abstract

OBJECTIVES: To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).

Authors

  • Luuk J Oostveen
    Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands. Luuk.Oostveen@radboudumc.nl.
  • Frederick J A Meijer
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine Radboud University Medical Center Geert Grooteplein 10, 6525 GA, Netherlands.
  • Frank de Lange
    Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Ewoud J Smit
    Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Sjoert A Pegge
    Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Stefan C A Steens
    Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Martin J van Amerongen
    Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Mathias Prokop
    Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Ioannis Sechopoulos
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.

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