The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis.

Journal: European radiology
Published Date: May 1, 2022

Abstract

OBJECTIVE: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR).

Authors

  • J Abel van Stiphout
    Department of Clinical Technology, Faculty of Mechanical, Maritime, and Materials Engineering (3ME), Delft University of Technology, Mekelweg 2, NL-2628 CD, Delft, The Netherlands. j.a.vanstiphout@student.tudelft.nl.
  • Jan Driessen
    Department of Clinical Technology, Faculty of Mechanical, Maritime, and Materials Engineering (3ME), Delft University of Technology, Mekelweg 2, NL-2628 CD, Delft, The Netherlands.
  • Lennart R Koetzier
    Department of Clinical Technology, Faculty of Mechanical, Maritime, and Materials Engineering (3ME), Delft University of Technology, Mekelweg 2, NL-2628 CD, Delft, The Netherlands.
  • Lara B Ruules
    Department of Clinical Technology, Faculty of Mechanical, Maritime, and Materials Engineering (3ME), Delft University of Technology, Mekelweg 2, NL-2628 CD, Delft, The Netherlands.
  • Martin J Willemink
    Departments of Radiology (L.D.H., G.M., K.H., M.K., M.J.W., A.M.S., D.F.) and Surgery (M.F.), Stanford University School of Medicine, 300 Pasteur Dr, Room S-072, Stanford, CA 94305-5105.
  • Jan W T Heemskerk
    Department of Radiology C-2S, Leiden University Medical Center, Albinusdreef 2, NL-2333 ZA, Leiden, The Netherlands.
  • Aart J van der Molen
    Department of Radiology C-2S, Leiden University Medical Center, Albinusdreef 2, NL-2333 ZA, Leiden, The Netherlands.

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