Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses.

Journal: Japanese journal of radiology
Published Date: Mar 2, 2023

Abstract

PURPOSE: The aim of this study was to assess the impact of the deep learning reconstruction (DLR) with single-energy metal artifact reduction (SEMAR) (DLR-S) technique in pelvic helical computed tomography (CT) images for patients with metal hip prostheses and compare it with DLR and hybrid iterative reconstruction (IR) with SEMAR (IR-S).

Authors

  • Reina Hosoi
    Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Koichiro Yasaka
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Masumi Mizuki
    Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Haruomi Yamaguchi
    Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Rintaro Miyo
    Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Akiyoshi Hamada
    From the Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Osamu Abe
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.

Keywords

External Resources

Popular Topics
Recent Journals