Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction.

Journal: Neuroradiology
Published Date: Mar 22, 2024

Abstract

PURPOSE: We investigated whether the quality of high-resolution computed tomography (CT) images of the temporal bone improves with deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR).

Authors

  • Nana Fujita
    Department of Radiology, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, Japan.
  • Koichiro Yasaka
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Sosuke Hatano
    Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.
  • Naoya Sakamoto
    Department of Gastroenterology and Hepatology, Hokkaido University Graduate School of Medicine, Sapporo 0608638, Japan.
  • Ryo Kurokawa
    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.

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