Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages.

Journal: Radiologic technology
PMID: 39472011

Abstract

PURPOSE: To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative reconstruction.

Authors

  • Akira Katayama
    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.
  • Hiroshi Hirakawa
    Akira Katayama, MD; Koichiro Yasaka, MD, PhD; Hiroshi Hirakawa, MD; Yuta Ohtake, MD; and Osamu Abe, MD, PhD, work for the University of Tokyo.
  • Yuta Ohtake
    Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, 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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