Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
PMID: 39435504

Abstract

BACKGROUND: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality.

Authors

  • Fuminari Tatsugami
    Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Toru Higaki
    Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.
  • Ikuo Kawashita
    Department of Clinical Radiology, Hiroshima International University, 555-36, kurosegakuendai, Higashihiroshima, Hiroshima, 739-2695, Japan.
  • Chikako Fujioka
    Department of Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Yuko Nakamura
    Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Shinya Takahashi
    Department of Cardiovascular Surgery, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, Hiroshima, 734-0037, Japan.
  • Kazuo Awai
    Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.

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