Ultra-high resolution computed tomography with deep-learning-reconstruction: diagnostic ability in the assessment of gastric cancer and the depth of invasion.

Journal: Abdominal radiology (New York)
Published Date: Jun 28, 2024

Abstract

PURPOSE: To evaluate the image quality of ultra-high-resolution CT (U-HRCT) images reconstructed using an improved deep-learning-reconstruction (DLR) method. Additionally, we assessed the utility of U-HRCT in visualizing gastric wall structure, detecting gastric cancer, and determining the depth of invasion.

Authors

  • Masaya Tanabe
    Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
  • Masahiro Tanabe
    Department of Radiology, Yamaguchi University Graduate School of Medicine, Japan. Electronic address: m-tanabe@yamaguchi-u.ac.jp.
  • Hideko Onoda
    Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan.
  • Masatoshi Nakashima
    Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
  • Mayumi Higashi
    Department of Radiology, Yamaguchi University Graduate School of Medicine, Japan.
  • Yosuke Kawano
    Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan.
  • Keiko Hideura
    Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
  • Takaaki Ueda
    Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
  • Taiga Kobayashi
    Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
  • Katsuyoshi Ito
    Department of Radiology, Yamaguchi University Graduate School of Medicine, Japan.

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