Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.

Journal: Abdominal radiology (New York)
PMID: 38755452

Abstract

PURPOSE: To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phase (HBP) of Gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI).

Authors

  • Yukihisa Takayama
    Department of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan. ytakayama@fukuoka-u.ac.jp.
  • Keisuke Sato
    Department of Electrical and Control Systems Engineering, Toyama College, National Institute of Technology, Toyama 939-8045, Japan.
  • Shinji Tanaka
    Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan.
  • Ryo Murayama
    Department of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.
  • Ryotaro Jingu
    Radiology Center, Fukuoka University Hospital, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.
  • Kengo Yoshimitsu
    Department of Radiology, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-Ku, Fukuoka City, Fukuoka, 814-0180, Japan.

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