Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.

Journal: Abdominal radiology (New York)
PMID: 39560744

Abstract

OBJECTIVES: To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstruction (cDLR) algorithms.

Authors

  • Akio Tamura
    Department of Radiology, Iwate Medical University School of Medicine, 19-1 Uchimaru, Morioka 020-8505, Japan. Electronic address: a.akahane@gmail.com.
  • Eisuke Mukaida
    Department of Radiology, Iwate Medical University School of Medicine, Iwate, Japan.
  • Yoshitaka Ota
    Division of Central Radiology, Iwate Medical University Hospital, Iwate, Japan.
  • Shun Abe
    Division of Central Radiology, Iwate Medical University Hospital, Iwate, Japan.
  • Makoto Orii
    Department of Radiology, Iwate Medical University, 2-1-1, Idaidori, Yahaba, 028-3695, Iwate, Japan. kori931@gmail.com.
  • Yoshiro Ieko
    Iwate Medical University School of Medicine, Shiwa-gun, Japan.
  • Kunihiro Yoshioka
    Department of Radiology, Iwate Medical University School of Medicine, Iwate, Japan.

Keywords

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