Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation.

Journal: Japanese journal of radiology
Published Date: Apr 7, 2025

Abstract

PURPOSE: To evaluate the impact of an AI-based, noise reduction technique for compensation of image degradation on pediatric and neonatal chest and abdomen radiography using a visual grading analysis.

Authors

  • So Ode
    Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan. so.ode@marianna-u.ac.jp.
  • Atsuko Fujikawa
    Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
  • Atsushi Hiroishi
    Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan.
  • Yuki Saito
    Department of Rehabilitation Medicine, Hirosaki University, Graduate School of Medicine, Japan.
  • Takao Tanuma
    Imaging Center, St. Marianna University School of Medicine Hospital.
  • Daigo Suzuki
    Imaging Center, St. Marianna University School of Medicine Hospital.
  • Yuichi Sasaki
    Imaging Center, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan.
  • Hidefumi Mimura
    Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan.

Keywords

External Resources

Popular Topics
Recent Journals