MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.

Journal: European radiology
Published Date: Nov 15, 2022

Abstract

OBJECTIVES: The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI.

Authors

  • Woojin Jung
    Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.
  • Hyun-Soo Lee
    Siemens Healthineers Ltd, Seoul, Republic of Korea.
  • Minkook Seo
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
  • Yoonho Nam
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, Catholic University of Korea, Seoul, Republic of Korea.
  • Yangsean Choi
    Department of Radiology, Seoul Saint Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Na-Young Shin
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 065-591, Republic of Korea.
  • Kook-Jin Ahn
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
  • Bum-Soo Kim
    Department of Radiology, Seoul Saint Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Jinhee Jang
    GenesisEgo, Seoul, Republic of Korea.

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