Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
PMID: 38343091

Abstract

BACKGROUND: The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition.

Authors

  • Hokun Kim
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Moon Hyung Choi
    Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Young Joon Lee
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021 Seoul, Republic of Korea. Electronic address: yjleerad@catholic.ac.kr.
  • Dongyeob Han
    Siemens Healthineers Ltd., Seoul, Republic of Korea.
  • Mahmoud Mostapha
    Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America. Electronic address: mahmoudm@cs.unc.edu.
  • Dominik Nickel
    MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.

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