Comparative analysis of image quality and interchangeability between standard and deep learning-reconstructed T2-weighted spine MRI.

Journal: Magnetic resonance imaging
Published Date: Mar 19, 2024

Abstract

RATIONALE AND OBJECTIVES: MRI reconstruction of undersampled data using a deep learning (DL) network has been recently performed as part of accelerated imaging. Herein, we compared DL-reconstructed T2-weighted image (T2-WI) to conventional T2-WI regarding image quality and degenerative lesion detection.

Authors

  • Seungeun Lee
    Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Joon-Yong Jung
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. messengr@catholic.ac.kr.
  • Heeyoung Chung
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
  • Hyun-Soo Lee
    Siemens Healthineers Ltd, Seoul, Republic of Korea.
  • Dominik Nickel
    MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
  • Jooyeon Lee
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX 77030, USA. Electronic address: Jooyeon.Lee@uth.tmc.edu.
  • So-Yeon Lee
    From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.).

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