Cerebrospinal fluid flow artifact reduction with deep learning to optimize the evaluation of spinal canal stenosis on spine MRI.

Journal: Skeletal radiology
PMID: 37996559

Abstract

PURPOSE: The aim of study was to employ the Cycle Generative Adversarial Network (CycleGAN) deep learning model to diminish the cerebrospinal fluid (CSF) flow artifacts in cervical spine MRI. We also evaluate the agreement in quantifying spinal canal stenosis.

Authors

  • Ue-Hwan Kim
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  • Hyo Jin Kim
    Department of Internal Medicine, Pusan National University Hospital, Busan, Korea.
  • Jiwoon Seo
    Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Jee Won Chai
    Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Jiseon Oh
    Department of Radiology, Seoul National University Hospital and College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea.
  • Yoon-Hee Choi
    Department of Physical Medicine and Rehabilitation, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Republic of Korea. yoonhee.choi83@gmail.com.
  • Dong Hyun Kim
    Department of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seoul, Korea.

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