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NTIS 바로가기Investigative magnetic resonance imaging, v.23 no.2, 2019년, pp.81 - 99
Lee, Doohee (Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University) , Lee, Jingu (Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University) , Ko, Jingyu (Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University) , Yoon, Jaeyeon (Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University) , Ryu, Kanghyun (Department of Electrical and Electronic Engineering, Yonsei University) , Nam, Yoonho (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea)
Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods...
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