Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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Deep Learning in MR Image Processing

  • 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)
  • Received : 2018.09.12
  • Accepted : 2019.02.18
  • Published : 2019.06.30
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Abstract

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. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

Keywords

References

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