qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks.

Journal: Magnetic resonance in medicine
Published Date: Jul 8, 2020

Abstract

PURPOSE: To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging.

Authors

  • Huan Minh Luu
    Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  • Dong-Hyun Kim
    Neurobiota Research Center, College of Pharmacy, Kyung Hee University, Dongdaemun-gu, Seoul 02447, Republic of Korea.
  • Jae-Woong Kim
    Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  • Seung-Hong Choi
    Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
  • Sung-Hong Park
    From the Graduate School of Medical Science and Engineering (K.H.K., S.H.P.) and Department of Bio and Brain Engineering (S.H.P.), Korea Advanced Institute of Science and Technology, Room 1002, CMS (E16) Building, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea (S.H.C.); Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.C.); and Center for Nanoparticle Research, Institute for Basic Science, Seoul, Republic of Korea (S.H.C.).

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