Deep learning-based method for reducing residual motion effects in diffusion parameter estimation.

Journal: Magnetic resonance in medicine
Published Date: Oct 15, 2020

Abstract

PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects.

Authors

  • Ting Gong
    Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Qiqi Tong
    Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Zhiwei Li
    Department of Instrument Science & Technology, Zhejiang University, Hangzhou, 310027, China.
  • Hongjian He
    Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Hui Zhang
    Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Jianhui Zhong
    Department of Imaging Sciences, University of Rochester, Rochester, New York.

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