SuperDTI: Ultrafast DTI and fiber tractography with deep learning.

Journal: Magnetic resonance in medicine
PMID:

Abstract

PURPOSE: To develop a deep learning-based reconstruction framework for ultrafast and robust diffusion tensor imaging and fiber tractography.

Authors

  • Hongyu Li
    Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States.
  • Zifei Liang
    Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, USA.
  • ChaoYi Zhang
    School of Technology, Beijing Forestry University, Beijing, China.
  • Ruiying Liu
    Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Weihong Zhang
    Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Dong Liang
    Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.
  • Bowen Shen
    Computer Science, Virginia Tech, Blacksburg, Virginia, USA.
  • Xiaoliang Zhang
    Department of Information, The First Affiliated Hospital of Nanjing Medical University & Jiangsu Province Hospital, Nanjing, Jiangsu, China; Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Yulin Ge
    Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, USA.
  • Jiangyang Zhang
    Radiology, New York University School of Medicine, New York City, NY, United States.
  • Leslie Ying