Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and evaluate the clinical feasibility of this approach.

Authors

  • Caohui Duan
    State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.
  • Yongqin Xiong
    Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China.
  • Kun Cheng
    Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China.
  • Sa Xiao
    Department of Ophthalmology, University of Washington, Seattle, Washington, USA.
  • Jinhao Lyu
    Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China.
  • Cheng Wang
    Department of Pathology, Dalhousie University, Halifax, NS, Canada.
  • Xiangbing Bian
    Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Dekang Zhang
    Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China.
  • Ling Chen
    Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States.
  • Xin Zhou
    School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.
  • Xin Lou
    College of Pharmacy, Henan University of Chinese Medicine, Zhengzhou 450046, China.