A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM).

Journal: Magnetic resonance imaging
Published Date: Feb 3, 2022

Abstract

PURPOSE: This study developed a data-driven optimization to improve the accuracy of deep learning QSM quantification.

Authors

  • Zuojun Wang
    Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
  • Peng Xia
    State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
  • Fan Huang
  • Hongjiang Wei
    Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address: hongjiang.wei@sjtu.edu.cn.
  • Edward Sai-Kam Hui
    Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
  • Henry Ka-Fung Mak
    Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
  • Peng Cao
    Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.

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