Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction.

Journal: IEEE transactions on bio-medical engineering
Published Date:

Abstract

OBJECTIVE: To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction.

Authors

  • Yue Guan
    Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Yudu Li
    Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
  • Ziwen Ke
    Research Center for Medical AI, CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Xi Peng
    Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Ruihao Liu
  • Yao Li
    Center of Robotics and Intelligent Machine, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, No. 266 Fangzhen Road, Beibei District, Chongqing, 400714, China.
  • Yiping P Du
    Institute for Medical Imaging Technology (IMIT), School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address: yipingdu@sjtu.edu.cn.
  • Zhi-Pei Liang
    Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.