Deep learning-enhanced T mapping with spatial-temporal and physical constraint.

Journal: Magnetic resonance in medicine
Published Date:

Abstract

PURPOSE: To propose a reconstruction framework to generate accurate T maps for a fast MR T mapping sequence.

Authors

  • Yuze Li
    Disinfection and Supply Center, Liyang People's Hospital, Liyang 213300, Jiangsu, China.
  • Yajie Wang
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Haikun Qi
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, London, United Kingdom.
  • Zhangxuan Hu
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Zhensen Chen
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Runyu Yang
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Huiyu Qiao
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Jie Sun
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xihai Zhao
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Hua Guo
    Zhumadian Psychiatric Hospital, Zhumadian 463000, Henan, China.
  • Huijun Chen
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China. Electronic address: chenhj_cbir@mail.tsinghua.edu.cn.