Generalizable, sequence-invariant deep learning image reconstruction for subspace-constrained quantitative MRI.

Journal: Magnetic resonance in medicine
Published Date:

Abstract

PURPOSE: To develop a deep subspace learning network that can function across different pulse sequences.

Authors

  • Zheyuan Hu
    National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore. Electronic address: e0792494@u.nus.edu.
  • Zihao Chen
  • Tianle Cao
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Hsu-Lei Lee
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Yibin Xie
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Debiao Li
  • Anthony G Christodoulou
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.