Calibrationless reconstruction of uniformly-undersampled multi-channel MR data with deep learning estimated ESPIRiT maps.

Journal: Magnetic resonance in medicine
Published Date: Feb 27, 2023

Abstract

PURPOSE: To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (ESPIRiT) maps from uniformly-undersampled multi-channel MR data by deep learning.

Authors

  • Junhao Zhang
  • Zheyuan Yi
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Yujiao Zhao
    Department of Rheumatology, Yale University, New Haven, CT, USA.
  • Linfang Xiao
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Jiahao Hu
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Christopher Man
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Vick Lau
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Shi Su
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Fei Chen
    Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
  • Alex T L Leong
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Ed X Wu
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China.

Keywords

External Resources

Popular Topics
Recent Journals