Pushing the limits of low-cost ultra-low-field MRI by dual-acquisition deep learning 3D superresolution.

Journal: Magnetic resonance in medicine
Published Date: Apr 3, 2023

Abstract

PURPOSE: Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large-scale publicly available 3T brain data.

Authors

  • Vick Lau
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Linfang Xiao
    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.
  • Shi Su
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Ye Ding
  • Christopher Man
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China.
  • Xunda Wang
    Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China.
  • Anderson Tsang
    Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, SAR, Hong Kong, China.
  • Peng Cao
    Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.
  • Gary K K Lau
    Department of Medicine, LKS Faculty of Medicine, The University of Hong Kong, SAR, Hong Kong, China.
  • Gilberto K K Leung
    Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, SAR, Hong Kong, 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.

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