Fast, high-quality, and unshielded 0.2 T low-field mobile MRI using minimal hardware resources.

Journal: Magma (New York, N.Y.)
PMID: 38967865

Abstract

OBJECTIVE: To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources.

Authors

  • Lei Li
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
  • Qingyuan He
    Department of Radiology, Peking University Third Hospital, Beijing, China.
  • Shufeng Wei
    Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China.
  • Huixian Wang
    Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China.
  • Zheng Wang
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan 430060, China.
  • Zhao Wei
    Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Beijing, 100037, China. zw@fuwai.com.
  • Hongyan He
    Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China.
  • Ce Xiang
    Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China.
  • Wenhui Yang
    Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China. yangwenh@mail.iee.ac.cn.

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