Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.

Journal: Magma (New York, N.Y.)
PMID: 39964601

Abstract

OBJECTIVE: Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.

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.
  • Wenhui Yang
    Institute of Electrical Engineering Chinese Academy of Sciences, Beijing, China. yangwenh@mail.iee.ac.cn.

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