A review on deep learning MRI reconstruction without fully sampled k-space.

Journal: BMC medical imaging
Published Date: Dec 24, 2021

Abstract

BACKGROUND: Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to accelerate MRI reconstruction. Recently, deep learning provides a new direction for MRI, while most of them require a large number of data pairs for training. However, there are many scenarios where fully sampled k-space data cannot be obtained, which will seriously hinder the application of supervised learning. Therefore, deep learning without fully sampled data is indispensable.

Authors

  • Gushan Zeng
    School of Computer and Information Engineering, Fujian Engineering Research Center for Medical Data Mining and Application, Xiamen University of Technology, Xiamen, China.
  • Yi Guo
    Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Jiaying Zhan
    School of Computer and Information Engineering, Fujian Engineering Research Center for Medical Data Mining and Application, Xiamen University of Technology, Xiamen, China.
  • Zi Wang
    Clinical Medical College, Yangzhou University, 225009 Yangzhou, Jiangsu, China.
  • Zongying Lai
    School of Information Engineering, Jimei University, Xiamen, China.
  • Xiaofeng Du
    School of Computer and Information Engineering, Fujian Engineering Research Center for Medical Data Mining and Application, Xiamen University of Technology, Xiamen, China.
  • Xiaobo Qu
  • Di Guo

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