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Research progress of deep learning in chemical exchange saturation transfer magnetic resonance imaging
ZHANG Lihong  XU Chongxin  HOU Beibei  TANG Chaosheng  SUN Junding 

Cite this article as: Zhang LH, Xu CX, Hou BB, et al. Research progress of deep learning in chemical exchange saturation transfer magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2022, 13(11): 165-168. DOI:10.12015/issn.1674-8034.2022.11.034.


[Abstract] Deep learning, a significant method of artificial intelligence, has been used for chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) in recent years, the basic principle is to indirectly realize the detection of specific low concentration of solute molecules through the reduction of water signal. Problems such as slow collection speed, slow quantification speed, and inaccurate quantitative evaluation affect the application and promotion of CEST MRI in clinical practice, and how to improve these problems has also become the focus of research. As a new research direction of artificial intelligence, deep learning has only been applied to CEST-MRI technology in recent years. This method is mainly used in the quantification and acceleration aspects of CEST MRI. The quantification usage includes prediction of the high field results and quantify proton exchange rate and concentration. The acceleration studies include acceleration on acquisition and acceleration on quantification. As for the method itself, the most frequently used algorithm is convolutional neural network and deep neural networks. Other studies included the comparison among different deep learning models and establishment of deep learning models based on different MRI sequences. This paper is to review the application of deep learning in CEST MRI in detail,which can be used as reference for interested parties in this field and further research and development on this basis. Then accelerate the clinical transformation of CEST MRI.
[Keywords] magnetic resonance imaging;chemical exchange saturation transfer;deep learning;quantitation;acceleration

ZHANG Lihong   XU Chongxin   HOU Beibei   TANG Chaosheng   SUN Junding*  

College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China

Sun JD, E-mail: sunjd@hpu.edu.cn

Conflicts of interest   None.

Received  2022-03-31
Accepted  2022-09-14
DOI: 10.12015/issn.1674-8034.2022.11.034
Cite this article as: Zhang LH, Xu CX, Hou BB, et al. Research progress of deep learning in chemical exchange saturation transfer magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2022, 13(11): 165-168.DOI:10.12015/issn.1674-8034.2022.11.034

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