Application of generative adversarial network in magnetic resonance image reconstruction.
10.7507/1001-5515.202204007
- Author:
Xin CAI
1
;
Xuewen HOU
1
;
Guang YANG
2
;
Shengdong NIE
1
Author Information
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
2. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200062, P. R. China.
- Publication Type:Journal Article
- Keywords:
Deep learning;
Generative adversarial network;
Image reconstruction;
Magnetic resonance imaging
- MeSH:
Humans;
Acceleration;
Algorithms;
Magnetic Resonance Imaging;
Technology
- From:
Journal of Biomedical Engineering
2023;40(3):582-588
- CountryChina
- Language:Chinese
-
Abstract:
Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients' cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.
