A Comparative Study of Unsupervised Deep Learning Methods for MRI Reconstruction
10.13104/imri.2020.24.4.179
- Author:
Zhuonan HE
1
;
Cong QUAN
;
Siyuan WANG
;
Yuanzheng ZHU
;
Minghui ZHANG
;
Yanjie ZHU
;
Qiegen LIU
Author Information
1. Department of Electronic Information Engineering, Nanchang University, Nanchang, China
- Publication Type:Review Article
- From:Investigative Magnetic Resonance Imaging
2020;24(4):179-195
- CountryRepublic of Korea
- Language:English
-
Abstract:
Recently, unsupervised deep learning methods have shown great potential in image processing. Compared with a large-amount demand for paired training data of supervised methods with a specific task, unsupervised methods can learn a universal and explicit prior information on data distribution and integrate it into the reconstruction process. Therefore, it can be used in various image reconstruction environments without showing degraded performance. The importance of unsupervised learning in MRI reconstruction appears to be growing. Nevertheless, the establishment of prior formulation in unsupervised deep learning varies a lot depending on mathematical approximation and network architectures. In this work, we summarized basic concepts of unsupervised deep learning comprehensively and compared performances of several state-of-the-art unsupervised learning methods for MRI reconstruction.
