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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The task of fast magnetic resonance (MR) image reconstruction is to reconstruct high-quality MR images from undersampled images. Most of the existing methods are based on U-Net, and these methods mainly adopt several simple connections within the network, which we call microscopic design ideas. However, these considerations cannot make full use of the feature information inside the network, which leads to low reconstruction quality. To solve this problem, we rethought the feature utilization method of the encoder and decoder network from a macroscopic point of view and propose a densely macroscopic feature fusion network for fast magnetic resonance image reconstruction. Our network uses three stages to reconstruct high-quality MR images from undersampled images from coarse to fine. We propose an inter-stage feature compensation structure (IFCS) which makes full use of the feature information of different stages and fuses the features of different encoders and decoders. This structure uses a connection method between sub-networks similar to dense form to fuse encoding and decoding features, which is called densely macroscopic feature fusion. A cross network attention block (CNAB) is also proposed to further improve the reconstruction performance. Experiments show that the quality of undersampled MR images is greatly improved, and the detailed information of MR images is enriched to a large extent. Our reconstruction network is lighter than many previous methods, but it achieves better performance. The performance of our method is about 10% higher than that of the original method, and about 3% higher than that of most existing methods. Compared with the nearest optimal algorithms, the performance of our method is improved by about 0.01–0.45%, and our computational complexity is only 1/14 of these algorithms.

Details

Title
DMFF-Net: Densely Macroscopic Feature Fusion Network for Fast Magnetic Resonance Image Reconstruction
Author
Sun, Zhicheng; Pang, Yanwei  VIAFID ORCID Logo  ; Sun, Yong; Liu, Xiaohan  VIAFID ORCID Logo 
First page
3862
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20799292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2748519168
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.