Paper Info
Title
An Edge Enhanced Srgan For Mri Super Resolution In Slice-Selection Direction
Abstract
The low resolution MRI in slice-select direction will lead to information loss and artifacts in 2D multi-slices MRI, which is not conducive to the diagnosis and treatment of diseases. Therefore, we proposed an edge enhanced super-resolution generative adversarial networks (EE-SRGAN) for MRI super resolution in slice-select direction. Firstly, a two-stage super-resolution generator network (TSSR) for solving the problem that the down-sampling ratio of MRI resolution in single direction reached 12 times. In addition, in order to overcome the problem of image smoothness caused by high peak signal-to-noise ratio (PSNR) and improve the visual reality of reconstruction image, we construct a generative adversarial networks based on TSSR. Finally, in order to achieve more texture details, we proposed an edge enhanced loss function to optimize the generator network. From the experimental results, we find that our TSSR is better (increased 1.78 dB PSNR), EE-SRGAN provides more satisfactory visual effect and beneficial to segmentation task (increased 2.14% Dice index) than state-of-art super-resolution network.
Year
DOI
Venue
2019
10.1007/978-3-030-33226-6_2
MULTIMODAL BRAIN IMAGE ANALYSIS AND MATHEMATICAL FOUNDATIONS OF COMPUTATIONAL ANATOMY
Keywords
DocType
Volume
MRI Slice-Selection, Two-Stage Super-Resolution, Edge enhanced
Conference
11846
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Jia Liu118332.42
Fang Chen200.68
Xianyu Wang301.69
Hongen Liao439070.91