Paper Info
Title
High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network.
Abstract
Purpose: An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T2 mapping from single-shot OverLapping-Echo Detachment (OLED) planar imaging. Methods: The training dataset was obtained from simulations carried out on SPROM software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponded T2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T2 mapping from simulation and in vivo human brain data. Results: Though the ResNet was trained entirely on simulated data, the trained network was generalized well to real human brain data. The results from simulation and in vivo human brain experiments show that the proposed method significantly outperformed the echo-detachment-based method. Reliable T2 mapping was achieved within tens of milliseconds after the network had been trained while the echo-detachment-based OLED reconstruction method took minutes. Conclusion: The proposed method will greatly facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and ResNet has the potential to reconstruct images from complex MRI sequence efficiently.
Year
Venue
Field
2017
arXiv: Computer Vision and Pattern Recognition
Mr imaging,Residual,Computer vision,Pattern recognition,Computer science,Planar Imaging,Convolutional neural network,Software,Artificial intelligence,Residual neural network
DocType
Volume
Citations 
Journal
abs/1708.05170
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Congbo Cai100.34
Yiqing Zeng200.68
Chao Wang3895190.04
Shuhui Cai4146.10
Jun Zhang51102188.11
Zhong Chen653.24
Xinghao Ding759152.95
Jianhui Zhong86311.31