The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
This is the official implementation code for DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction published in IEEE Transactions on Medical Imaging (2018).
Guang Yang*, Simiao Yu*, et al.
(* equal contributions)
If you use this code for your research, please cite our paper.
@article{yang2018_dagan,
author = {Yang, Guang and Yu, Simiao and Dong, Hao and Slabaugh, Gregory G. and Dragotti, Pier Luigi and Ye, Xujiong and Liu, Fangde and Arridge, Simon R. and Keegan, Jennifer and Guo, Yike and Firmin, David N.},
journal = {IEEE Trans. Med. Imaging},
number = 6,
pages = {1310--1321},
title = {{DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction}},
volume = 37,
year = 2018
}
If you have any questions about this code, please feel free to contact Simiao Yu ([email protected]).
The original code is in python 3.5 under the following dependencies:
Code tested in Ubuntu 16.04 with Nvidia GPU + CUDA CuDNN (whose version is compatible to tensorflow v1.1.0).
Prepare data
Download pretrained VGG16 model
Train model
Test trained model
Please refer to the paper for the detailed results.