Deep-ADMM-Net

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This is a testing and training code for Deep ADMM-Net in "Deep ADMM-Net for Compressive Sensing MRI" (NIPS 2016)

If you use this code, please cite our paper:

[1] Yan Yang, Jian Sun, Huibin Li, Zongben Xu. Deep ADMM-Net for Compressive Sensing MRI, NIPS(2016).

http://gr.xjtu.edu.cn/web/jiansun/publications

All rights are reserved by the authors.

Yan Yang -2017/04/05. For more detail, feel free to contact: yangyan92@stu.xjtu.edu.cn

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Usage:

1. For testing the trained network

   1). Load trained network with different stages in main_ADMM_Net_test.m.
   If you apply ADMM-Net to reconstruct other MR images, it is best to re-train the models.

    The models in './net/network_20' are trained from 100 real MR trainging images with 20% sampling rate.
    The models in './net/network_30' are trained from 100 real MR trainging images with 30% sampling rate.
   

   2). Load sampling pattern with different sampling ratios in main_ADMM_Net_test.m

    The mask in './mask/mask_20' is a pseudo radial sampling pattern with 20% sampling rate.
   

   3). Load test image in main_ADMM_Net_test.m

    The images in './data/Brain_data' are real-valued brain MR images.
    The images in './data/Chest_data' are 50 real-valued chest MR testing images in our paper.
   

   4). Network setting is in 'config.m '.

   5). To test our ADMM-Net, run 'main_ADMM_Net_test.m'

2. For training the networks

   1). The training chest dataset is in './data/ChestTrain_20'.
   Run 'Gen_traindata.m' to generate training data, and load corresponding sampling pattern in this operation.

   2). Modify the network setting and trainging setting in 'config.m '.

   3). To train ADMM-Net by L-BFGS algorithm, run 'main_netTrain.m' .

   4). After training, the trained network and the training error are saved in './Train_output'.

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The testing result of the demo images.

1) Brain_data1.(20% sampling rate)

|--------------|  re_LOss  |  re_PSnr  | 
|--------------|-----------|-----------| 
|  net-stage7- |  0.0578   |  35.60    |  
|  net-stage14 |  0.0562   |  35.83    |  
|  net-stage15 |  0.0561   |  35.85    |  


2) Brain_data2.(20% sampling rate)

|--------------|  re_LOss  |  re_PSnr  | 
|--------------|-----------|-----------|  
|  net-stage7- |  0.0957   |  30.40    |  
|  net-stage14 |  0.0929   |  30.65    |  
|  net-stage15 |  0.0927   |  30.67    |