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Stabilizing Deep Tomographic Reconstruction Networks

Wu, Weiwen; Hu, Dianlin; Cong, Wenxiang; Shan, Hongming; Wang, Shaoyu; Niu, Chuang; Yan, Pingkun; Yu, Hengyong; Vardhanabhuti, Varut; Wang, Ge

# Stabilizing Deep Tomographic Reconstruction Networks

 

# This repository contains the code, mentioned networks and test datasets from the paper "Stabilizing Deep Tomographic Reconstruction Networks" by W. Wu, et al.

 

# The code is divided into two modalities, i.e., CT and MRI, corresponding to two folders named by CT and MRI. ACID is a framework, the authors can use the framework based on themselves trained works.

 

#If you use the code, please cite our work

 

@article{Wu 2020,

    title={ Stabilizing Deep Tomographic Reconstruction Networks },

    author={ Weiwen Wu,Dianlin Hu, Wenxiang Cong,Hongming Shan,Shaoyu Wang,Chuang Niu,Pingkun Yan,Hengyong Yu,Varut Vardhanabhutiand Ge Wang },

    journal={arXiv preprint arXiv: 2008.01846},

    year={2020}

}

 

# CT folder: There are 11 sub-folder and Testmain.m. To run this code, you need to ensure your computer or work station run FBPConvNet, which can be  downloaded publically from https://github.com/panakino/FBPConvNet. The lib subfolder should be added into path.

 

#  Run Testmain.m to fast generate the reconstruction results with modifying the path. ACID subfolder contains ACID reconstruction demos for structure-changes, tiny-perturbation, more-input-data and ACID against whole Adversarial attack. Ablation subfolder is used to generate the ablation results. Demo_adversarial_pert_ACID and Demo_adversarial_pert_NN are used to adversarial attacks from the whole ACID and a single NN, where Demo_adversarial_pert_NN is sorted out based on Antun, Vegard, et al. "On instabilities of deep learning in image reconstruction and the potential costs of AI."?PNAS, 117.48 (2020): 30088-30095. Run ACIDFindPerMain.m to find the adversarial attack for whole ACID and run Demo_adversarial_pert_NN_ELL for generating the adversarial attack for Ell-50.

 

# CS-based and dictionary learning-based reconstruction methods are also included

 

# Testdata and Out_data subfolder are used to store inputdata and reconstruction results.

# Environment: Window 10 system, Matlab 2017b, Matconvnet-1.0-beta23, cuda 10.0

 

# MRI folder: these files focus on MRI reconstruction. There are three methods related to deep-learning-based MRI reconstruction in our paper, including AUTOMAP, DAGAN, ADMM-Net and the traditional method TGV. Their reconstruction results used in the reference are included in this folder.

 

# You can reproduce the results by downloading all the files and configure your workstation following the instruction of different established reconstruction methods, such as AUTOMAP, DAGAN, ADMM-Net.

 

# All the test data can be found in "InputData" and all the reconstruction images can be found in "ReconResult". Specified environment depending on network environment, for example, ACID building in DAGAN depends on Windows 10 system, TensorFlow 1.8.0, cuda 10.0

 

#If you have any problems, please contact with weiwenwu12@gmail.com; dianlinhu@gmail.com or any one of co-authors.

 

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