Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss

This repository contains the code for CNN/WGAN-MSE/VGG network introduced in the following paper

Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss

Installation

Make sure you have Python installed, then install TensorFlow on your system.

Usage

Prepare the training data

In order to start the training process, please prepare your training data in the following form:

• data: N x W x H
• label: N x W x H

Here N, W, and H are number, depth, width, and height of the input data, respectively. Then data and label are stored in a hdf5 file.

Pre-trained VGG model

Please also download the pre-trained VGG model from here.

Training network

python train_cnn.py
python train_wgan.py

Contact

Email: qs dot yang18 at gmail dot com

Any discussions, suggestions and questions are welcome!

Citation

@article{ldct_wgan_perceptual_loss,
  author={Q. Yang and P. Yan and Y. Zhang and H. Yu and Y. Shi and X. Mou and M. K. Kalra and Y. Zhang and L. Sun and G. Wang},
  journal={IEEE Transactions on Medical Imaging},
  title={Low Dose {CT} Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss},
  year={2018},
  volume={37},
  number={6},
  pages={1348-1357},
  doi={10.1109/TMI.2018.2827462},
  ISSN={0278-0062},
  month={june},
}

Requirements

Python Tensorflow