Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods have shown great potential in removing the complex and spatial-variant noises in CT images. However, some residue artifacts would appear in the denoised image due to complexity of noises. A cascaded training network was proposed in this work, where the trained CNN was applied on the training dataset to initiate new trainings and remove artifacts induced by denoising. A cascades of convolutional neural networks (CNN) were built iteratively to achieve better performance with simple CNN structures. Experiments were carried out on 2016 Low-dose CT Grand Challenge datasets to evaluate the method's performance.
@article{arxiv.1705.04267,
title = {A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising},
author = {Dufan Wu and Kyungsang Kim and Georges El Fakhri and Quanzheng Li},
journal= {arXiv preprint arXiv:1705.04267},
year = {2017}
}