CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging. This work proposes a new deep convolutional neural network (CNN), called JSR-Net, that jointly reconstructs CT images and their associated Radon domain projections. JSR-Net combines the traditional model-based approach with deep architecture design of deep learning. A hybrid loss function is adapted to improve the performance of the JSR-Net making it more effective in protecting important image structures. Numerical experiments demonstrate that JSR-Net outperforms some latest model-based reconstruction methods, as well as a recently proposed deep model.
@article{arxiv.1812.00510,
title = {JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data},
author = {Haimiao Zhang and Bin Dong and Baodong Liu},
journal= {arXiv preprint arXiv:1812.00510},
year = {2019}
}