JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data
This addresses the challenging problem of incomplete CT reconstruction for medical imaging applications, representing an incremental improvement over existing approaches.
The paper tackles CT image reconstruction from incomplete data by proposing JSR-Net, a deep CNN that jointly reconstructs images and Radon domain projections, outperforming latest model-based and deep learning methods in numerical experiments.
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.