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CT image reconstruction from incomplete data, such as sparse views andlimited angle reconstruction, is an important and challenging problem inmedical imaging. This work proposes a new deep convolutional neural network(CNN), called JSR-Net, that jointly reconstructs CT images and their associatedRadon domain projections. JSR-Net combines the traditional model-based approachwith deep architecture design of deep learning. A hybrid loss function isadapted to improve the performance of the JSR-Net making it more effective inprotecting important image structures. Numerical experiments demonstrate thatJSR-Net outperforms some latest model-based reconstruction methods, as well asa recently proposed deep model.

JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data