2018/12/03 by Haimiao Zhang, Bin Dong, Zhang, Haimiao +3
Engineering · Medicine · #65C20 #68Q32 #92C50 #Advanced X-ray and CT Imaging #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Medical Imaging Techniques and Applications #Medical Physics (physics.med-ph) #Optimization and Control (math.OC) #Radiation Dose and Imaging
paper · pdf · doi:10.48550/arxiv.1812.00510
openalex publication_date 2018/12/03 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.