12:00-12:30 lunch；12:30-13:30 Talk

Abstract: CT image reconstruction from incomplete data, such as sparse views and limited angle

reconstruction, is an important and challenging problem in medical imaging. In this talk, I will

present a deep convolutional neural network (CNN) architecture, 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 adopted to improve the JSR-Net performance, which is efficient to  protect important

structures in the phantom. Numerical experiments demonstrate that JSR-Net outperforms some

latest model based reconstruction methods, as well as a recently proposed deep model.

欢迎各位同学积极参加，报名链接https://www.wjx.top/jq/29438199.aspx， 报名截止时间为10月25日

下午16:00，我们将为报名同学提供午餐。