报告题目 (Title)：Supervised and unsupervised learning methods for CT Image Reconstruction
中文题目：CT重建的监督以及无监督学习方法
报告人 (Speaker)：丁乔乔 助理研究员（上海交通大学）
报告时间 (Time)：2024年4月26日 (周五) 11:00
报告地点 (Place)：校本部GJ403
邀请人(Inviter)：纪丽洁
摘要：Image reconstruction from down-sampled and corrupted measurements, such as fast MRI and low dose CT, is mathematically ill-posed inverse problem. Deep neural network (DNN) has been becoming a prominent tool in the recent development of medical image reconstruction methods. In this talk, I will introduce two works on incorporating classical image reconstruction method and deep learning method. In the first work, we proposed a multi-scale DNN for sparse view CT reconstruction, which directly learns an interpolation scheme to predict the complete set of 2D Fourier coefficients in Cartesian coordinates from the given measurements in polar coordinates. In the second work, we proposed an unsupervised deep learning method for LDCT image reconstruction, which does not require any external training data. The proposed method is built on a re-parameterization technique for Bayesian inference via deep network with random weights, combined with additional total variational(TV) regularization. The experiments on both sparse CT and low dose CT problem show that the proposed method provided state-of-the-art performance.