【数学所】Supervised and unsupervised learning methods for CT Image Reconstruction

Time：Friday, May 27th, 2022, 14:00-15:00

Speaker:  Qiaoqiao Ding, Shanghai Jiao Tong University

Location：Tecent Meeting

Room Number: 778-1386-4492

Link:  https://meeting.tencent.com/dm/N9BlY0oqqrMp

Abstract: 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.