数学科学研究所
Insitute of Mathematical Science

Seminar: Supervised and unsupervised learning methods for CT Image Reconstruction

Seminar| Institute of Mathematical Sciences

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

Location:Tecent Meeting

 

Speaker:  Qiaoqiao DingShanghai Jiao Tong University

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.


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

Room Number: 778-1386-4492


地址:上海市浦东新区华夏中路393号
邮编:201210
上海市徐汇区岳阳路319号8号楼
200031(岳阳路校区)