报告专家：丁乔乔 助理研究员（上海交通大学 自然科学研究院）

报告题目：Deep Learning-Based Medical Image Reconstruction from Incomplete Data

报告地点：会议号：452127112（腾讯会议）

报告时间：6月8日下午3:00

报告简介：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.

专家个人简介：In 2018, Dr. Qiaoqiao Ding graduated from Shanghai Jiao Tong University and received her Ph.D. degree in applied mathematics. From 2018 to 2021, she carried out research as a postdoc at National University of Singapore under the supervision of Prof. Hui Ji. In 2021, she joined the research scientist faculty of Institute of Natural Sciences of Shanghai Jiao Tong University. Her research interests include machine learning, image processing and medical imaging.