报告题目：A neural network with encoded visible edge prior for limited-angle computed tomography reconstruction

报 告 人：李宏伟教授（首都师范大学）

报告时间：2021年11月30日  14:30-15:30

报告地点：腾讯会议（723624521）

报告摘要：

The limited-angle reconstruction problem is highly ilposed and conventional reconstruction algorithms would introduce heavy artifacts. Various models and methods have been proposed to improve the quality of reconstructions by introducing different priors regarding to the projection data or ideal images. However, the assumed priors might not be practically applicable to all limited-angle reconstruction problems. Convolutional neural network (CNN) exhibits great promise in the modelling of data coupling and has recently become an important technique in medical imaging applications. Although existing CNN methods have demonstrated promising results, their robustness is still a concern. In light of the theory of visible and invisible boundaries, we propose an alternating edge-preserving diffusion and smoothing neural network (AEDSNN) for limited-angle reconstruction which builds the visible boundaries as priors into its structure. The proposed method generalizes the alternating edge-preserving diffusion and smoothing (AEDS) method for limited-angle reconstruction by replacing its regularization terms by CNNs. Experiments show that the proposed AEDSNN significantly outperforms existing methods in terms of both reconstruction quality and efficiency. Especially, it effectively breaks through the piecewise constant assumption usually assumed by conventional reconstruction algorithms, and works well with piecewise smooth images.

报告人简介：

李宏伟，男，教授，博士生导师，首都师范大学数学学院。1998年毕业于中国科学院计算数学与科学工程计算研究所，获理学硕士学位。2002年毕业于中科院软件研究所，获工学博士学位。2002-2005就职于中科院软件所并行计算中心，研究偏微分方程大规模数值并行计算。2005-2008挪威卑尔根大学集成石油研究中心，博士后，从事油藏模拟反问题计算及图像处理。2008至今，首都师范大学数学科学学院，主要研究CT重建及相关图像处理问题。他的研究以实际应用为导向，其所研制的图像增强图像分割以及CT图像环状伪影校正算法已被CT厂商采用。近年来，他的部分研究兴趣转移到了具有挑战性的CT重建问题上来，如低剂量和有限角重建，相关研究成果已发表在Medical Physics, Optical Express, JMTV, TIP, inverse problems等国际知名期刊上。