报告题目：Globally Convergent methods for inverse problems in Diffuse Optical Tomography and its applications

报告人：苏建忠 教授

主持人：单文锐 副教授

时 间： 2015年1月9日（周五）上午 9:00--10:00

地 点：主楼 1214

报告摘要：

In this talk, we give an overview of both theory and experimental applications of a numerical Globally Convergent Method (GCM) for an inverse problem in Diffuse Optical Tomography. The method is for an inverse problem for an elliptic partial differential equation with an unknown potential, an important mathematical problem at the core of Near-Infrared laser imaging technology. The GCM reconstruction method fundamentally differs from other current methods based on the Newton's method or optimization scheme. GCM does not require a relative precise first guess and hence it is capable in dealing with complex media and realistic geometry for biomedical applications. Several sets of boundary data measurements are generated by placing the light source at several designated locations. Mathematically, a global convergence theorem assures the success of the numerical reconstruction method. Then we use this method in experiments of an optical phantom emulating rat brain suffering a stroke. We present the experimental setup of optical measurements and report accurate images and their physical parameters of hidden interior objects inside an optical phantom, which are reconstructed based on light intensity data collected on the object’s surface. Finally we test the method in animal experiments. The examples illustrate how the mathematical theory of GCM be used in tomographic reconstruction of experimental data.

报告人简介：

苏建忠，美国得克萨斯大学阿灵顿分校数学系教授、系主任（Professor and Chair）。主要研究方向为神经动力系统、偏微分方程。