Date and Time: Friday, October 15, 11:00-11:59 am
Speaker: Ruchi Guo, University of California, Irvine
Title: A Deep Direct Sampling Method for Solving Electrical Impedance Tomography
Abstract: Electrical impedance tomography (EIT) is a promising technique for non-invasive and radiation-free type of medical imaging. But a high-quality reconstruction for the EIT problem is challenging due to its severe ill-posedness. Based on the idea of direct sampling methods (DSMs) developed by Chow, Ito and Zou, we present a framework to construct deep neural networks for solving EIT problems, which is able to capture the underlying mathematical structure from backprojection of boundary measurement to coefficient distribution. The resulting Deep DSM (DDSM) is easy for implementation and its offline-online decomposition inherits efficiency from the original DSM that does not need any optimization process in reconstruction. Additionally, it is capable of systematically incorporating multiple Cauchy data pairs to achieve high-quality reconstruction and is highly robust to large noise.