Image Reconstruction for Electrical Impedance Tomography Using Group Sparsity Constraint

Abstract: Electrical Impedance Tomography (EIT) is one of electrical tomography modalities for non-intrusive, high-temporal-resolution conductivity imaging. Recently, EIT has found it extensive research and applications in both industrial and biomedical fields. Despite its high temporal resolution, improvement of image quality in practical situations is intensely desired and has become a research focus for long. In this work, an image reconstruction algorithm based on group sparsity constraint is introduced. In EIT, conductivity variance usually appears as groups. Therefore, clustered sparsity prior is expected to be effective to promote image features in EIT imaging. Motivated by this idea, EIT image reconstruction based on group sparsity constraint is investigated and grouping methods are discussed. Numerical analysis is performed on small scale conductivity phantoms to validate the algorithm. Furthermore, static experiments using our in-house developed EIT system are presented as well. Both results confirm the performance of group sparsity based algorithm.

Biography: Yunjie Yang is a PhD student of the Agile Tomography Group at IDCOM, University of Edinburgh. He received the B.Eng. and M.Sc. degrees in measurement science and electronics from Anhui University and Tsinghua University in China, respectively. His current research interests include Electrical Impedance Tomography (EIT) and Electrical Capacitance Tomography (ECT) for biomedical and industrial imaging. He received the 2015 IEEE I&M Society Graduate Fellowship Award to partially support his PhD research on an advanced EIT system for medical imaging. He is also awarded an Innovation Initiative Grant in 2016 by the University of Edinburgh Development Trust to develop a unified tomography platform.