A New Divide-and-Conquer Method for 3-D Electrical Impedance Tomography

Sebastien Martin, T.m. Choi*

*此作品的通信作者

    研究成果: Article同行評審

    2 引文 斯高帕斯(Scopus)

    摘要

    Electrical impedance tomography (EIT) is a patient-safe approach for imaging applications and is a promising technology for biomedical imaging. By applying a small electrical current to living tissue and measuring the electrical potential at different points of the tissue's boundary, it is possible to solve the inverse problem and to generate a map of the tissue's conductivities. Although it is a promising method, the reconstruction process remains a complicated task, and real-time nonlinear image reconstruction algorithms have not yet been widely adopted. Recent studies have proposed the use of artificial neural networks (ANN) to solve the EIT inverse problem. ANNs can give a nonlinear conductivity distribution. Although several studies have examined 2-D finite-element models, very little work has been done on 3-D problems. In this paper, a solution based on the divide-and-conquer method and ANNs is used to solve the nonlinear inverse problem without any linearization. The solution presented here reduces the difficulty of training large ANNs, which are commonly required to solve a 3-D EIT problem with artificial intelligence algorithms.

    原文English
    文章編號5100404
    期刊IEEE Transactions on Magnetics
    54
    發行號3
    DOIs
    出版狀態Published - 1 三月 2018

    指紋

    深入研究「A New Divide-and-Conquer Method for 3-D Electrical Impedance Tomography」主題。共同形成了獨特的指紋。

    引用此