ARTIFICIAL BOUNDARY CONDITIONS AND DOMAIN TRUNCATION IN ELECTRICAL IMPEDANCE TOMOGRAPHY. PART II: STOCHASTIC EXTENSION OF THE BOUNDARY MAP

作者：Calvetti Daniela^*; Hadwin Paul J; Huttunen Janne M J; Kaipio Jari P; Somersalo Erkki

来源：Inverse Problems and Imaging, 2015, 9(3): 767-789.

DOI：10.3934/ipi.2015.9.767

• Domain decomposition
• Dirichlet-to-Neumann
• Steklov-Poincare
• principal component approximation
• Bayes' theorem

In [3], the authors discussed the electrical impedance tomography (EIT) problem, in which the computational domain with an unknown conductivity distribution comprises only a portion of the whole conducting body, and a boundary condition along the artificial boundary needs to be set so as to minimally disturbs the estimate in the domain of interest. It was shown that a partial Dirichlet-to-Neumann operator, or Steklov-Poincare map, provides theoretically a perfect boundary condition. However, since the boundary condition depends on the conductivity in the truncated portion of the conductive body, it is itself an unknown that needs to be estimated along with the conductivity of interest. In this article, we develop further the computational methodology, replacing the unknown integral kernel with a low dimensional approximation. The viability of the approach is demonstrated with finite element simulations as well as with real phantom data.

• 出版日期2015-8