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Benchmark computations for the polarization tensor characterization of small conducting objects

Amad, AAS; Ledger, P. D.; Betcke, T.; Praetorius, D.

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Authors

AAS Amad

T. Betcke

D. Praetorius



Abstract

The characterisation of small low conducting inclusions in an otherwise uniform background from low-frequency electrical field measurements has important applications in medical imaging using electrical impedance tomography as well as in geological imaging using electrical resistivity tomography. It is known that such objects can be characterised by a Póyla-Szegö (polarizability) tensor. Such characterisations have attracted interest as they can provide object features in a machine learning classification algorithm and provide an alternative imaging solution. However, to be able train machine learning algorithms, large dictionaries are required and it is essential that the characterisations are accurate. In this work, we obtain accurate numerical approximations to the tensor coefficients, by applying an adaptive boundary element method. The goal being to provide a sequence of benchmark computations for the tensor coefficients to allow other software developers check the accuracy of their codes.

Journal Article Type Article
Acceptance Date Jun 13, 2022
Online Publication Date Jun 16, 2022
Publication Date 2022-11
Publicly Available Date Mar 28, 2024
Journal Applied Mathematical Modelling
Print ISSN 0307-904X
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 111
Pages 94-107
DOI https://doi.org/10.1016/j.apm.2022.06.024
Keywords Boundary element method; Adaptive mesh; Benchmark computations; Object characterisation; Inverse problems
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S0307904X22002943#!

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