Benchmark computations for the polarization tensor characterization of small conducting objects

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

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.

Citation

Amad, A., Ledger, P. D., Betcke, T., & Praetorius, D. (2022). Benchmark computations for the polarization tensor characterization of small conducting objects. Applied Mathematical Modelling, 111, 94-107. https://doi.org/10.1016/j.apm.2022.06.024

• Journal Article Type: Article
• Acceptance Date: Jun 13, 2022
• Online Publication Date: Jun 16, 2022
• Publication Date: 2022-11
• 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#!