Learned enclosure method for experimental EIT data
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
Electrical impedance tomography (EIT) is a non-invasive imaging method with
diverse applications, including medical imaging and non-destructive testing.
The inverse problem of reconstructing internal electrical conductivity from
boundary measurements is nonlinear and highly ill-posed, making it difficult to
solve accurately. In recent years, there has been growing interest in combining
analytical methods with machine learning to solve inverse problems. In this
paper, we propose a method for estimating the convex hull of inclusions from
boundary measurements by combining the enclosure method proposed by Ikehata
with neural networks. We demonstrate its performance using experimental data.
Compared to the classical enclosure method with least squares fitting, the
learned convex hull achieves superior performance on both simulated and
experimental data.
