Learned enclosure method for experimental EIT data
This work addresses the ill-posed inverse problem in EIT for applications like medical imaging and non-destructive testing, representing an incremental improvement over existing methods.
The paper tackled the problem of reconstructing the convex hull of inclusions in electrical impedance tomography (EIT) from boundary measurements by combining the enclosure method with neural networks, achieving superior performance compared to the classical method on both simulated and experimental data.
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.