A Two-Stage Imaging Framework Combining CNN and Physics-Informed Neural Networks for Full-Inverse Tomography: A Case Study in Electrical Impedance Tomography (EIT)

TL;DR

This paper introduces a two-stage hybrid framework combining CNNs and PINNs to improve the reconstruction of internal conductivities in Electrical Impedance Tomography, addressing limitations of existing methods.

Contribution

It presents a novel two-stage approach that integrates data-driven CNNs with physics-informed neural networks for more accurate EIT reconstructions.

Findings

Enhanced reconstruction accuracy in EIT

Effective integration of supervised and unsupervised learning

Overcomes limitations of prior PINN-based methods

Abstract

Electrical Impedance Tomography (EIT) is a highly ill-posed inverse problem, with the challenge of reconstructing internal conductivities using only boundary voltage measurements. Although Physics-Informed Neural Networks (PINNs) have shown potential in solving inverse problems, existing approaches are limited in their applicability to EIT, as they often rely on impractical prior knowledge and assumptions that cannot be satisfied in real-world scenarios. To address these limitations, we propose a two-stage hybrid learning framework that combines Convolutional Neural Networks (CNNs) and PINNs. This framework integrates data-driven and model-driven paradigms, blending supervised and unsupervised learning to reconstruct conductivity distributions while ensuring adherence to the underlying physical laws, thereby overcoming the constraints of existing methods.