Deep prior embedding method for Electrical Impedance Tomography

Abstract

This paper presents a novel deep learning-based approach for Electrical Impedance Tomography (EIT) reconstruction that effectively integrates image priors to enhance reconstruction quality. Traditional neural network methods often rely on random initialization, which may not fully exploit available prior information. Our method addresses this by using image priors to guide the initialization of the neural network, allowing for a more informed starting point and better utilization of prior knowledge throughout the reconstruction process. We explore three different strategies for embedding prior information: non-prior embedding, implicit prior embedding, and full prior embedding. Through simulations and experimental studies, we demonstrate that the incorporation of accurate image priors significantly improves the fidelity of the reconstructed conductivity distribution. The method is robust across varying levels of noise in the measurement data, and the quality of the reconstruction is notably higher when the prior closely resembles the true distribution. This work highlights the importance of leveraging prior information in EIT and provides a framework that could be extended to other inverse problems where prior knowledge is available.