Conditional Diffusion Model for Electrical Impedance Tomography

TL;DR

This paper introduces a conditional diffusion model with voltage constraints to improve the quality and robustness of electrical impedance tomography images, addressing noise sensitivity and non-linearity issues.

Contribution

It proposes a novel conditional diffusion model with voltage consistency constraints for EIT image reconstruction, integrating forward information to enhance image quality.

Findings

Significantly improves reconstructed image quality.

Demonstrates robustness and generalization in experiments.

Validates effectiveness through simulation and physical tests.

Abstract

Electrical impedance tomography (EIT) is a non-invasive imaging technique, which has been widely used in the fields of industrial inspection, medical monitoring and tactile sensing. However, due to the inherent non-linearity and ill-conditioned nature of the EIT inverse problem, the reconstructed image is highly sensitive to the measured data, and random noise artifacts often appear in the reconstructed image, which greatly limits the application of EIT. To address this issue, a conditional diffusion model with voltage consistency (CDMVC) is proposed in this study. The method consists of a pre-imaging module, a conditional diffusion model for reconstruction, a forward voltage constraint network and a scheme of voltage consistency constraint during sampling process. The pre-imaging module is employed to generate the initial reconstruction. This serves as a condition for training the conditional diffusion model. Finally, based on the forward voltage constraint network, a voltage consistency constraint is implemented in the sampling phase to incorporate forward information of EIT, thereby enhancing imaging quality. A more complete dataset, including both common and complex concave shapes, is generated. The proposed method is validated using both simulation and physical experiments. Experimental results demonstrate that our method can significantly improves the quality of reconstructed images. In addition, experimental results also demonstrate that our method has good robustness and generalization performance.