A Conditional Diffusion Model for Electrical Impedance Tomography Image Reconstruction

TL;DR

This paper introduces CDEIT, a novel conditional diffusion model for electrical impedance tomography (EIT) image reconstruction, which effectively generates high-quality conductivity images from boundary voltage measurements, outperforming existing methods.

Contribution

The paper proposes a new diffusion-based approach for EIT reconstruction that leverages conditional denoising, enabling better image quality and applicability to real datasets.

Findings

CDEIT outperforms state-of-the-art methods on synthetic and real datasets.

The normalization procedure allows models trained on simulated data to be applied to real data.

The approach demonstrates robustness across varying experimental conditions.

Abstract

Electrical impedance tomography (EIT) is a non-invasive imaging technique, capable of reconstructing images of the electrical conductivity of tissues and materials. It is popular in diverse application areas, from medical imaging to industrial process monitoring and tactile sensing, due to its low cost, real-time capabilities and non-ionizing nature. EIT visualizes the conductivity distribution within a body by measuring the boundary voltages, given a current injection. However, EIT image reconstruction is ill-posed due to the mismatch between the under-sampled voltage data and the high-resolution conductivity image. A variety of approaches, both conventional and deep learning-based, have been proposed, capitalizing on the use of spatial regularizers, and the paradigm of image regression. In this research, a novel method based on the conditional diffusion model for EIT reconstruction is proposed, termed CDEIT. Specifically, CDEIT consists of the forward diffusion process, which first gradually adds Gaussian noise to the clean conductivity images, and a reverse denoising process, which learns to predict the original conductivity image from its noisy version, conditioned on the boundary voltages. Following model training, CDEIT applies the conditional reverse process on test voltage data to generate the desired conductivities. Moreover, we provide the details of a normalization procedure, which demonstrates how EIT image reconstruction models trained on simulated datasets can be applied on real datasets with varying sizes, excitation currents and background conductivities. Experiments conducted on a synthetic dataset and two real datasets demonstrate that the proposed model outperforms state-of-the-art methods. The CDEIT software is available as open-source (https://github.com/shuaikaishi/CDEIT) for reproducibility purposes.