A Novel Generative Adversarial Network Based on Conditional VAE for EIT Reconstruction of Lung Diseases

Electrical impedance tomography (EIT), a non-invasive, real-time, and cost-effective imaging technique, is widely studied in medical diagnostics for lung diseases. However, the severely ill-posed nonlinear inverse problem in EIT leads to reconstructed images being susceptible to noise-induced artifacts. This study aims to advance a deep learning technique to reconstruct high-resolution conductivity distributions using voltages measured by EIT sensors. We proposed a novel reconstruction algorithm called generative adversarial network based on conditional variational autoencoder (CVAE-GAN). We incorporated the true conductivity as a conditional variable into the latent representation of the variational autoencoder (VAE) decoder and encoder to form a conditional variational autoencoder (CVAE). A residual module was introduced into the CVAE decoder and encoder to facilitate the network in learning deeper feature representations, which improves the performance of the model. The adversarial learning strategy leverages the improved CVAE as the generator in a GAN framework, substantially enhancing the accuracy and robustness of the reconstructed images. Experimental results demonstrate that CVAE-GAN outperforms five state-of-the-art deep learning methods. Compared to the best alternative model, it achieves an 8.9% improvement in peak signal-to-noise ratio (PSNR) and a 3.2% improvement in structural similarity index (SSIM), while reducing mean squared error (MSE) by 33.33% and relative error (RE) by 24.57%. These results highlight the significant performance gains in terms of both accuracy and robustness for EIT image reconstruction. The proposed CVAE-GAN framework represents a significant advancement in EIT image reconstruction. By addressing key challenges such as noise-induced artifacts and achieving robust reconstructions, it provides a generalizable approach with transformative potential for real-world applications in medical imaging, particularly in the diagnostics and monitoring of lung diseases.