PGCT-PINN: A Physics-Guided Cooperative Training Framework for Enhanced Resolution and Consistency in Lung EIT Imaging

Electrical impedance tomography (EIT) has attracted increasing attention in medical imaging due to its noninvasive, portable, and real-time imaging capabilities. However, conventional EIT reconstruction methods still struggle to achieve high spatial resolution while maintaining physical consistency, particularly in pulmonary applications. In this work, we propose a novel physics-guided cooperative training framework based on PINN (PGCT-PINN) that enhances both resolution and consistency in lung EIT imaging. The proposed approach integrates the EIT forward model into a dual-branch architecture, comprising a voltage distribution prediction network (V-Net) and a conductivity reconstruction network (σ -Net) while introducing cooperative training and physics-guided loss functions to leverage both data-driven features and physical constraints. High-resolution 3-D thorax-lung simulation datasets are generated from CT priors via finite element modeling to enable robust training under both data-sufficient and data-limited conditions. Extensive simulation and phantom experiments demonstrate that PGCT-PINN achieves higher reconstruction accuracy, clearer structural boundaries, and stronger generalization capability, outperforming representative data-driven baselines, such as Pix2Pix and U-Net, when training data are limited. Furthermore, a lightweight version of PGCT-PINN is deployed on a Raspberry Pi-based embedded platform, achieving real-time lung imaging at 16 frames/s, thus offering a practical solution for bedside pulmonary monitoring and early disease detection.