Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique aiming to reconstruct the internal conductivity distribution of an object from boundary measurements. Current research heavily emphasizes improving the accuracy and robustness of EIT reconstructions by employing advanced deep learning architectures, such as convolutional neural networks (CNNs), physics-informed neural networks (PINNs), and diffusion models, often combined with traditional methods like the D-bar method or half-quadratic splitting. These advancements address the ill-posed nature of the inverse problem inherent in EIT, leading to improved image quality and broader applicability across diverse fields, including medical imaging and industrial process monitoring.