Deep Learning for Image Reconstruction in Electrical Tomography: A Review

Electrical tomography (ET) has emerged as a safe and real-time imaging technology for decades due to its fast, noninvasive, and radiation-free characteristics. By reconstructing the spatial distribution or dynamic changes in the electrical properties of target regions, ET finds broad applications in nondestructive testing, industrial process monitoring, and biomedical research. Recent advancements in deep learning have significantly enhanced the accuracy, efficiency, and robustness of ET image reconstruction, particularly in electrical impedance tomography (EIT), electrical capacitance tomography (ECT), and electromagnetic tomography (EMT). Despite the growing interest, challenges persist in handling the ill-posed nature of ET imaging, especially for EMT, which remains underexplored compared to EIT and ECT. This review delves into the fundamental principles of ET and surveys recent progress in deep learning-driven approaches. We focus on two key paradigms: direct reconstruction networks (data-driven) and hybrid frameworks that integrate deep learning with classical algorithms (image-driven). The discussion highlights emerging trends, including multimodal ET integration, dataset optimization, and the design of advanced neural architectures tailored for ET-specific constraints. This study underscores the potential of deep learning to redefine ET systems, paving the way for more intelligent, versatile, and accurate imaging solutions.