title: Electrical impedance tomography aided by deep learning for applications in lung imaging and two-phase flow measurement
Creators: name: Yu, Hao
Publisher: name: The University of Edinburgh
Year: 2025
Subjects: subject: Electrical Impedance Tomography (EIT)
subject: Deep Learning
subject: Lung Imaging
subject: Two-Phase Flow
subject: Reference Voltage Estimation

description: Electrical impedance tomography (EIT) is an emerging, agile, and non-invasive imaging technology that has attracted extensive attention in both medical imaging and industrial inspection. In medical applications, the principal challenge lies in the highly nonlinear and ill-posed nature of EIT, which makes image reconstruction particularly sensitive to imperfect measurement signals and less robust to model inaccuracies. Although traditional model-based reconstruction methods have shown success in certain scenarios, they often exhibit limitations in terms of image quality, conductivity reconstruction accuracy, and computational efficiency, thereby constraining their practical application. In the industrial domain, a key issue is the high sensitivity of the reference voltage to environmental changes; without timely updates, the reference voltage inevitably effects the reconstructed conductivity distribution’s accuracy, ultimately leading to suboptimal measurements of dispersed-phase volume fractions in two-phase flow systems. Recently, data-driven approaches have become a research frontier in tomographic imaging, widely applied to computed tomography (CT) and magnetic resonance imaging (MRI). Nevertheless, learning-based reconstruction methods for EIT sensor systems are still at an early stage, and the seamless integration of deep learning algorithms with EIT hardware remains largely unexplored. Aiming to address this gap, this thesis systematically investigates the development of robust EIT algorithms based on deep learning, high-accuracy data-driven reference voltage estimation, and precise measurement of dispersed-phase volume fractions in industrial two-phase flow systems. In so doing, we provide theoretical insights and practical solutions for incorporating deep learning into EIT. To tackle the trade-off between temporal resolution and imaging accuracy in pulmonary imaging and industrial EIT, we propose a high-resolution reconstruction algorithm based on structureaware hybrid fused learning. Unlike traditional data-driven methods that rely on a singlebranch neural network to reconstruct multiple targets with different conductivity levels, our algorithm decouples the measured voltage by a segmentation branch and a conductivity reconstruction branch. It separately extracts structural features and conductivity information, then fuses the two features to reconstruct the conductivity distribution of target region, thus achieving a significant improvement in imaging accuracy. Importantly, we have thoroughly considered network complexity in the algorithm design, striving to balance high-accuracy imaging with real-time performance. Numerical simulations and experimental validations on multiple datasets, including three datasets with different geometric shapes and three experimental datasets, show that our proposed method excels in reconstructing multi-level, continuous conductivity distributions. Furthermore, analysis on real lung breathing data demonstrates the algorithm’s ability to accurately capture dynamic physiological changes over a complete respiratory cycle, thereby confirming its reliability and effectiveness in practical applications. Given the sensitivity of industrial time-difference EIT reference voltage to environmental changes, current research on this key problem remains limited. Existing methods often use data fitting to estimate the reference voltage; however, their accuracy deteriorates in complex and dynamic environments. To address this challenge, we introduce two novel methods for reference voltage estimation: a multiple measurement (MM) approach and a convolutional neural network (CNN)-based approach. The MM method exploits the fluctuation characteristics of the measurement voltage, allowing for rapid data fitting in dynamically changing scenarios. Meanwhile, the CNN-based approach leverages deep learning to establish a nonlinear mapping between the measurement voltage and the reference voltage, offering superior robustness in both static and dynamic environments. Comprehensive experiments, using an NEL oil–water dataset, a gas–water two-phase flow dataset from the University of Leeds, and a static water tank dataset from the University of Edinburgh, demonstrate that our methods significantly outperform traditional approaches in terms of reference voltage estimation accuracy, reconstruction quality, and robustness. Notably, under dynamic conditions, the CNN method exhibits exceptional performance, effectively meeting the real-time requirements of complex industrial inspection scenarios. Building on the remarkable capability of deep learning in nonlinear fitting and feature representation, its application to EIT for accurate dispersed-phase volume fraction measurement in the industrial sector presents exciting opportunities. By reducing the dependency on direct reference voltage measurements, deep learning not only optimizes two-phase flow volume fraction measurement processes but also greatly enhances measurement precision and efficiency. To this end, we propose an approach called attention-UNet with a fully connected network (AU-FC). By incorporating an attention mechanism, the method effectively suppresses voltage regions unrelated to the volume fraction, thereby enhancing feature extraction of crucial signals. Through the voltage reconstruction, AU-FC delivers highly accurate volume fraction predictions. Experimental validation on our constructed NEL dataset reveals that AUFC achieves superior performance in terms of MSE, MAE, and MAPE, surpassing existing methods and demonstrating real-time inference capabilities, making it an efficient and robust solution for online industrial monitoring. In conclusion, this thesis addresses the research gap in pulmonary breathing-state monitoring and two-phase flow dispersed-phase measurement using EIT by developing deep learning– based imaging and detection methods. The findings significantly advance EIT technology in terms of image quality, robustness, and application breadth, laying a solid theoretical and practical foundation for EIT to evolve into a more intelligent and resilient imaging and measurement technology.