Contactless Electrical Impedance Tomography with Deep Learning for Lung Monitoring: Phantom Study

This study proposes the application of contactless electrical impedance tomography (CEIT) for lung imaging, which is a crucial tool for respiratory disease diagnosis and monitoring. By employing the contactless measurement approach, CEIT not only avoids the contact impedance of traditional EIT but also has the potential to reduce discomfort for users, such as avoiding pressure on the chest caused by traditional EIT electrode belts. This study designed a phantom experiment to investigate the feasibility of CEIT in monitoring lung respiration. The deformations of the lung simulators are monitored through the difference in magnitude and phase angle of the measured impedance data. An image reconstruction algorithm combining the Landweber iteration and neural network was proposed. The method comprehensively utilized impedance magnitude and phase angle information, significantly improving the quality of CEIT's image reconstruction. With the proposed image reconstruction method, our CEIT can effectively achieve ventilation monitoring. It is a significant advancement for CEIT in the field of lung monitoring.