Unsupervised Deep-Learning-Based Error Image Prior (DLEIP) Algorithm for Lung Electrical Impedance Tomography (EIT)

A novel unsupervised algorithm, named deep-learning-based error image prior (DLEIP), is proposed for lung electrical impedance tomography (EIT). An attention-guided denoising network (AD-Net) is employed in DLEIP algorithm to optimizes the initial conductivity distribution through built-in back-propagation. The sparse block, feature enhancement block and attention block are also introduced to AD-Net for improving learning efficiency, while incorporating residual learning technique to extract the deep error image prior. The simulation results indicate that the ventilation areas and the lesions can be effectively reconstructed by DLEIP algorithm. The correlation coefficients (CCs) of the reconstructed images are higher than 0.82, and the relative errors (REs) are lower than 0.40. Moreover, the DLEIP algorithm has good regularization integration ability. By integrating with total variation (TV) regularization, the DLEIP-TV algorithm can be obtained to further improves the imaging accuracy. During the experiment, the mapping model is constructed in the circular domain to verify the practicality of the proposed algorithm. The results indicate that the shape and size of the object in the domain can be reconstructed more accurately. The CCs of the reconstructed images are higher than 0.85, and the REs are lower than 0.26.