Target Adaptive Differential Iterative Reconstruction (TADI): A Robust Algorithm for Real-Time Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a noninvasive functional diagnostic technique that has been successfully applied to human lung and brain. EIT reconstruction is an ill-conditioned problem: the regularization parameters establish a trade-off between acceptable data fitting and acceptable solution stability. This trade-off is necessary to select the optimal regularization parameters for each data frame to continuously obtain high-quality images in real-time monitoring. However, using the objective regularization parameter selection method before each image reconstruction may reduce the reconstruction efficiency, and using heuristic selection for all images may cause significant quality degradation, thereby creating challenges in long-term clinical EIT monitoring. This study explores a robust EIT target adaptive differential iterative reconstruction algorithm that does not require the advance selection of the optimal regularization parameter to facilitate the clinical application of EIT long-term bedside monitoring. Specifically, second-order Taylor approximation expansion and Euler-Lagrange theorem were used to derive the conductivity differential iteration relationship. Furthermore, the reconstruction quality and generalization ability of the proposed algorithm were validated based on comparisons with the L-curve and the generalized cross-validation parameter selection methods through simulations, phantom experiments, and human lung recruitment data. Compared with the L-curve and generalized cross-validation methods, the TADI algorithm reduces the position error by 33.07% and 47.17%, the ringing by 26.49% and 32.69%, and the shape deformation by 18.34% and 19.40% on average, respectively. The results revealed that the TADI algorithm could achieve a significant improvement over the existing state-of-the-art methods in terms of stability, consistency, and efficiency.