A Reliable Deep Learning Scheme for Nonlinear Reconstructions in Electrical Impedance Tomography

Significant progress has recently been made in applying deep learning methods to Electrical Impedance Tomography (EIT), which is a promising technique for non-invasive, bedside ionizing radiation free, and real-time monitoring of lung health. However, different with conventional methods solved from physical model, deep learning methods, as data-driven approaches, suffer from reliability problem, i.e., the “confidence level” is unknown when using deep learning methods as EIT solvers. In this work, a reliable deep learning scheme (RDLS) is proposed to deal with typical nonlinear EIT problems. A noticeable characteristic of the proposed RDLS is that, besides the reconstructions, pixel-based uncertainties of the results are also predicted with a Bayesian framework. Further, physical information based on a spectral analysis and back-propagated field are incorporated into the proposed RDLS to improve the robustness. By training with random inclusions, it is quantitatively shown that the proposed RDLS provides high-quality reconstructions and uncertainty qualifications, where the predicted uncertainties are verified by both linear and nonlinear correlations with true absolute errors that are only known when ground truth is known. Our approach provides fast, high-quality, and robust EIT imaging with pixel-based uncertainties in both numerical and lab-control experimental tests.