Iterative Decomposition Algorithm for Electrical Impedance Tomography to Image Large Resistivity Changes

In electrical impedance tomography (EIT), the commonly used linear reconstruction algorithms are typically suitable for imaging small resistivity changes. However, in many applications of EIT, such as in imaging maximum ventilation of the lung with EIT, the resistivity changes can be very large. In such cases, the linear algorithms have reduced accuracy and may affect the image interpretation. To address this issue, a novel iterative decomposition algorithm (IDA) is developed. In IDA, the large resistivity change target is decomposed into small changes so that the final solution can be obtained through multiple linear reconstructions, and the sensitivity matrix is iteratively updated based on the result of each linear reconstruction. To test the performances of IDA, both simulation and in vivo experiments were conducted. The experimental results demonstrate that, in imaging large resistivity changes in lung ventilation, the traditional linear EIT algorithm caused nonnegligible linear approximation errors (LAEs) and location errors (LEs). For IDA, it could reduce LAEs and LEs by 13.4% and 11.6% respectively. The resistivity changes reconstructed by IDA had a better correlation with the lung volume changes. Therefore, IDA was verified as an efficient method for imaging large resistivity changes.