Data Pattern With ECT Sensor and Its Impact on Image Reconstruction

Abstract

Electrical capacitance tomography (ECT) is a tomographic imaging technique for monitoring and studying dielectric processes. Image reconstruction in ECT is a nonlinear and typical inverse problem and has been widely investigated. In this paper, we investigate the ECT sensor data pattern and its impact on image reconstruction. By analyzing the capacitance data of certain typical permittivity distributions, the non-linearity with ECT image reconstruction is characterized. A modified linear model for ECT image reconstruction is presented, in which the contributions to image reconstruction due to the capacitance between different electrode pairs are considered. Additionally, the error associated with ECT sensor sensitivity distribution is taken into account. Based on the presented model, a total least squares and total variation based regularization method is used to reconstruct the permittivity distribution. The results based on both simulation and experimental data show that the presented model together with the total least squares and total variation based regularization method can produce fairly high quality reconstructions with clear edges and high precision.