An Inclusion Boundary and Conductivity Simultaneous Estimation Method for Ultrasound Reflection Guided Electrical Impedance Tomography

Abstract

Electrical impedance tomography (EIT) is a promising technology to visualize the conductivity distribution within a closed domain by injecting electric currents and measuring the corresponding voltages at its boundary. Due to the advantages of non-radiative, high-speed and low-cost, EIT has been widely used in industrial and biomedical fields. However, the image reconstruction of EIT is a nonlinear ill-posed inverse problem, which greatly limits its spatial resolution and noise robustness. Although many efforts have been devoted to developing the EIT imaging method, the improvement of EIT spatial resolution and noise robustness is still a focus of current research. Inspired by multimodal information fusion, an ultrasound reflection guided EIT imaging method is proposed to simultaneously reconstruct the inclusion boundary and conductivity. The local structural information obtained from ultrasound reflection measurements will provide a good initial guess and prior constraint on inclusion boundary reconstruction of EIT under the Bayesian inversion framework. The Maximum A Posterior (MAP) estimation combining with alternative optimization strategy is used to solve the inclusion boundary and conductivity simultaneous estimation problem. The numerical and phantom experimental tests are carried out and show that the proposed method has better imaging accuracy and noise robustness than traditional single-modality EIT method.