A Shape-Based Statistical Inversion Method
A shape-based statistical inversion method is proposed for Electrical Impedance Tomography (EIT) and Ultrasound Reflection Tomography (URT) dual-modality imaging. It is promising to improve the imaging accuracy in inclusion detection problems. The proposed image reconstruction method is based on the statistical shape inversion framework. The likelihood function is derived from EIT and URT forward models. The prior distribution is constructed using the Markov random field (MRF) prior. The measurement uncertainty is modeled by conditional error model method. The statistical shape inversion problem is solved by the Maximum a posterior (MAP) method with conventional error model. A set of numerical and experimental tests are carried out to evaluate the performance of the proposed method. The results show that the proposed EIT/URT dualmodality imaging method has obvious improvement in imaging accuracy compared to the traditional single-modality EIT and URT methods
Dual modality imaging, inclusion boundary reconstruction, electrical impedance tomography, ultrasound reflection tomography, statistical inversion.