Optimal Bayesian experimental design for electrical impedance tomography in medical imaging

Abstract Optimal design of electronic devices such as sensors is essential since it results in more accurate output at the shortest possible time. In this work, we develop optimal Bayesian inversion for electrical impedance tomography (EIT) technology in order to improve the quality of medical images generated by EIT and to put this promising imaging technology into practice. We optimize Bayesian experimental design by maximizing the expected information gain in the Bayesian inversion process in order to design optimal experiments and obtain the most informative data about the unknown parameters. We present optimal experimental designs including optimal frequency and optimal electrode configuration, all of which result in the most accurate estimation of the unknown quantities to date and high-resolution EIT medical images, which are crucial for diagnostic purposes. Numerical results show the efficiency of the proposed optimal Bayesian inversion method for the EIT inverse problem.