Image reconstruction algorithm for electrical impedance tomography based on block sparse Bayesian learning

Electrical impedance tomography (EIT) is a promising agile imaging modality that allows estimation of the electrical conductivity distribution at the interior of an object from boundary measurements, which is attractive in a broad spectrum of biomedical, geophysical, and industrial continuous monitoring applications. As the inverse problem of EIT image reconstruction generally suffers from severe ill-posedness, various regularization techniques have been adopted to impose moderate constraints so as to obtain desired, usually sparse solutions. Extensive investigation showed that an enhanced recover performance can be achieved by exploiting a priori knowledge on the sparse structures. For regularization incorporating such knowledge, Bayesian approach is considered a natural mechanism. In this paper, we introduce the concept of sparse Bayesian learning to EIT imaging, which yields improved accuracy and robustness in the image reconstruction performance by exploiting signal structures in terms of their block sparsity and intra-block correlation. The effectiveness of the proposed algorithm is confirmed by the phantom test results.