Electrical Impedance Tomography using a Weighted Bound-Optimization Block Sparse Bayesian Learning Approach

Electrical Impedance Tomography (EIT) is a developing medical imaging technique which derives the conductivity distribution of a subject with significant temporal resolution. Despite the recent advances in both EIT reconstruction algorithms and hardware, the limited spatial resolution, i.e. low distinguishability between the inclusions, and the presence of artifacts remain the main issues. To address them, block sparse Bayesian learning (BSBL) frameworks have been adopted in EIT, based on the assumption of block-structured inclusions and using minimization of a Bayesian-form cost function in an unsupervised learning manner. To further improve the imaging quality and to enhance convergence speed we combine a Bound-Optimization (BO) and a weighted BSBL approach, introducing priorily estimated weights obtained by a single-step approach, to each block's hyperparameter estimation. Simulations based on 2D circular domains and evaluation using experimental and in-vivo data verify the proposed method's performance compared to traditional regularization and BSBL approaches.