Image Reconstruction of Electrical Impedance Tomography Based on Optical Image-Guided Group Sparsity

The low spatial resolution of Electrical Impedance Tomography (EIT) makes it challenging to conduct quantitative analysis the electrical properties imaging targets in biomedical applications. We this paper propose integrate optical into EIT improve image quality and report a dual-modal reconstruction algorithm based on image-guided group sparsity (IGGS). IGGS receives an RGB microscopic measurements as inputs, extracts structural features conductivity distribution from images fuses information two modalities generate high-quality image. superior performance is verified by numerical simulation real-world experiments. Compared with selected single-modal algorithms, i.e., classical Tikhonov regularization state-of-the-art Structure-Aware Sparse Bayesian Learning Enhanced Adaptive Group Sparsity Total Variation, proposed method presents superiorities terms shape preservation, background noise suppression, differentiation contrasts.