Image Reconstruction Based on Structured Sparsity for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a tomographic imaging modality for reconstructing the conductivity distribution through boundary current injection and induced voltage measurement. High-quality image is of great significant for improving the qualitative imaging performance in biomedical application. In this paper, the structured sparsity algorithm is proposed to incorporate with the underlying structure of the conductivity on the basis of the sparse priors. The structured sparsity is integrated into the iterative process of the Symkaczmarz algorithm for EIT image reconstruction. Both simulation and experiment results indicate that the proposed method has feasibility for pulmonary ventilation imaging and great potential for improving the image quality.