Towards a Combination of Low Rank and Sparsity in EIT Imaging

Abstract

Electrical impedance tomography (EIT) calculates the internal conductivity distribution of a body using electrical contact measurement and has become increasingly attractive in the biomedical field. However, the design of optimal tomography image reconstruction algorithms has not achieved an adequate level of progress and maturity. The spatial-temporal properties are crucial for the improvement of reconstruction quality and efficiency in dynamic EIT reconstruction. However, these properties have not been fully utilized in previous research. In this paper, a mathematical model for EIT reconstruction is built upon a combination of the low-rank and the sparsity theories. In addition to the low-rank method based on the nuclear norm constraint, the patch-based sparse method is also used to obtain the spatial features of a reconstructed image, according to the characteristic of an irregular boundary for the EIT image. The mathematical model of the new method is solved using the variable split (VS) algorithm. The imaging results are compared with the reconstruction results of the traditional algorithms. The experimental results demonstrate better performance of the new method compared with the traditional methods. The effectiveness of the proposed scheme is verified.