A sub-domain based regularization method with prior information for human thorax imaging using electrical impedance tomography

Abstract

Electrical impedance tomography (EIT) is a non-invasive imaging technique that can be used as a bed-side monitoring tool for human thorax imaging. EIT has high temporal resolution characteristics but at the same time it suffers from poor spatial resolution due to ill-posedness of the inverse problem. Often regularization methods are used as a penalty term in the cost function to stabilize the sudden changes in resistivity. In human thorax monitoring, with conventional regularization methods employing Tikhonov type regularization, the reconstructed image is smoothed between the heart and the lungs, that is, it makes it difficult to distinguish the exact boundaries of the lungs and the heart. Sometimes, obtaining structural information of the object prior to this can be incorporated into the regularization method to improve the spatial resolution along with helping create clear and distinct boundaries between the objects. However, the boundary of the heart is changed rapidly due to the cardiac cycle hence there is no information concerning the exact boundary of the heart. Therefore, to improve the spatial resolution for human thorax monitoring during the cardiac cycle, in this paper, a sub-domain based regularization method is proposed assuming the lungs and part of background region is known. In the proposed method, the regularization matrix is modified anisotropically to include sub-domains as prior information, and the regularization parameter is assigned with different weights to each sub-domain. Numerical simulations and phantom experiments for 2D human thorax monitoring are performed to evaluate the performance of the proposed regularization method. The results show a better reconstruction performance with the proposed regularization method.