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.

In electrical impedance tomography (EIT), it is important to acquire reliable measurement data through EIT system for achieving good reconstructed image. In order to have reliable data, various methods for checking and optimizing the EIT measurement system have been studied. However, most of the methods involve additional cost for testing and the measurement setup is often evaluated before the experiment. It is useful to have a method which can detect the faulty electrode data during the experiment without any additional cost. This paper presents a method based on random sample consensus (RANSAC) to find the incorrect data on fault electrode in EIT data. RANSAC is a curve fitting method that removes the outlier data from measurement data. RANSAC method is applied with Gauss Newton (GN) method for image reconstruction of human thorax with faulty data. Numerical and phantom experiments are performed and the reconstruction performance of the proposed RANSAC method with GN is compared with conventional GN method. From the results, it can be noticed that RANSAC with GN has better reconstruction performance than conventional GN method with faulty electrode data.

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