Effects of incompatible boundary information in EIT on the convergence behavior of an iterative algorithm

An optimized strategy for real-time hemorrhage monitoring with electrical impedance tomography

Delayed detection of an internal hemorrhage may result in serious disabilities and possibly death for a patient. Currently, there are no portable medical imaging instruments that are suitable for long-term monitoring of patients at risk of internal hemorrhage. Electrical impedance tomography (EIT) has the potential to monitor patients continuously as a novel functional image modality and instantly detect the occurrence of an internal hemorrhage. However, the low spatial resolution and high sensitivity to noise of this technique have limited its application in clinics. In addition, due to the circular boundary display mode used in current EIT images, it is difficult for clinicians to identify precisely which organ is bleeding using this technique. The aim of this study was to propose an optimized strategy for EIT reconstruction to promote the use of EIT for clinical studies, which mainly includes the use of anatomically accurate boundary shapes, rapid selection of optimal regularization parameters and image fusion of EIT and computed tomography images. The method was evaluated on retroperitoneal and intraperitoneal bleeding piglet data. Both traditional backprojection images and optimized images among different boundary shapes were reconstructed and compared. The experimental results demonstrated that EIT images with precise anatomical information can be reconstructed in which the image resolution and resistance to noise can be improved effectively.

MNR Method with Self-Determined Regularization Parameters for Solving Inverse Resistivity Problem

The modified Newton-Raphson (MNR) method is used to solve the inverse resistivity problem in this paper. Using Tikhonov regularization method, comparisons among the L-curve method, the zero-crossing (ZC) method and the generalized cross validation (GCV) method are carried out for determining the regularization parameters of MNR method. By these criterions the appropriate regularization parameters are self-determined and adjusted with the reconstruction iterations. Our simulation experiments on 2D circle model showed that the GCV method can provide the best reconstruction quality with the fastest speed in inverse resistivity problem using MNR method.

Three EIT approaches for static imaging of head

Three EIT approaches for static imaging of head are investigated in this paper. The modified Newton-Raphson (MNR) method and the differential evolution (DE) algorithm are applied to the impedance reconstruction of 2D section of head based on real head model. Comparisons are carried out on the results obtained using simulated data, and a DE-MNR combination method is proposed, which demonstrated high impedance reconstruction quality with fast convergence in the 2D EIT simulation for static imaging of head.