Study On Image Reconstruction Algorithm For Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a functional imaging technique, which can reveal physiological and pathological information from human body's impedance properties. The advantages such as the non-invasive modality make EIT a hot topic in the research of medical imaging. Image reconstruction in EIT is a difficult problem because of the ill-posedness and non-linearity. This dissertation mainly studies EIT image reconstruction algorithms with the aim of increasing precision and spatial resolution of EIT. An adaptive mesh refinement method based on resistivity gradients in EIT reconstruction is proposed, and its advantage over the uniformly refined mesh analyzed. From various points of view, several reconstruction methods are proposed. Their performances are checked through theoretical analysis, computer simulation and experiments. Finally preliminary studies on imaging complex conductivity distribution and reconstruction of electrical impedance tomography based on independent component analysis are carried out.Major accomplishments of the dissertation include:1. In order to improve the solution to the forward problem in EIT based on FEM, an adaptive mesh refinement technique is introduced. Once a region of impedance change in a coarse mesh is located based on the calculated gradient, the local mesh is refined so as to achieve higher precision. This method has a major impact on obtaining an efficient solution to the forward problem, leading to the improvement in resolution quality and saving of memory space.2. As the regularization operator in the generalized Tikhonov approach lacks characteristics of both the object and the EIT reconstructed image, performance of the modified Newton-Raphson (MNR) algorithm is unsatisfactory in practice. In this dissertation, a reconstruction algorithm is proposed based on minimization of the augmented cost functional including the soft constraint. The algorithm is formulated under the exponentially weighted least square criteria. By reducing the condition number of a Hessian matrix, the ill-posedness is reduced. Experimental results indicate that the method given here is better than the traditional method in terms of EIT precision.3. The Newton-Raphson reconstruction algorithm requires second derivative since a fine mesh needs to be calculated and regularized by iteration. The algorithm tends to be lengthy, therefore unstable even divergent. An image reconstruction method for EIT based on the non-linear conjugate gradients iteration algorithm (NLCG) improved by adding a modified factor is proposed. The algorithm is computationally efficient. Stability of the EIT solution is improved because NLCG...