Abstract
Tissue impedance reflects the individual information related to the tissue structure and the function of the tissue. A fixed coefficient regularization algorithm is one of the impedance reconstruction algorithms to improve the ill-conditioning problem of the Newton-Raphson algorithm. However, the determination of a good coefficient needs a lot of experimental data and a long period of computation time. Because a good smoothing coefficient has to be manually chosen from a number of coefficients, and the fixed coefficient regularization algorithm sometimes distorts the information, or fails to obtain any effect. In this paper, a new automatic algorithm with successive determination of variables is introduced. This algorithm automatically calculates the smoothing coefficient from the average value of the ill-conditioned matrix. We have evaluated the performance of this average variable algorithm through our impedance imaging system and two phantoms. It shows that the average variable algorithm yields efficient superior images, within the short reconstruction time, and is more practical in application; as compared to the fixed coefficient regularization algorithm.
