Boundary image reconstruction based on the nonmonotonic and self-adaptive trust region method for electrical impedance tomography.

The trust region (TR) method is an effective algorithm for image reconstruction of electrical impedance tomography (EIT). Aiming at the drawback of the traditional TR method, an improved method named the nonmonotonic and self-adaptive trust region (NSTR) method is proposed in this paper, in which three kinds of modified techniques are shown that help in improving the computational precision and convergence speed of the algorithm. The comparisons with image reconstruction are carried out between the NSTR, Levenberd-Marquardt (LM) and TR methods. Simulation experiment results indicate that for both the LM and TR methods it is difficult to accurately reconstruct concave and multiple boundaries. However the NSTR method cannot only realize accurate reconstruction, but also provide faster convergence speed. A noise simulation test is carried out and results show that the NSTR method also has strong stability in image reconstruction for EIT. This new method presents a feasible and effective way to research on image reconstruction for EIT.

[1]  J P Morucci,et al.  Bioelectrical impedance techniques in medicine. Part III: Impedance imaging. Third section: medical applications. , 1996, Critical reviews in biomedical engineering.

[2]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[3]  Boying Wu,et al.  Combining nonmonotone conic trust region and line search techniques for unconstrained optimization , 2011, J. Comput. Appl. Math..

[4]  Manuchehr Soleimani,et al.  A trust region subproblem for 3D electrical impedance tomography inverse problem using experimental data , 2009 .

[5]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[6]  K. Y. Kim,et al.  An oppositional biogeography-based optimization technique to reconstruct organ boundaries in the human thorax using electrical impedance tomography , 2011, Physiological measurement.

[7]  A S Vale-Cardoso,et al.  The effect of 50/60 Hz notch filter application on human and rat ECG recordings , 2010, Physiological measurement.

[8]  Andy Adler,et al.  The impact of electrode area, contact impedance and boundary shape on EIT images , 2011, Physiological measurement.

[9]  William R B Lionheart EIT reconstruction algorithms: pitfalls, challenges and recent developments. , 2004, Physiological measurement.

[10]  Shi Zhang,et al.  Electrical impedance tomography based on Tikhonov regularization method improved by level set method , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[11]  Ya-Xiang Yuan,et al.  A trust region-CG algorithm for deblurring problem in atmospheric image reconstruction , 2002 .

[12]  Wu Qing-jun Nonmonotone trust region algorithm for unconstrained optimization problems , 2010 .

[13]  Chang Seop Koh,et al.  Electric resistivity tomography for geophysical inverse problems , 1997 .

[14]  Andrew Binley,et al.  Detecting Leaks from Waste Storage Ponds using Electrical Tomographic Methods , 1999 .

[15]  Yue Xiu-li,et al.  Multifunctional magnetic nanoparticles for magnetic resonance image-guided photothermal therapy for cancer , 2014 .

[16]  R. Patterson,et al.  Non-invasive determination of absolute lung resistivity in adults using electrical impedance tomography , 2010, Physiological measurement.

[17]  Eung Je Woo,et al.  Breast EIT using a new projected image reconstruction method with multi-frequency measurements. , 2012, Physiological measurement.

[18]  N. Holmer,et al.  Electrical Impedance Tomography , 1991 .

[19]  Ya-Xiang Yuan,et al.  On the truncated conjugate gradient method , 2000, Math. Program..