Title :
Impedance Imaging With First-Order TV Regularization
Author :
Yoon Mo Jung ; Sangwoon Yun
Author_Institution :
Dept. of Comput. Sci. & Eng., Yonsei Univ., Seoul, South Korea
Abstract :
EIT problem is a typical inverse problem with serious ill-posedness. In general, regularization techniques are necessary for such ill-posed inverse problems. To overcome ill-posedness, the total variation (TV) regularization is widely used and it is also successfully applied to EIT. For realtime monitoring, a fast and robust image reconstruction algorithm is required. By exploiting recent advances in optimization, we propose a first-order TV algorithm for EIT, which simply consists of matrix-vector multiplications and in which the sparse structure of the system can be easily exploited. Furthermore, a typical smoothing parameter to overcome nondifferentibility of the TV term is not needed and a closed form solution can be applied in part using soft thresholding. It shows a fast reconstruction in the beginning. Numerical experiments using simulated data and real experimental data support our claim.
Keywords :
electric impedance imaging; image reconstruction; inverse problems; medical image processing; patient monitoring; smoothing methods; two-dimensional electron gas; first-order total variation regularization; ill-posed inverse problems; image reconstruction algorithm; impedance imaging; matrix-vector multiplications; realtime monitoring; serious ill-posedness; smoothing parameter; soft thresholding; sparse structure; Conductivity; Electrodes; Image reconstruction; Noise; TV; Tomography; Alternating direction method of multipliers (ADMM); electrical impedance tomography (EIT); regularization, total variation (TV);
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2014.2351014