Improvements and artifact analysis in conductivity images using multiple internal electrodes.
Electrical impedance tomography is an attractive functional imaging method. It is currently limited in resolution and sensitivity due to the complexity of the inverse problem and the safety limits of introducing current. Recently, internal electrodes have been proposed for some clinical situations such as intensive care or RF ablation. This paper addresses the research question related to the benefit of one or more internal electrodes usage since these are invasive. Internal electrodes would be able to reduce the effect of insulating boundaries such as fat and bone and provide improved internal sensitivity. We found there was a measurable benefit with increased numbers of internal electrodes in saline tanks of a cylindrical and complex shape with up to two insulating boundary gel layers modeling fat and muscle. The internal electrodes provide increased sensitivity to internal changes, thereby increasing the amplitude response and improving resolution. However, they also present an additional challenge of increasing sensitivity to position and modeling errors. In comparison with previous work that used point sources for the internal electrodes, we found that it is important to use a detailed mesh of the internal electrodes with these voxels assigned to the conductivity of the internal electrode and its associated holder. A study of different internal electrode materials found that it is optimal to use a conductivity similar to the background. In the tank with a complex shape, the additional internal electrodes provided more robustness in a ventilation model of the lungs via air filled balloons.
Impedance Imaging Research Center and Department of Biomedical Engineering, Kyung Hee University, Korea.
Adnan
A
Farooq
Joubin Nasehi
JN
Tehrani
Alistair Lee
AL
McEwan
Eung Je
EJ
Woo
Tong In
TI
Oh
D000818:Animals; D016477:Artifacts; D003198:Computer Simulation; D004553:Electric Conductivity; D004566:Electrodes; D007091:Image Processing, Computer-Assisted; D023421:Models, Animal; D008962:Models, Theoretical; D012123:Pulmonary Ventilation / Q000502:physiology; D034421:Sus scrofa