Content area

Abstract

Electrical impedance tomography (EIT) is a recently developed technique which enables the internal conductivity of an object to be imaged using rings of external electrodes. In a recent study, EIT during cortical evoked responses showed encouraging changes in the raw impedance measurements, but reconstructed images were noisy. A simplified reconstruction algorithm was used which modelled the head as a homogeneous sphere. In the current study, the development and validation of an improved reconstruction algorithm are described in which realistic geometry and conductivity distributions have been incorporated using the finite element method. Data from computer simulations and spherical or head-shaped saline-filled tank phantoms, in which the skull was represented by a concentric shell of plaster of Paris or a real human skull, have been reconstructed into images. There were significant improvements in image quality as a result of the incorporation of accurate geometry and extracerebral layers in the reconstruction algorithm. Image quality, assessed by blinded subjective expert observers, also improved significantly when data from the previous evoked response study were reanalysed with the new algorithm. In preliminary images collected during epileptic seizures, the new algorithm generated EIT conductivity changes which were consistent with the electrographic ictal activity. Incorporation of realistic geometry and conductivity into the reconstruction algorithm significantly improves the quality of EIT images and lends encouragement to the belief that EIT may provide a low-cost, portable functional neuroimaging system in the foreseeable future.

Details

Title
Electrical impedance tomography of human brain function using reconstruction algorithms based on the finite element method
Author
Bagshaw, Andrew P; Liston, Adam D; Bayford, Richard H; Tizzard, Andrew; Gibson, Adam P; Tidswell, AThomas; Sparkes, Matthew K; Dehghani, Hamid; Binnie, Colin D; Holder, David S
Pages
752-764
Publication year
2003
Publication date
Oct 1, 2003
Publisher
Elsevier Limited
ISSN
10538119
e-ISSN
10959572
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1506601230
Copyright
Copyright Elsevier Limited Oct 1, 2003