Paper Info
Title
Use of anisotropic modelling in electrical impedance tomography: description of method and preliminary assessment of utility in imaging brain function in the adult human head.
Abstract
Electrical Impedance Tomography (EIT) is an imaging method which enables a volume conductivity map of a subject to be produced from multiple impedance measurements. It has the potential to become a portable non-invasive imaging technique of particular use in imaging brain function. Accurate numerical forward models may be used to improve image reconstruction but, until now, have employed an assumption of isotropic tissue conductivity. This may be expected to introduce inaccuracy, as body tissues, especially those such as white matter and the skull in head imaging, are highly anisotropic. The purpose of this study was, for the first time, to develop a method for incorporating anisotropy in a forward numerical model for EIT of the head and assess the resulting improvement in image quality in the case of linear reconstruction of one example of the human head. A realistic Finite Element Model (FEM) of an adult human head with segments for the scalp, skull, CSF, and brain was produced from a structural MRI. Anisotropy of the brain was estimated from a diffusion tensor-MRI of the same subject and anisotropy of the skull was approximated from the structural information. A method for incorporation of anisotropy in the forward model and its use in image reconstruction was produced. The improvement in reconstructed image quality was assessed in computer simulation by producing forward data, and then linear reconstruction using a sensitivity matrix approach. The mean boundary data difference between anisotropic and isotropic forward models for a reference conductivity was 50%. Use of the correct anisotropic FEM in image reconstruction, as opposed to an isotropic one, corrected an error of 24 mm in imaging a 10% conductivity decrease located in the hippocampus, improved localisation for conductivity changes deep in the brain and due to epilepsy by 4–17 mm, and, overall, led to a substantial improvement on image quality. This suggests that incorporation of anisotropy in numerical models used for image reconstruction is likely to improve EIT image quality.
Year
DOI
Venue
2008
10.1016/j.neuroimage.2008.07.023
NeuroImage
Keywords
Field
DocType
EIT,Anisotropy,Brain function
Iterative reconstruction,Isotropy,Anisotropy,Cognitive psychology,Image quality,Psychology,Finite element method,Artificial intelligence,Acoustics,Optical tomography,Human head,Electrical impedance tomography
Journal
Volume
Issue
ISSN
43
2
1053-8119
Citations 
PageRank 
References 
11
0.90
11
Authors
9
Name
Order
Citations
PageRank
Juan-Felipe P J Abascal1123.00
Simon R Arridge253274.17
David Atkinson39512.19
Raya Horesh4273.53
Lorenzo Fabrizi5131.73
Marzia De Lucia6495.55
Lior Horesh7226.04
Richard H. Bayford87412.81
David S. Holder9769.83