Development of algorithms to image impedance changes inside the human head.

Bayford, Richard ORCID: https://orcid.org/0000-0001-8863-6385, Bagshaw, Andrew P., Liston, Adam D., Tizzard, Andrew ORCID: https://orcid.org/0000-0002-6159-4901 and Holder, David S. (2002) Development of algorithms to image impedance changes inside the human head. In: The First Mummy Range Workshop on Electric Impedance Imaging, Colorado, US. . [Conference or Workshop Item]

Abstract

It is assumed that inclusion of head geometry in the forward model will improve the localisation accuracy of image reconstruction algorithms for imaging impedance changes inside the human head. Initially we developed an algorithm, which assumed that the human head could be modelled as a simple homogenous sphere or multiple shelled spheres. This is an assumption often used in localising dipoles in the human head from measurement of surface potentials (Electroencephalogram EEG). This did not always yield a solution with correct localisation.
To test the idea that including geometric information will improve localisation we have developed a new difference reconstruction algorithm for Electrical Impedanc Tomography (EIT) that incorporates a forward model derived from a Finite Element model of the human head.
The new algorithm uses a sensitivity approach. The linear sensitivity matrix is created from a geometrically accurate model of the human head obtain from segmented MRI data. Generating meshes of the human head is difficult due to the complex geometry and multiple shells, i.e. scalp, skull, CSF and brain. This can produce distorted elements, which impact of the quality on the reconstructed image. Also the number of elements in scalp and CSF are limited reducing the accuracy of the final solution. We examine the effect of the mesh quality on the reconstructed image.
The inverse solution was obtained by normalising the sensitivity matrix then inverting it using truncated Singular Value Decomposition (SVD). We present the results of this new image reconstruction algorithm and compare
its image quality and location accuracy against an algorithm which models the head as four concentric spheres.

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