The effect of layers in imaging brain function using electrical impedance tomograghy.
Electrical impedance tomography (EIT) has promise for imaging brain function with rings of scalp electrodes, but hitherto human images have been collected and reconstructed using a simple algorithm in which the head was modelled as a homogeneous sphere. The purpose of this work was to assess the improvement in image quality which could be achieved by adding layers to represent the cerebro-spinal fluid (CSF), skull and scalp in the forward model employed by the reconstruction algorithm. Solutions to the forward model were produced analytically and using the linear finite element method (FEM). This was undertaken for computer simulated data when a spherical conductivity change of 10%, radius 5 mm, was moved through 29 positions within a head modelled as four concentric spheres of radius 80-92 mm in order to verify the accuracy of the linear FEM by comparison with the analytical method. Test data were also recorded in a 93.5 mm, spherical, saline-filled tank in which the skull was simulated by a hollow sphere of plaster of Paris, 5 mm thick and a 20 x 20 mm right-cylindrical Perspex object, a 100% conductivity decrease, was moved through 39 positions. The best images were achieved by reconstruction with a four- or three-shell analytical model, giving a spatial accuracy of 5.8 +/- 2.2 mm for computer simulated or 14.0 +/- 5.8 mm for tank data. Mean FWHM was 57 mm and 91 mm in the XY-plane and along the z-axis, respectively. Reconstruction with a homogeneous analytical model gave localization errors greater by about 50-300%, but a reduction in FWHM of about 5% of the image diameter. Unexpectedly, reconstruction with FEM models gave poorer results similar to the analytical homogeneous case. This confirms that addition of shells to the forward model improves image quality as expected with an analytical model for reconstruction, but that the FEM method employed, which used a medium mesh and a linear element computation, requires improvement in order to yield the expected benefits.
Middlesex University, Archway Campus, Furnival Building, Highgate, London N19 3UA, UK.
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Liston
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Bayford
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Holder
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D017097:Electric Impedance; D004566:Electrodes; D006801:Humans; D008954:Models, Biological; D012535:Scalp; D012886:Skull; D014054:Tomography / Q000295:instrumentation* / Q000379:methods*