Estimation of electrical conductivity distribution within the human head from magnetic flux density measurement

Abstract

We have developed a new algorithm for magnetic resonance electrical impedance tomography (MREIT), which uses only one component of the magnetic flux density to reconstruct the electrical conductivity distribution within the body. The radial basis function (RBF) network and simplex method are used in the present approach to estimate the conductivity distribution by minimizing the errors between the 'measured' and model-predicted magnetic flux densities. Computer simulations were conducted in a realistic-geometry head model to test the feasibility of the proposed approach. Single-variable and three-variable simulations were performed to estimate the brain-skull conductivity ratio and the conductivity values of the brain, skull and scalp layers. When SNR = 15 for magnetic flux density measurements with the target skull-to-brain conductivity ratio being 1/15, the relative error (RE) between the target and estimated conductivity was 0.0737 +/- 0.0746 in the single-variable simulations. In the three-variable simulations, the RE was 0.1676 +/- 0.0317. Effects of electrode position uncertainty were also assessed by computer simulations. The present promising results suggest the feasibility of estimating important conductivity values within the head from noninvasive magnetic flux density measurements.

Anisotropic WM conductivity reconstruction based on diffusion tensor magnetic resonance imaging: a simulation study

“…The conductivity tensor map derived from the diffusion tensor image provided the anisotropic conductivity values for each element. The RBF-MREIT [20] algorithm was used to perform the computer simulations on a realistic head model. In the algorithm realization, the current density data and magnetic flux density data were used to estimate the anisotropic conductivity, respectively.…”

“…In order to avoid the singularity occurring when r = r ' , B(r) is treated as a node variable and J(r ' ) is used at the centre of each finite element in Eq.3. The comparison between the analytical solution and the numerical solution by FEM method was performed [20,21] to indicate the feasibility of solving the forward problem using the FEM.…”

“…5mA bipolarity square-wave electric currents of 20Hz frequency were injected into the head along the equator of the head model as shown in Figure 3, which allowed more signals flowing into the inner brain [20]. 7 electrode pair positions were chosen to inject the current in Copyright © 2010 SciRes.…”

“…The conductivity distribution image can be reconstructed based on the relationship between the conductivity and the measured magnetic flux density combined with the current density. MREIT reconstruction algorithms fall into two categories [10]: those utilizing internal current density [10][11][12][13][14] and those making use of measured magnetic flux density [15][16][17][18][19][20][21][22]. Considering the rotation problem of the object in the MRI system, the latter has the advantage of avoiding the object rotation over the former.…”

“…For the head tissue conductivity, the relatively novel and concise Radial Basis Function (RBF) and Response Surface Methodology (RSM) MREIT algorithms [20,21] have been proposed to focus on the piece-wise homogeneous head tissue conductivity reconstruction. Therefore, RBF-MREIT approach was extended to realize the estimation of anisotropic WM conductivity distribution of the head tissues in this paper.…”