Awada, K.A., Jackson, D.R., Baumann, S.B., Williams, J.T., Wilton, D.R., Fink, P.W. and Prasky, B.R. (1998) Effect of conductivity uncertainties and modeling errors on EEG source localization using a 2-D model. IEEE Transactions on Biomedical Engineering, 45(9), 1135-1145.

has been cited by the following article:

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

  AUTHORS: Dandan Yan, Wenlong Xu, Jing Li

  KEYWORDS: Magnetic Resonance Electrical Impedance Tomography; Radius Basic Function Neural Network; Diffusion Tensor Magnetic Resonance Imaging; Anisotropic Conductivity; WM

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.3 No.8, August 23, 2010

  ABSTRACT: The present study aims to estimate the in vivo anisotropic conductivities of the White Matter (WM) tissues by means of Magnetic Resonance Electrical Impedance Tomography (MREIT) technique. The realistic anisotropic volume conductor model with different conductivity properties (scalp, skull, CSF, gray matter and WM) is constructed based on the Diffusion Tensor Magnetic Resonance Imaging (DT- MRI) from a healthy human subject. The Radius Basic Function (RBF)-MREIT algorithm of using only one magnetic flux density component was applied to evaluate the eigenvalues of the anisotropic WM with target values set according to the DT-MRI data based on the Wolter’s model, which is more physiologically reliable. The numerical simulations study performed on the five-layer realistic human head model showed that the conductivity reconstruction method had higher accuracy and better robustness against noise. The pilot research was used to judge the feasibility, meaningfulness and reliability of the MREIT applied on the electrical impedance tomography of the complicated human head tissues including anisotropic characteristics.