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

Dandan Yan,Wenlong Xu,Jing Li

doi:10.4236/jbise.2010.38103

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.

Highlights

Knowledge of the electrical conductivity distribution in human body is important to many biomedical applications [1]
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
Given the parameters assumed above, the inverse problem was solved by the Radius Basic Function (RBF)-MREIT to search for the optimum conductivity values

Summary

Introduction

Knowledge of the electrical conductivity distribution in human body is important to many biomedical applications [1]. In the electroencephalography (EEG) or magnetoencephalography (MEG) based source localization or imaging, the conductivity distribution is often assumed to be isotropic and piece-wise homogeneous. This assumption is not entirely accurate since the conductivity is highly anisotropic within the WM [1]. Some methods have been reported to get the anisotropic WM conductivity [2,3], which are based on Basser’s [4] theory to infer the electrical conductivity tensor from the water self-diffusion tensor measured by diffusion tensor magnetic resonance imaging (DT-MRI). Wang [7] proposed a new algorithm to derive the anisotropic conductivity of the cerebral WM from the diffusion tensor magnetic resonance imaging data

Methods

Results

Discussion

Conclusion