A New Method to Derive White Matter Conductivity from Diffusion Tensor MRI - Wang, Kun (2008)
We propose a new algorithm to derive the anisotropic conductivity of the cerebral white matter (WM) from the diffusion tensor magnetic resonance imaging (DT-MRI) data. The transportation processes for both water molecules and electrical charges are described through a common multi-compartment model that consists of axons, glia or cerebrospinal fluid (CSF). The volume fraction (VF) of each compartment varies from voxel to voxel and is estimated from the measured diffusion tensor. The conductivity tensor at each voxel is then computed from the estimated VF values and the decomposed eigenvectors of the diffusion tensor. The proposed VF algorithm was applied to the DT-MRI data acquired from two healthy human subjects. The extracted anisotropic conductivity distribution was compared with those obtained by using two existing algorithms, which were based upon a linear conductivity-to-diffusivity relationship (Lin) and a volume constraint (VC), respectively. The present results suggest that the VF algorithm is capable of incorporating the partial volume effects of the CSF and the intravoxel fiber crossing structure, both of which are not addressed altogether by existing algorithms. Therefore, it holds potential to provide a more accurate estimate of the WM anisotropic conductivity, and may have important applications to neuroscience research or clinical applications in neurology and neurophysiology.