Visualization of diffusion tensor data using evenly spaced streamlines

Abstract

Diffusion tensor imaging allows investigating white matter structures in vivo which is of particular interest for neurosurgery. A promising approach for the reconstruction of neural pathways are streamline based techniques commonly referred to as fiber tracking. However, due to the diverging nature of tract systems, the density of streamlines varies over the domain without control resulting in sparse areas as well as cramped regions. To overcome this problem, we adapted the concept of evenly spaced streamlines to fiber tracking providing streamlines equally distributed over the domain. Additionally, we incorporated evenly spaced streamlines into region of interest based tracking. We also investigated an adaptive control of the distance between separate streamlines depending on the magnitude of anisotropic diffusion which provides a mechanism to emphasize dominant tract systems.