GREINER G.: Visualization of diffusion tensor data using evenly spaced streamlines

Diffusion tensor imaging allows investigating white matter structures in vivo which is of particular in-terest for neurosurgery. A promising approach for the reconstruction of neural pathways are stream-line based techniques commonly referred to as ber tracking. However, due to the diverging nature of tract systems, the density of streamlines varies over the domain without control resulting in sparse ar-eas as well as cramped regions. To overcome this problem, we adapted the concept of evenly spaced streamlines to ber tracking providing streamlines equally distributed over the domain. Additionally, we incorporated evenly spaced streamlines into re-gion of interest based tracking. We also investi-gated 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.