Item

Abstract

Fiber tracking is a standard tool to estimate the course of major
white matter tracts from diffusion tensor magnetic resonance imaging
(DT-MRI) data. In this work, we aim at supporting the visual
analysis of classical streamlines from fiber tracking by integrating
context from anatomical data, acquired by a $T_1$-weighted MRI
measurement. To this end, we suggest a novel visualization metaphor,
which is based on data-driven deformation of geometry and has been
inspired by a technique for anatomical fiber preparation known as
Klingler dissection. We demonstrate that our method conveys the
relation between streamlines and surrounding anatomical features
more effectively than standard techniques like slice images and
direct volume rendering. The method works automatically, but its
GPU-based implementation allows for additional, intuitive
interaction.