3D In-Context Visualisation for Probabilistic Tractography

In medical visualisation, the multi-modal display of probabilistic tractograms in anatomical context is a challenging problem. Probabilistic tractograms are scalar fields indicating the probability of connections in the brain; the anatomical context is supplied in the form of structural MRI data. The simultaneous representation of tracts and context leads to difficulties concerning the clear visibility of the respective data. In this work, I present novel approaches aimed at providing a very good depiction of the nature of the tractogram – i.e. its depth, shape, and value distribution – and supplementing it with a meaningful but unobtrusive context. The presented methods improve existing techniques, as, opposed to other volumetric approaches, the nature of the probabilistic tractogram becomes clear in a static image, and the context allows to perceive the cortex structure at the same time as the visualised data inside.

Master’s Thesis. University of Kaiserslautern.