Illustrative uncertainty visualization for DTI fiber pathways

Diffusion Tensor Imaging (DTI) and fiber tracking provide unique insight into the 3D structure of fibrous tissues in the brain. However, the output of fiber tracking contains a sig- nificant amount of uncertainty accumulated in the various steps of the processing pipeline. Existing DTI visualization methods do not present these uncertainties to the end user. This creates an impression of certainty that can be mislead- ing and even dangerous in applications such as neurosurgery which rely heavily on risk assessment and decision-making. However, adding uncertainty to an already complex visual- ization can easily lead to cognitive overload. In this work we propose illustrative confidence intervals to reduce the com- plexity of the visualization and present only those aspects of uncertainty that are of interest to the user. We look specifi- cally at the uncertainty in fiber shape due to noise and mod- eling errors. Any method that produces a set of streamlines with associated confidence values can be visualized with our framework.