Hendrik Bekker

Exploration of the Brain’s White Matter Structure through Visual Abstraction and Multi-Scale Local Fiber Tract Contraction

We present a visualization technique for brain fiber tracts from DTI data that provides insight into the structure of white matter through visual abstraction. We achieve this abstraction by analyzing the local similarity of tract segment directions at different scales using a stepwise increase of the search range. Next, locally similar tract segments are moved toward each other in an iterative process, resulting in a local contraction of tracts perpendicular to the local tract direction at a given scale. This not only leads to the abstraction of the global structure of the white matter as represented by the tracts, but also creates volumetric voids. This increase of empty space decreases the mutual occlusion of tracts and, consequently, results in a better understanding of the brains three-dimensional fiber tract structure. Our implementation supports an interactive and continuous transition between the original and the abstracted representations via various scale levels of similarity. We also support the selection of groups of tracts, which are highlighted and rendered with the abstracted visualization as context.

Illustrative Line Styles for Flow Visualization

We present a flexible illustrative line style model for the visualization of streamline data. Our model partitions view-oriented line strips into parallel bands whose basic visual properties can be controlled independently. We thus extend previous line stylization techniques specifically for visualization purposes by allowing the parametrization of these bands based on the local line data attributes. We demonstrate the effectiveness of our model by applying it to 3D flow field datasets.

Visualizing white matter structure of the brain using Dijkstra's algorithm

An undirected weighted graph may be constructed from diffusion weighted magnetic resonance imaging data. Every node represents a voxel and the edge weights between nodes represent the white matter connectivity between neighboring voxels. In this paper we propose and test a new method for calculating trajectories of fiber bundles in the brain by applying Dijkstras shortest path algorithm to the weighted graph. Subsequently, the resulting tree structure is pruned, showing the main white matter structures of the brain. The time consumption of this method is in the order of seconds.