# Structure-Aware Trail Bundling for Large DTI Datasets
## 7 Discussion  
We discuss next briefly the main characteristics of, and findings related to, our proposed method.  
**Technical points:** All steps of the presented method are generic, in the sense that they can be applied to any trail set consisting of a set of 3D point-sampled trajectories that is further annotated with anisotropy scalar values. A subset of the steps (3D bundling, shading, outline computation) are actually more generic in the sense that they only require a set of 3D point-sampled trails. The entire method is implemented on the GPU, and has a complexity linear in the number of sample points \(\mathbf{x}_{j}\). Practically, this allows us to bundle-and-render hundreds of thousands of 3D trails at interactive frame rates.  
**Quality:** The quality of the produced visualizations, in terms of ability to recover complex brain structures such as fiber sheets, depends strongly on the availability of a _dense_ trail set that samples the DTI volume. Indeed, a too sparse trail set will not be able to capture these structures, so bundling and shading can do little to improve this. Reseeding (Section 3.2.3) alleviates this by creating additional tracks on-the-fly during the visualization. However, this cannot _guarantee_ a dense sampling of all regions of interest. As such, starting with a rich, densely sampled, trail set is an important condition for quality.  
**Vercacity:** A well-known problem of bundling methods is that they deform the actual data, trading off veracity for simplification [7]. Although this is less critical when bundling abstract data, such as graph drawings, deforming DTI fibers is more problematic, as these can potentially convey false insights to the user. The anisotropic bundling techniques described in Sections 3.2.1 and 3.2.2 limit such problems as they _constrain_ the bundling to follow additional data-related information (anisotropy). Still, the inherent trade-off of bundling between simplification and veracity remains. This is not a problem specific to our method--many other methods that produce simplified visualizations of DTI data share the same issue [14; 16; 60]. Exploring how to both _measure_ and _limit_ deformations caused by bundling that create false insights is an interesting and important direction for future work.  
**Evaluation:** Our evaluation is inherently limited by the format chosen--inviting only professionals deeply familiar with DTI visualization, exposing them to precisely the same material, and setting a time limit of under one hour for the entire process. Inviting more people (with less DTI experience), allowing them to actually experiment live with the visualization tool, and recording more and more task-specific information would, obviously, give more insights into the specific strengths and weaknesses of our method. However, we believe that this type of targeted evaluation can only follow our _formative_ evaluation. Indeed, the latter could have delivered evidence that does not support the overall added-value claim for our visualization, in which case doing a targeted evaluation would not have made sense. The results of our evaluation, however, have singled out Aniso-Tubes and Aniso-Alpha as the top preferred styles for occlusion removal, clarity, and depicting connectivity. Hence, we have evidence that _anisotropic bundling_ (present only in the Aniso style) and _shading_ (present only in the Tubes style) are both of added value. With this information, we plan to execute a subsequent evaluation--using the combination of parameters mentioned above as presets--in which several more precisely defined tasks, with ground-truth information available for measuring the quality of completion, will be addressed using our tool.