# Structure-Aware Trail Bundling for Large DTI Datasets
## 6 External Evaluation
### Evaluation Process  
We invited 5 participants with good prior knowledge of DTI data and associated techniques. Their level of experience ranges from 5 to 37 years. One participant is the head of the neuroscience lab in a major French university (37 years experience); one participant is a neuroscientist (12 years experience); two are postdocs with 5 and 10 years experience; and one is a researcher with 10 years experience. Each evaluation lasted one hour and operated as follows. First, we provided a general introduction of our context and goal. After naming DTI and tractography (which was, as outlined above, well known to the participants), we introduced the problem of data size: The input data for this visualization is a large set (hundreds of thousands) of 3D tracts that follow the main anisotropy directions in a DTI volume. These tracts are in general locally aligned with the directions of the neural fibers. However, directly visualizing a large and densely sampled tract volume creates problems, mainly due to occlusion. We propose to address such problems by a simplified visualization technique that bundles close and same-direction tracts, so that occlusion decreases but the resulting structures still closely reflect the underlying fiber anatomy. We next introduced the six visualization techniques listed in Table 4 and illustrated them by interactively running our visualization tool. The dataset used during this presentation (and also further on in the evaluation) consists of a brain MRI scan coming from a healthy person, exhibiting no particular anatomical or physiological anomalies. As such, users should expect to see the typical structures they are familiar with from brain anatomy. We traced around 200 K fiber tracts in this brain. The fibers are seeded randomly in regions of high anisotropy (white matter) and traced until they exit this region and enter gray matter, as described in Section 3.1). We explained each of the six visualizations in terms of what the visualization actually _shows_, e.g., what bundling does, what is the working of alpha-blending and shading, and how to interpret the DDH image. During this presentation, we refrained from making _quality_ judgments, e.g., state that one visualization can better show certain structures than another one, since that would bias the participants. Following this global introduction, each user was asked to watch a 3-minute-long video that we prepared in advance. The users could not communicate among themselves, nor with the organizers of the evaluation, during this session, so as not to influence each other. The video shows the DTI trail set described above visualized by the six methods in the order listed in Table 4, i.e., going from the arguably most familiar style to the participants (Original) to the least familiar one (Aniso-Tubes). Each of the six methods is offered roughly the same amount of time in the video. Within such a sequence, the visualization is smoothly rotated along the Y axis, then along the X axis, with the same constant-speed motion patterns for all the sequences. Relevant parameters for the presented visualization style are then varied smoothly, so the participants can understand their effects. For example, for the Alpha styles, we increase the opacity value from the preset \(\alpha=0.1\) to reach 1 (full opacity) and then back, so one understands how the alpha-blending works. To understand the bundled styles, the video varies the relaxation parameter \(\gamma\) between the preset value (\(\gamma=0.2\), strongly bundled) to 1 (fully unbundled) and then back. Each sequence is captioned in the video by its name. Additional captions explain the parameters that are varied. Figure 15 shows eight frames from the watched video.