# Structure-Aware Trail Bundling for Large DTI Datasets
## 2 Related Work
### Diffusion Tensor Imaging and Tractography  
Conventional structural imaging techniques such as T1-, T2- and proton density-weighted imaging generally create high contrast between major tissue groups in the brain, which are: Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). Such structural imaging techniques are generally well suited for the study of tissue macrostructure yet provide little insight into the orientation of white matter fibers. DWI [17; 18] is a variant of conventional Magnetic Resonance Imaging (MRI) based on the tissue water diffusion rate, which is better suited for the study of white matter pathways. Although DWI refers to the contrast of the acquired images, DTI is a specific type of modeling (or abstraction) of the DWI datasets, in which diffusivity of water molecules is represented by tensors [10]. As an in-vivo non-destructive technique that requires no chemical tracers, DTI is presently one of the most promising methods for the study of white matter architecture in living humans. DTI provides quantitative estimates of white matter integrity and orientation by measuring molecular diffusion of water molecules (or Brownian motion). It is based on the phenomenon of diffusion anisotropy in the nerve tissue: water molecules diffuse faster along the neural fiber direction and slower in the fiber-transverse direction. DTI tensor datasets record, essentially, the spectrum of diffusion strengths per diffusion direction at every point in a 3D scan. Such datasets can be visualized using glyph techniques, which render the local major, medium, and minor eigenvalues of the diffusion tensor, scaled by their corresponding eigenvalues, over a dense sampling of the 3D scanned volume [29]. _Tractography_ methods improve in the above by reducing occlusion and also explicitly showing the fiber tract paths by essentially integrating streamlines in the major direction of the DTI eigenvector, seeded from a suitably chosen point set [11; 12]. More elaborate approaches include streamlines defined by tensor deflection [10], bi-tensor modeling [19] and streamsurface tracking [20]. Fiber tracking has several applications, including noninvasive visualization of white matter pathways, segmenting of specific tracts in the brain for image analysis, and relating white matter tract anatomy to brain tumors and lesions in patients who are candidates for neurosurgery [10]. Tractography essentially delivers large sets of (hundreds of thousands of) 3D fiber trails embedded in the 3D scan volume. Visualizing these next is challenging. Conventional line rendering is a simple and efficient baseline rendering technique to show such datasets that has been employed since fiber tracking was first introduced [12; 21]. Yet, such naive rendering of the densely seeded and criss-crossing 3D trails creates too much clutter and occlusion for many tasks to be accomplished effectively. Numerous visualization tools have been proposed to leverage interactivity to explore large DTI datasets, e.g., [22]. However, navigating the large space of parameter settings can be daunting for users. More extensive illumination [23], ambient occlusion [24] and alpha-blending [25] have been applied to line rendering of fiber tracts to further improve their visual exploration. Rendering 3D tubes with Phong shading [26] is based on more detailed geometric modeling than line rendering, and can produce even higher quality visualizations. The methods by [PERSON] et al. [27] and [PERSON] et al. [28] produce similar shading using screenspace techniques and incorporate illustrative rendering techniques [15] as well. Hyperstreamlines [25; 29] are an extension of cylindrical tubes that provide a richer representation of the DTI field. Clustering forms separate class of methods that help visualizing large tract sets. Such methods detect trails that are similar in terms of spatial position, diffusion values, and possibly other attributes (e.g., curvature, length) and group them into clusters.