Connectivity-aware sectional visualization of DTI volumes

We present three methods for visualizing cross-sections of diffusion tensor magnetic resonance imaging (DTI) volumes. The goal of our methods is to effectively bring 3D out-of-plane connectivity information into the cross-section plane, as inspecting 2D cross-sections is a common practice for scientific data exploration. In order to derive the connectivity information associated with a crosssection, we first sample points (seeds) on a regular grid on the cross-section and then, from each point, generate integral curves following the principal eigenvector of the underlying diffusion tensor field in both directions. We quantify how these curves relate to each other by computing an anatomically motivated pairwise distance measure between them and assemble the measures into a distance matrix. All of the three methods visualize these 3D distance relations on the cross-section plane. Our first method (Bohemian coloring) uses color changes proportional to the distances in the L*a*b* color space; our second method (icon layering) uses both color changes and icons of varying size and layout; our third visualization uses line segments rendered with different thicknesses and shades (edge rendering). All of our methods provide a way to visually segment 2D slices of DTI data with respect to the integral curves crossing the slice plane. We demonstrate our methods in visualizing the mid-sagittal cross-section of the corpus callosum in the brain. Experts report that our methods may facilitate demarcation of subtler anatomical-functional divisions of the corpus callosum and can be useful in quick diagnosis of subtle but critical changes in the number and shape of the neuro-fibers due to injuries. Also, a particular contribution of the current work is to introduce a continuous 2D color mapping technique based on a new geometric model, flat-torus, providing approximate perceptual uniformity and can be repeated an arbitrary number of times in both directions to increase sensitivity.