Fused DTI/HARDI Visualization

Abstract

High-angular resolution diffusion imaging (HARDI) is a diffusion weighted MRI technique that overcomes some of the decisive limitations of its predecessor, diffusion tensor imaging (DTI), in the areas of composite nerve fiber structure. Despite its advantages, HARDI raises several issues: complex modeling of the data, nonintuitive and computationally demanding visualization, inability to interactively explore and transform the data, etc. To overcome these drawbacks, we present a novel, multifield visualization framework that adopts the benefits of both DTI and HARDI. By applying a classification scheme based on HARDI anisotropy measures, the most suitable model per imaging voxel is automatically chosen. This classification allows simplification of the data in areas with single fiber bundle coherence. To accomplish fast and interactive visualization for both HARDI and DTI modalities, we exploit the capabilities of modern GPUs for glyph rendering and adopt DTI fiber tracking in suitable regions. The resulting framework, allows user-friendly data exploration of fused HARDI and DTI data. Many incorporated features such as sharpening, normalization, maxima enhancement and different types of color coding of the HARDI glyphs, simplify the data and enhance its features. We provide a qualitative user evaluation that shows the potentials of our visualization tools in several HARDI applications.

The influence of construction methodology on structural brain network measures: A review

“…The well-known white matter bundles, like the superior longitudinal fasciculus, the fasciculus connecting the thalamus and cortex, are required to accord with the prior anatomical knowledge, and the spurious streamlines should be controlled in a tolerant range (Bastiani et al, 2012). During the visual inspection, powerful visualization tools Prčkovska et al, 2011;Sepasian et al, 2012), which can combine tractography results, the underlying glyphs for both DTI and HARDI, many functional eigenvector relations, anatomical region and planes, will undoubtedly accelerate the process and improve the experiences.…”

Multi-Fiber Tractography Visualizations for Diffusion MRI Data

“…With the development of advanced interactive and exploratory tools for visualizing ODF glyphs, however, it has become possible to visualize the full ODFs efficiently in large quantities, e.g., for an entire slice [26,51]. Recent work has focused on representing the ODF glyphs at discrete points along a tract [25,27], whereas the multi-fiber hyperstreamlines proposed in this work provide a continuous and, hence, a more intuitive representation of the data. To further emphasize this difference, an example of the discrete and continuous way to visualize a fiber trajectory is given in Figure 10 using both DTI-based and multi-fiber reconstructions.…”

“…Visualization of the ODF is already possible on a local scale as voxel-wise glyph representations, which can be of great use to visualize the regional architectural complexity in known areas of crossing fibers [24]. However, as these local representations are only available for display at discrete positions [25-27], they are suboptimal to convey the anatomical continuity of fiber bundles. To date, and to the best of our knowledge, there are no fiber tractography visualization strategies that are specifically designed to address the interwoven and complex geometry of the WM fiber network [21,28,29].…”

Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation

“…On the low-level DWI data, in particular Diffusion Tensor Imaging (DTI), tensor glyphs in different shapes were invented to display the rich data dimensionality. For example, the latest design by Prčkovska et al [34] augmented spherical polar ploy glyphs [21] with optimized shape and color scheme. Their design can highlight multiple maxima in order to make the “peaks” of glyphs more distinct in the visualization.…”