Diffusion tensor imaging has shown potential in providing information about the location of white matter tracts within the human brain. Based on this data, a novel approach is presented establishing connectivity between functional regions using pathfinding. The probability distribution function of the local tensor thereby controls the state space search performed by pathfinding. Additionally, it serves as an indicator for the reliability of the computed paths visualized by color encoding. Besides the capability to handle noisy data, the probabilistic nature of the approach is also able to cope with crossing or branching fibers. The algorithm thus guarantees to establish a connection between cortical regions and on the same hand provides information about the probability of the obtained connection. This approach is especially useful for investigating the connectivity between certain centers of the brain as demonstrated by reconstructed connections between motor and sensory speech areas.
Visualizing diffusion tensor imaging data has recently gained increasing importance. The data is of particular interest for neurosurgeons since it allows analyzing the location and topology of
major white matter tracts such as the pyramidal tract. Various approaches such as fractional anisotropy, fiber tracking and glyphs
have been introduced but many of them suffer from ambiguous representations of important tract systems and the related anatomy. Furthermore, there is no information about the reliability of the presented visualization. However, this information is essential for neurosurgery. This work proposes a new approach of glyph visualization accelerated with consumer graphics hardware showing a
maximum of information contained in the data. Especially, the probability of major white matter tracts can be assessed from the
shape and the color of the glyphs. Integrating direct volume rendering of the underlying anatomy based on 3D texture mapping and a special hardware accelerated clipping strategy allows more comprehensive evaluation of important tract systems in the vicinity of a tumor and provides further valuable insights. Focusing on hardware acceleration wherever possible ensures high image quality and interactivity, which is essential for clinical application. Overall, the presented approach makes diagnosis and therapy planning based on diffusion tensor data more comprehensive and allows better assessment of major white matter tracts.