Andre Reichenbach’s research while affiliated with Leipzig University and other places

Fiber clustering algorithms are employed to find patterns in the structural connections of the human brain as traced by tractography algorithms. Current clustering algorithms often require the calculation of large similarity matrices and thus do not scale well for datasets beyond 100,000 streamlines. We extended and adapted the 2D vector field k– means algorithm of Ferreira et al. to find bundles in 3D tractography data from diffusion MRI (dMRI) data. The resulting algorithm is linear in the number of line segments in the fiber data and can cluster large datasets without the use of random sampling or complex multipass procedures. It copes with interrupted streamlines and allows multisubject comparisons.

Diffusion weighted magnetic resonance imaging (dMRI) together with tractography algorithms allow to probe for principal white matter tracts in the living human brain. Specifically, probabilistic tractography quantifies the existence of physical connections to a given seed region as a 3D scalar map of confidence scores. Fiber-Stippling is a visualization for probabilistic tracts that effectively communicates the diffusion pattern, connectivity score, and anatomical context. Unfortunately, it cannot handle multiple diffusion orientations per voxel, which exist in high angular resolution diffusion imaging (HARDI) data. Such data is needed to resolve tracts in complex configurations, such as crossings. In this work, we suggest a visualization based on Fiber-Stippling but sensible to multiple diffusion orientations from HARDI-based diffusion models. With such a technique, it is now possible to visualize probabilistic tracts from HARDI-based tractography algorithms. This implies that tract crossings may now be visualized as crossing stipples, which is an essential step towards an accurate visualization of the neuroanatomy, as crossing tracts are widespread phenomena in the brain.

The human cortex is organized in spatially distinct regions of different functional units. Cortex parcellations based on magnetic resonance imaging (MRI) of living human subjects are common practice, and recently, structural connectivity from diffusion weighted resonance imaging (dwMRI) have been successfully applied to generate such parcellations. The exploration of structural connectivity data together with cortex parcellations has proven to be challenging due to overlapping tracts and structures, limited depth perception, and the large number of tracts, which clutter the visualization. However, the involvement of structural connectivity forces such visualizations to act in anatomical space. While structural connectivity can be communicated using three-dimensional or slicebased visualizations, cortex parcellations are visualized on three-dimensional surfaces. In this work, we solve this problem by proposing an interactive illustrative 3D visualization for both structural connectivity data and cortex parcellations in anatomical space. We achieve this by providing an abstract visualization of the structural connectivity while still being able to provide the full detail on demand. Our visualization furthermore employs interactivity and illustrative depth-enhancing, which are supported by anatomical context and textual annotations and thus help the user to build a mental map of the connections in the brain. Functional and effective connectivity might benefit from such a combined visualization as they use cortex parcellations as well.

Diffusion MRI tractography has evolved into a widely used, important tool within neurosciences, providing the foundation for in-vivo fiber anatomy and hence for mapping of structural connectivity in the human brain. This renders it crucially important to understand the influence of the various MRI imaging artifacts on the tractography results. In this paper, we focus on the thermal noise that is present in all MRI measurements and compare its effect on the output of several established tractography algorithms. We create a reference dataset by denoising with a Non-Local Means filter and evaluate the effect of noise added to the reference on the tractography results with a Monte-Carlo simulation. Our results indicate that among the algorithms tested, the Tensorlines approach is the most robust for tracking white matter fiber bundles and both the Tensorlines and the Bayes DTI approach are good choices for calculating gray matter structural connectivity.

The universe of biochemical reactions in metabolic pathways can be modeled as a complex network structure augmented with domain specific annotations. Based on the functional properties of the involved reactions, metabolic networks are often clustered into so-called pathways inferred from expert knowledge. To support the domain expert in the exploration and analysis process, we follow the well-known Table Lens metaphor with the possibility to select multiple foci. In this paper, we introduce a novel approach to generate an interactive layout of such a metabolic network taking its hierarchical structure into account and present methods for navigation and exploration that preserve the mental map. The layout places the network nodes on a fixed rectilinear grid and routes the edges orthogonally between the node positions. Our approach supports bundled edge routes heuristically minimizing a given cost function based on the number of bends, the number of edge crossings and the density of edges within a bundle.