A single diffusion MRI streamline fiber tracking dataset may contain hundreds of thousands, and often millions of streamlines and can take up to several gigabytes of memory. This amount of data is not only heavy to compute, but also difficult to visualize and hard to store on disk (especially when dealing with a collection of brains). These problems call for a fiber-specific compression format ...

In this study, we aimed to identify major fiber pathways and their spatiotemporal relationships within transient fetal zones in the human fetal brain by comparing postmortem high-angular resolution diffusion MR imaging (HARDI) in combination with deterministic streamline tractography and histology. Diffusion weighted imaging was performed on postmortem human fetal brains [N = 9, age = 18-34 pos...

Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring bra...

Diffusion tensor imaging (DTI) is a recent magnetic resonance (MR) technique that provides comprehensive information about water diffusivity. Since diffusivity correlates with tissue structure at the microscopic level DTI reveals fibrous structures such as white matter (WM) pathways in the field of neurosurgery. WM tracts are thereby of major interest and the ability to distinguish between diff...

A novel tensor interpolation method is introduced that allows Diffusion Tensor Imaging (DTI) streamlining to overcome low-anisotropy regions and permits branching of trajectories using information gathered from the neighbourhood of low-anisotropy voxels met during the tracking. The interpolation method is performed in Log-Euclidean space and collects directional information in a spherical neigh...