A computational geometry approach for modeling neuronal fiber pathways
This work addresses the need for more efficient modeling of white matter pathways in brain imaging, particularly for Alzheimer's disease diagnosis, but it appears incremental as it builds on existing tractography methods with a new computational geometry approach.
The authors tackled the problem of time-consuming and intractable tractography analysis for neuronal fiber pathways by developing a computational geometry-based algorithm to simplify connectivity, and they applied it to diffusion MRI data to distinguish Alzheimer's subjects from normal controls.
We propose a novel and efficient algorithm to model high-level topological structures of neuronal fibers. Tractography constructs complex neuronal fibers in three dimensions that exhibit the geometry of white matter pathways in the brain. However, most tractography analysis methods are time consuming and intractable. We develop a computational geometry-based tractography representation that aims to simplify the connectivity of white matter fibers. Given the trajectories of neuronal fiber pathways, we model the evolution of trajectories that encodes geometrically significant events and calculate their point correspondence in the 3D brain space. Trajectory inter-distance is used as a parameter to control the granularity of the model that allows local or global representation of the tractogram. Using diffusion MRI data from Alzheimer's patient study, we extract tractography features from our model for distinguishing the Alzheimer's subject from the normal control. Software implementation of our algorithm is available on GitHub.