Filtering of spurious streamlines via streamline orientation and pathway density

Motivation: Tract extraction from whole-brain tractograms requires either an extensive knowledge of inclusion and exclusion zones or manual efforts to obtain clean tracts. Goal(s): Automated filtering of spurious streamlines can accelerate the tract extraction process. The algorithm should be versatile, while preserving tract shape and minimizing parameter adjustments. Approach: We developed a filtering algorithm based on streamline direction and density along an average trajectory. Our method was compared to four other filtering implementations. Results: Our algorithm is applicable to a wide variety of tracts with a high and low streamline count. It offers efficient filtering and provides a conservative filtering preserving tract morphology. Impact: We introduced an efficient filtering algorithm, removing spurious streamlines while preserving tract morphology across tracts of low and high density with default parameters. Additionally, the computed average trajectory enables the analysis of metrics in multi-fixel models along the tract pathway.