Enhancing tractography filtering by accounting for outliers and partial voluming in diffusion weighted measurements 

The white matter structures of the human brain can be represented via diffusion tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its clinical feasibility. Filtering algorithms have been proposed to reduce these invalid streamlines. We augmented the COMMIT filtering algorithm to adjust for two typical artifacts present in diffusion-weighted images: partial voluming and signal drop-outs due to subject motion. We demonstrate that our robust algorithm is capable to properly filter tractography reconstructions despite these artifacts and could be useful especially for clinical studies with uncooperative patient groups such as neonates.