Enhancing Reliability Of Structural Brain Connectivity With Outlier Adjusted Tractogram Filtering

Diffusion-weighted magnetic resonance imaging tractography is used to represent brain structures but it has limited specificity. Tractogram filtering is proposed to fix this by utilizing e.g. microstructural information to find which streamlines are essential in respect to the original measurements. However, filtered results can be biased if the measurements are unreliable due to partial voluming or artifacts e.g. due to subject motion. We propose augmenting filtering methods with outlier information to adjust for such unreliability. We implemented this in the Convex Optimization modelling for Microstructure Informed Tractography (COMMIT) framework to conduct experiments on data from a synthetic fiber phantom and the Human Connectome Project. Our results demonstrate that the newly augmented COMMIT provides more precise estimations of intra-axonal signal fractions than the original algorithm when diffusion-weighted images are affected by artifacts. Furthermore, we argue this approach could be highly beneficial for clinical studies with limited resolution and numerous unreliable measurements.