Tractography is a powerful tool to study structural connectivity of the brain. However, its accuracy is known to be limited and tractograms are contaminated with large numbers of false positive streamlines. In this work, we propose a novel tractogram filtering approach. Our method leverages the topographic regularity of connections with which nearby streamline tend to follow similar trajectories. With this observation, we introduce an anisotropic smoothing approach for track orientation density images. These images are back projected onto the streamlines, which provides information about fiber-to-bundle coupling (FICO). Streamlines are then filtered by thresholding their FICO value.
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