Track Filtering via Iterative Correction of TDI Topology

We propose a new technique to clean outlier tracks from fiber bundles reconstructed by tractography. Previous techniques were mainly based on computing pair-wise distances and clustering methods to identify unwanted tracks, which relied heavy upon user inputs for parameter tuning. In this work, we propose the use of topological information in track density images (TDI) to achieve a more robust filtering of tracks. There are two main steps of our iterative algorithm. Given a fiber bundle, we first convert it to a TDI, then extract and score its critical points. After that, tracks that contribute to high scoring loops are identified and removed using the Reeb graph of the level set surface of the TDI. Our approach is geometrically intuitive and relies only on a single parameter that enables the user to decide on the length of insignificant loops. In our experiments, we use our method to reconstruct the optic radiation in human brain using the multi-shell HARDI data from the human connectome project (HCP). We compare our results against spectral filtering and show that our approach can achieve cleaner reconstructions. We also apply our method to 215 HCP subjects to test for asymmetry of the optic radiation and obtain statistically significant results that are consistent with post-mortem studies.