Randomized iterative spherical -deconvolution informed tractogram filtering

Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing prob-lems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, trac-togram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable for judging the compliance of individual streamlines with the acquired data since its results depend on the size and composition of the sur-rounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify stream-lines with very consistent filtering results, which were used as pseudo-ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of complying and non-complying streamlines with the acquired data with an accuracy above 80%.

Automated summary

Tractography is important for brain connectivity studies, but currently faces reliability problems. A method called SIFT has been developed to remove anatomically implausible connections in tractograms, but it is not suitable for individual streamline assessment. To address this, researchers propose applying SIFT to randomly selected tractogram subsets to retrieve multiple assessments and train a classifier for distinguishing compliant and non-compliant streamlines, achieving over 80% accuracy.