Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering

TL;DR

This paper introduces a novel approach using randomized SIFT filtering and machine learning to improve the plausibility assessment of brain fiber streamlines in tractography, enhancing reliability in brain connectivity studies.

Contribution

It proposes applying SIFT to random subsets of tractograms to generate multiple assessments, enabling training of classifiers to distinguish plausible from implausible streamlines.

Findings

Classifier accuracy exceeds 80% in identifying plausible streamlines.

Randomized SIFT assessments provide robust pseudo ground truths.

The method is freely available on GitHub.

Abstract

Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems 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, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, \textit{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 to judge the plausibility of individual streamlines since its results depend on the size and composition of the surrounding 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 streamlines 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 plausible and implausible streamlines with accuracy above 80%. The software code used in the paper and pretrained weights of the classifier are distributed freely via the Github repository https://github.com/djoerch/randomised_filtering.