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Abstract #0703

Consensus tractography: decreasing algorithm dependency to improve fiber reconstructions

Ilaria Gabusi1, Giulia Biolo1, Matteo Battocchio1, and Alessandro Daducci1
1Diffusion and Connectivity Estimation (DICE) lab, Department of Computer Science, University of Verona, Verona, Italy

Synopsis

Keywords: Tractography, Tractography & Fibre Modelling

Motivation: Diffusion MRI tractography is a powerful tool for analyzing non-invasively the brain white matter architecture. However, the reconstructions are highly algorithm- and parameter-dependent, providing only a partial view of the actual connectivity.

Goal(s): To address this limitation, we developed the consensus tractography with the aim of obtaining more anatomically accurate reconstructions.

Approach: The proposed method combines multiple tractograms and employs an advanced microstructure-informed tractography technique to control false positives and redundancy among streamlines.

Results: Tests on synthetic data proved that consensus tractography consistently outperforms individual reconstructions, while the analysis of ten subjects showed its ability to better represent the underlying anatomical structure.

Impact: We addressed the well-known algorithm dependency of tractography reconstructions and proposed a consensus-based strategy. Our solution provides clearer and more accurate white matter mapping, showing significant potential to facilitate precise findings and better support clinical applications.

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