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Structure-Aware Trail Bundling for Large DTI Datasets

Abstract : Creating simplified visualizations of large 3D trail sets with limited occlusion and preservation of the main structures in the data is challenging. We address this challenge for the specific context of 3D fiber trails created by DTI tractography. For this, we propose to jointly simplify trails in both the geometric space (by extending and adapting an existing bundling method to handle 3D trails) and in the image space (by proposing several shading and rendering techniques). Our method can handle 3D datasets of hundreds of thousands of trails at interactive rate, has parameters for the most of which good preset values are given, and produces visualizations that have been found, in a small-scale user study involving five medical professionals, to be better in occlusion reduction, conveying the connectivity structure of the brain, and overall clarity than existing methods for the same data. We demonstrate our technique with several real-world public DTI datasets.
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https://hal-enac.archives-ouvertes.fr/hal-03641308
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Soumis le : jeudi 14 avril 2022 - 11:31:45
Dernière modification le : mercredi 20 avril 2022 - 17:30:36
Archivage à long terme le : : vendredi 15 juillet 2022 - 18:56:03

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algorithms-13-00316-v2.pdf
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Steven Bouma, Christophe Hurter, Alexandru Telea. Structure-Aware Trail Bundling for Large DTI Datasets. Algorithms, MDPI, 2020, ⟨10.3390/a13120316⟩. ⟨hal-03641308⟩

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