Abstract
As tractography datasets continue to grow in size, there is a need for improved visualization methods that can capture structural patterns occurring in large tractography datasets. Transparency is an increasingly important aspect of finding these patterns in large datasets but is inaccessible to tractography due to performance limitations. In this paper, we propose a rendering method that achieves performant rendering of transparent streamlines, allowing for exploration of deeper brain structures interactively. The method achieves this through a novel approximate order-independent transparency method that utilizes voxelization and caching view-dependent line orders per voxel. We compare our transparency method with existing tractography visualization software in terms of performance and the ability to capture deeper structures in the dataset.
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References
Everitt, C.: Nvidia corporation: order-independent transparency (2001). https://developer.download.nvidia.com/assets/gamedev/docs/OrderIndependentTransparency.pdf
Kanzler, M., Rautenhaus, M., Westermann, R.: A voxel-based rendering pipeline for large 3d line sets. IEEE Trans. Visual. Comput. Graph. 25(07), 2378–2391 (01 2018). https://doi.org/10.1109/TVCG.2018.2834372
Kern, M., Neuhauser, C., Maack, T., Han, M., Usher, W., Westermann, R.: A comparison of rendering techniques for 3D line sets with transparency. IEEE Trans. Visual Comput. Graph. 27(8), 3361–3376 (2021). https://doi.org/10.1109/TVCG.2020.2975795
MRtrix3: Add support for visualizing tractography data (Issue #177). https://github.com/MRtrix3/mrtrix3/issues/177
Rheault, F., Houde, J.C., Descoteaux, M.: Visualization, interaction and tractometry: dealing with millions of streamlines from diffusion MRI tractography. Front. Neuroinform. 11 (06 2017). https://doi.org/10.3389/fninf.2017.00042
Salvi, M., Vaidyanathan, K.: Multi-layer alpha blending, pp. 151–158 (03 2014). https://doi.org/10.1145/2556700.2556705
Schultz, T., Sauber, N., Anwander, A., Theisel, H., Seidel, H.P.: Virtual klingler dissection: putting fibers into context. Comput. Graph. Forum 27(3) (2008). https://doi.org/10.1111/j.1467-8659.2008.01243.x
Tax, C., et al.: Seeing more by showing less: orientation-dependent transparency rendering for fiber tractography visualization. PloS one 10, e0139434 (10 2015). https://doi.org/10.1371/journal.pone.0139434
Tournier, J.D., et al.: Mrtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202, 116137 (2019). https://doi.org/10.1016/j.neuroimage.2019.116137. https://www.sciencedirect.com/science/article/pii/S1053811919307281
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K.: The wu-minn human connectome project: an overview. NeuroImage 80, 62–79 (2013). https://doi.org/10.1016/j.neuroimage.2013.05.041. https://www.sciencedirect.com/science/article/pii/S1053811913005351, mapping the Connectome
Wang, R., Benner, T., Sorensen, A., Wedeen, V.: Diffusion toolkit: a software package for diffusion imaging data processing and tractography. In: Proceedings of the International Soc Mag Reson Med vol. 15 (01 2007)
Wasserthal, J., Neher, P., Maier-Hein, K.H.: Tractseg - fast and accurate white matter tract segmentation. NeuroImage 183, 239–253 (2018). https://doi.org/10.1016/j.neuroimage.2018.07.070, https://www.sciencedirect.com/science/article/pii/S1053811918306864
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Osman, B., Pereira, M., van de Wetering, H., Chamberland, M. (2023). Voxlines: Streamline Transparency Through Voxelization and View-Dependent Line Orders. In: Karaman, M., Mito, R., Powell, E., Rheault, F., Winzeck, S. (eds) Computational Diffusion MRI. CDMRI 2023. Lecture Notes in Computer Science, vol 14328. Springer, Cham. https://doi.org/10.1007/978-3-031-47292-3_9
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DOI: https://doi.org/10.1007/978-3-031-47292-3_9
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