Abstract
Diffusion MRI fiber tracking datasets can contain millions of 3D streamlines, and their representation can weight tens of gigabytes of memory. These sets of streamlines are called tractograms and are often used for clinical operations or research. Their size makes them difficult to store, visualize, process or exchange over the network. We propose a new compression algorithm well-suited for tractograms, by taking advantage of the way streamlines are obtained with usual tracking algorithms. Our approach is based on unit vector quantization methods combined with a spatial transformation which results in low compression and decompression times, as well as a high compression ratio. For instance, a 11.5GB tractogram can be compressed to a 1.02GB file and decompressed in 11.3 seconds. Moreover, our method allows for the compression and decompression of individual streamlines, reducing the need for a costly out-of-core algorithm with heavy datasets. Last, we open a way toward on-the-fly compression and decompression for handling larger datasets without needing a load of RAM (i.e. in-core handling), faster network exchanges and faster loading times for visualization or processing.
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References
Alexandroni, G., Zimmerman Moreno, G., Sochen, N., & Greenspan, H. (2017). The fiber-density-coreset for redundancy reduction in huge fiber-sets. NeuroImage, 146, 246–256. https://doi.org/10.1016/j.neuroimage.2016.11.027.
Caiafa, C.F., & Pestilli, F. (2017). Multidimensional encoding of brain connectomes. Scientific Reports, 7(1), 11,491. https://doi.org/10.1038/s41598-017-09250-w.
Chung, M.K., Adluru, N., Lee, J.E., Lazar, M., Lainhart, J.E., & Alexander, A.L. (2009). Efficient parametric encoding scheme for white matter fiber bundles. IEEE EMBS, 2009, 6644–6647. https://doi.org/10.1109/IEMBS.2009.5332866.
Cigolle, Z.H., Donow, S., Evangelakos, D., Mara, M., McGuire, M., & Meyer, Q. (2014). A survey of efficient representations for independent unit vectors. JCGT, 3(2), 1–30.
Demir, A., & Çetingül, H.E. (2015). Sequential Hierarchical, Agglomerative Clustering of White Matter Fiber Pathways. IEEE Trans Biomed Eng, 62(6), 1478–1489. https://doi.org/10.1109/TBME.2015.2391913.
Garyfallidis, E., Brett, M., Correia, M.M., Williams, G.B., & Nimmo-Smith, I. (2012). Quickbundles, a Method for Tractography Simplification. Frontiers in Neuroscience, 6(175), 1–13.
González, A. (2010). Measurement of areas on a sphere using fibonacci and Latitude-Longitude lattices. Mathematical Geosciences, 42(1), 49–64. https://doi.org/10.1007/s11004-009-9257-x.
Gori, P., Colliot, O., Marrakchi-Kacem, L., Worbe, Y., Fallani, F.D.V., Chavez, M., Poupon, C., Hartmann, A., Ayache, N., & Durrleman, S. (2016). Parsimonious approximation of streamline trajectories in white matter fiber bundles. IEEE Trans Med Imag, 35(12), 2609–2619. https://doi.org/10.1109/TMI.2016.2591080.
Guevara, P., Poupon, C., Rivière, D, Cointepas, Y., Descoteaux, M., Thirion, B., & Mangin, J.F. (2011). Robust clustering of massive tractography datasets. NeuroImage, 54(3), 1975–1993.
Keinert, B., Innmann, M., Sänger, M, & Stamminger, M. (2015). Spherical Fibonacci Mapping. ACM TOG, 34(6), 193:1–193:7.
Kumar, K., & Desrosiers, C. (2016). A sparse coding approach for the efficient representation and segmentation of white matter fibers. In IEEE ISBI (pp. 915–919). https://doi.org/10.1109/ISBI.2016.7493414.
Lindstrom, P. (2014). Fixed-Rate Compressed Floating-Point arrays. IEEE TVCG, 20(12), 2674–2683.
Liu, M., Vemuri, B.C., & Deriche, R. (2012). Unsupervised automatic white matter fiber clustering using a Gaussian mixture model. IEEE International Symposium on Biomedical Imaging, 2012(9), 522–525. https://doi.org/10.1109/ISBI.2012.6235600.
Maddah, M., Wells, W.M., Warfield, S.K., Westin, C.F., & Grimson, W.E.L. (2007). Probabilistic Clustering and Quantitative Analysis of White Matter Fiber tracts. In IPMI (vol. 20, pp. 372–383).
Mercier, C., Gori, P., Rohmer, D., Cani, M.P., Boubekeur, T., Thiery, J.M., & Bloch, I. (2018). Progressive and Efficient Multi-Resolution Representations for Brain Tractograms. In EG VCBM (pp. 89–93).
Meyer, Q., Süßmuth, J, Sußner, G, Stamminger, M., & Greiner, G. (2010). On Floating-point Normal Vectors. In EGSR (pp. 1405–1409).
Moreno, G.Z., Alexandroni, G., Sochen, N., & Greenspan, H. (2017). Sparse Representation for White Matter Fiber Compression and Calculation of Inter-Fiber Similarity. In Computational Diffusion MRI (pp. 133–143).
Olivetti, E., Bertò, G, Gori, P., Sharmin, N., & Avesani, P. (2017). Comparison of Distances for Supervised Segmentation of White Matter Tractography. In PRNI (pp. 1–4). https://doi.org/10.1109/PRNI.2017.7981502, 1708.01440.
Petrovic, V., Fallon, J., & Kuester, F. (2007). Visualizing Whole-Brain DTI Tractography with GPU-based Tuboids and LoD Management. IEEE TVCG, 13(6), 1488–1495. https://doi.org/10.1109/TVCG.2007.70532.
Presseau, C., Jodoin, P.M., Houde, J.C., & Descoteaux, M. (2015). A new compression format for fiber tracking datasets. NeuroImage, 109, 73–83.
Rheault, F., Houde, J.C., & Descoteaux, M. (2017). Visualization, interaction and tractometry: Dealing with millions of streamlines from diffusion MRI tractography. Frontiers in Neuroinformatics, 11, 42.
Rousseau, S., & Boubekeur, T. (2017). Fast Lossy Compression of 3D Unit Vector Sets. In SIGGRAPH Asia Tech. Briefs (pp. 23:1–23:4).
Siless, V., Chang, K., Fischl, B., & Yendiki, A. (2018). Anatomicuts: Hierarchical clustering of tractography streamlines based on anatomical similarity. NeuroImage, 166(Supplement C), 32–45. https://doi.org/10.1016/j.neuroimage.2017.10.058.
Soares, J., Marques, P., Alves, V., & Sousa, N. (2013). A hitchhiker’s guide to diffusion tensor imaging. Frontiers in Neuroscience, 7, 31.
Tournier, J.D., Mori, S., & Leemans, A. (2011). Diffusion tensor imaging and beyond. Magnetic Resonance in Medicine, 65(6), 1532– 1556.
Tournier, J.D., Calamante, F., & Connelly, A. (2012). MRTrix: Diffusion tractography in crossing fiber regions. Int J of Imaging Systems and Technology, 22(1), 53–66.
Van Essen, D., Ugurbil, K., Auerbach, E., & et al. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231.
Wassermann, D., Bloy, L., Kanterakis, E., Verma, R., & Deriche, R. (2010). Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers. NeuroImage, 51(1), 228–241. https://doi.org/10.1016/j.neuroimage.2010.01.004.
Zhang, S., & Laidlaw, D.H. (2002). Hierarchical Clustering of Streamtubes. Technical report: Brown University, CS Department 3.
Zhang, F., Wu, Y., Norton, I., Rigolo, L., Rathi, Y., Makris, N., & O’Donnell, L.J. (2018). An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. NeuroImage, 179, 429–447. https://doi.org/10.1016/j.neuroimage.2018.06.027.
Acknowledgements
We would like to thank Alessandro Delmonte for his precious help and his feedback on our results. This work was partially supported by the French National Research Agency (ANR) under grant ANR 16-LCV2-0009-01 ALLEGORI, by the DGA and by Labex DigiCosme (project ANR11LABEX0045DIGICOSME) operated by ANR as part of the program Investissement d’Avenir Idex ParisSaclay (ANR11IDEX000302).
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C. Mercier, S. Rousseau contributed equally to this work
Appendices
Appendix
A Pseudo-code
This is the C++ pseudo-code of our compression and decompression algorithm. The pseudo-code of the mapping and inverseMapping functions can be found in Rousseau and Boubekeur (2017) article. The quantize and unquantize functions are the octahedral quantization (Meyer et al. 2010) or the spherical Fibonacci (Keinert et al. 2015). For more details on the implementation, the reader can refer to the source code available on GitHub. https://github.com/syrousseau/qfib
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Mercier, C., Rousseau, S., Gori, P. et al. QFib: Fast and Efficient Brain Tractogram Compression. Neuroinform 18, 627–640 (2020). https://doi.org/10.1007/s12021-020-09452-0
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DOI: https://doi.org/10.1007/s12021-020-09452-0