Abstract
This paper aims at proposing a method to generate atlases of white matter fibers’ geometry that consider local orientation and curvature of fibers extracted from tractography data. Tractography was performed on diffusion magnetic resonance images from a set of healthy subjects and each tract was characterized voxel-wise by its curvature and Frenet–Serret frame, based on which similar tracts could be clustered separately for each voxel and each subject. Finally, the centroids of the clusters identified in all subjects were clustered to create the final atlas. The proposed clustering technique showed promising results in identifying voxel-wise distributions of curvature and orientation. Two tractography algorithms (one deterministic and one probabilistic) were tested for the present work, obtaining two different atlases. A high agreement between the two atlases was found in several brain regions. This suggests that more advanced tractography methods might only be required for some specific regions in the brain. In addition, the probabilistic approach resulted in the identification of a higher number of fiber orientations in various white matter areas, suggesting it to be more adequate for investigating complex fiber configurations in the proposed framework as compared to deterministic tractography.
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References
Alexander, D.C.: Modelling, fitting and sampling in diffusion MRI. In: Laidlaw, D., Weickert, J. (eds.) Visualization and Processing of Tensor Fields, pp. 3–20. Springer (2009)
Brun, A., Knutsson, H., Park, H.J., Shenton, M.E., Westin, C.F.: Clustering fiber traces using normalized cuts. In: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), vol. LNCS 3216-I, pp. 368–375 (2004). https://doi.org/10.1007/b100265.Clustering
Brusini, I., Jörgens, D., Smedby, Ö., Moreno, R.: Influence of tractography algorithms and settings on local curvature estimations. In: Proceedings of OHBM Annual Meeting (2017)
Cabeen, R.P., Bastin, M.E., Laidlaw, D.H.: Kernel regression estimation of fiber orientation mixtures in diffusion MRI. NeuroImage 127, 158–172 (2016)
do Carmo, M.P.: The local theory of curves parametrized by arc length. In: Differential Geometry of Curves and Surfaces, pp. 16–26. Prentice Hall (1976)
Corouge, I., Gouttard, S., Gerig, G.: Towards a shape model of white matter fiber bundles using diffusion tensor MRI. In: International Conference on Engineering in Medicine and Biology Society (EMBS), vol. 2, pp. 344–347. IEEE (2004). https://doi.org/10.1109/ISBI.2004.1398545
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979). https://doi.org/10.1109/TPAMI.1979.4766909
Dhollander, T., Emsell, L., Van Hecke, W., Maes, F., Sunaert, S., Suetens, P.: Track orientation density imaging (TODI) and track orientation distribution (TOD) based tractography. NeuroImage 94, 312–336 (2014)
Everitt, B., Landau, S., Leese, M., Stahl, D.: Hierarchical clustering. In: Cluster Analysis, pp. 71–110. Wiley (2011). https://doi.org/10.1002/9780470977811.ch4
Garyfallidis, E., Brett, M., Correia, M.M., Williams, G.B., Nimmo-Smith, I.: Quickbundles, a method for tractography simplification. Front. Neurosci. 6(175) (2012)
Grabner, G., Janke, A.L., Budge, M.M., Smith, D., Pruessner, J., Collins, D.L.: Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults. In: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), vol. 9, pp. 58–66 (2006). https://doi.org/10.1007/11866763_8
Jeurissen, B., Leemans, A., Tournier, J.D., Jones, D.K., Sijbers, J.: Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain Mapp. 34(11), 2747–2766 (2013). https://doi.org/10.1002/hbm.22099
O’ Donnell, L., Westin, C.F.: White matter tract clustering and correspondence in populations. In: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), vol. 8-I, pp. 140–147 (2005)
Rheault, F., St-Onge, E., Sidhu, J., Chenot, Q., Petit, L., Descoteaux, M.: Bundle-specific tractography. In: Computational Diffusion MRI, pp. 129–139. Springer International Publishing (2018)
Schultz, T.: Learning a reliable estimate of the number of fiber directions in diffusion MRI. Med. Image Comput. Comput. Assist. Interv. 15, 493–500 (2012)
Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472 (2007). https://doi.org/10.1016/j.neuroimage.2007.02.016
Tournier, J.D., Calamante, F., Connelly, A.: Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proc. Int. Soc. Magn. Reson. Med. 18, 1670 (2010)
Tournier, J.D., Calamante, F., Connelly, A.: MRtrix: diffusion tractography in crossing fiber regions. Internat. J. Imaging Syst. Technol. 22(1), 53–66 (2012). https://doi.org/10.1002/ima.22005
Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23(3), 1176–1185 (2004). https://doi.org/10.1016/j.neuroimage.2004.07.037
Van Essen, D.C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T.E.J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S.W., Della Penna, S., Feinberg, D., Glasser, M.F., Harel, N., Heath, A.C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., Oostenveld, R., Petersen, S.E., Prior, F., Schlaggar, B.L., Smith, S.M., Snyder, A.Z., Xu, J., Yacoub, E.: The human connectome project: a data acquisition perspective. NeuroImage 62(4), 2222–2231 (2012). https://doi.org/10.1016/j.neuroimage.2012.02.018
WU-Minn Consortium Human: Connectome Project: WU-Minn HCP 500 Subjects. Data Release: Reference Manual (2014)
Zappalà, S.: Evaluation of the inter-subject variability of white matter tract curvature and its influence on finite element simulations of an anisotropic model of the brain tissue. Master’s thesis. KTH Royal Institute of Technology, Stockholm, Sweden (2016)
Acknowledgements
Data were partially provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
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Brusini, I., Jörgens, D., Smedby, Ö., Moreno, R. (2019). Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-05831-9_27
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