Abstract
Finding the fiber k-nearest-neighbors (k-NN) is often essential to brain white matter analysis yet it is computationally prohibitive, and no efficient approximation to it is known to the best of our knowledge. We observe a strong relationship between the point-wise distances and tract-wise distances. Based on this observation, we propose a fast algorithm for approximating the k-NN distances of large fiber bundles with point-wise K-NN algorithm, and we call it the fast fiber k-NN algorithm. Furthermore, we apply our fast fiber k-NN algorithm to white matter topography analysis, which is an emerging problem in brain connectomics reasearch. For the latter task, we first propose to quantify the white matter topography by metric embedding, which gives rise to the first anatomically meaningful fiber-wise measure of white matter topography to the best of our knowledge. In addition, we extend the individual white matter topography analysis to group-wise analysis using the k-NN fiber distances computed with our fast algorithm. In our experiments, (a) we find that our fast fiber k-NN algorithm reasonably approximates the ground-truth distance at 1–2 percent of its computational cost, (b) we also verify the anatomical validity of our proposed topographic measure, and (c) we find that our fast fiber k-NN algorithm performs identically well compared with the exhaustive fiber distance computation, for the group-wise white matter topography analysis for 792 subjects from the Human Connectome Project.
This work was in part supported by the National Institute of Health (NIH) under Grant RF1AG056573, R01EB022744, U01EY025864, U01AG051218, P41 EB015922, P50AG05142. Data used in this paper were 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abraham, I., Bartal, Y., Neiman, O.: Advances in metric embedding theory. Adv. Math. 228(6), 3026–3126 (2011)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Borg, I., Groenen, P.J.: Modern Multidimensional Scaling: Theory and Applications. Springer, Heidelberg (2005). https://doi.org/10.1007/0-387-28981-X
Cabeen, R.P., Laidlaw, D.H., Toga, A.W.: Quantitative imaging toolkit: software for interactive 3D visualization, processing, and analysis of neuroimaging datasets. In: Proceedings of the International Society for Magnetic Resonance in Medicine (2018)
Gibson, W.: On the least-squares orthogonalization of an oblique transformation. Psychometrika 27(2), 193–195 (1962)
Glasser, M.F., et al.: The minimal preprocessing pipelines for the human connectome project. Neuroimage 80, 105–124 (2013)
Jbabdi, S., Sotiropoulos, S.N., Behrens, T.E.: The topographic connectome. Curr. Opin. Neurobiol. 23(2), 207–215 (2013)
Jianu, R., Demiralp, C., Laidlaw, D.: Exploring 3D DTI fiber tracts with linked 2D representations. IEEE Trans. Vis. Comput. Graph. 15(6), 1449–1456 (2009)
Jin, Y., et al.: Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics. Neuroimage 100, 75–90 (2014)
Lambert, C., Simon, H., Colman, J., Barrick, T.R.: Defining thalamic nuclei and topographic connectivity gradients in vivo. Neuroimage 158, 466–479 (2017)
O’Donnell, L.J., Westin, C.F.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Med. Imaging 26(11), 1562–1575 (2007)
Poulin, P., et al.: Learn to track: deep learning for tractography. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 540–547. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_62
Siless, V., Chang, K., Fischl, B., Yendiki, A.: Anatomicuts: hierarchical clustering of tractography streamlines based on anatomical similarity. Neuroimage 166, 32–45 (2018)
Tournier, J., Calamante, F., Connelly, A., et al.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22(1), 53–66 (2012)
Wang, J., Aydogan, D.B., Varma, R., Toga, A.W., Shi, Y.: Topographic regularity for tract filtering in brain connectivity. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 263–274. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_21
Wang, J., Aydogan, D.B., Varma, R., Toga, A.W., Shi, Y.: Modeling topographic regularity in structural brain connectivity with application to tractogram filtering. NeuroImage 183, 87–98 (2018)
Wedeen, V.J., et al.: The geometric structure of the brain fiber pathways. Science 335(6076), 1628–1634 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, J., Shi, Y. (2019). A Fast Fiber k-Nearest-Neighbor Algorithm with Application to Group-Wise White Matter Topography Analysis. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-20351-1_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20350-4
Online ISBN: 978-3-030-20351-1
eBook Packages: Computer ScienceComputer Science (R0)