Abstract
We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit.
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References
Barnett, V., & Lewis, T. (1994). Outliers in statistical data Vol. 3. New York: Wiley New York.
Basser, P.J., & Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of magnetic resonance. Series B, 111(3), 209–219.
Bishop, C.M. (1994). Novelty detection and neural network validation. In Vision, Image and Signal Processing, IEEE Proceedings, (Vol. 141, IET pp. 217–222).
Bland, J.M., & Altman, D.G. (1990). A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement. Computers in biology and medicine, 20 (5), 337–40.
Brun, A., Knutsson, H., Park, H.J., Shenton, M.E., & Westin, C-F. (2004). Clustering fiber traces using normalized cuts. MICCAI, 2004(3216), 368–375. https://doi.org/10.1007/b100265.Clustering.
Cabeen, R.P., Bastin, M.E., & Laidlaw, D.H. (2013). A Diffusion MRI Resource of 80 Age-varied Subjects with Neuropsychological and Demographic Measures. In Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM).
Cabeen, R.P., Bastin, M.E., & Laidlaw, D.H. (2016). Kernel regression estimation of fiber orientation mixtures in diffusion MRI. NeuroImage, 127, 158–172.
Cabeen, R.P., Bastin, M.E., & Laidlaw, D.H. (2017). A Comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging. NeuroImage, 146, 100–112.
Cabeen, R.P., Laidlaw, D.H., & Toga, A.W. (2018). Quantitative Imaging Toolkit: Software for Interactive 3D Visualization, Processing, and Analysis of Neuroimaging Datasets. In Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM).
Clayden, J.D., Storkey, A.J., & Bastin, M.E. (2007). A Probabilistic Model-Based Approach to Consistent White Matter Tract Segmentation. IEEE Transaction on Medical Imaging, 26(11), 1555–1561. https://doi.org/10.1109/TMI.2007.905826.
Corouge, I., Fletcher, P.T., Joshi, S.C., Gouttard, S., & Gerig, G. (2006). Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Medical Image Analysis, 10(5), 786–798. https://doi.org/10.1016/j.media.2006.07.003.
Correia, S., Lee, S.Y., Voorn, T., Tate, D.F., Paul, R.H., Zhang, S., Salloway, S.P., Malloy, P.F., & Laidlaw, D.H. (2008). Quantitative tractography metrics of white matter integrity in diffusion-tensor MRI. NeuroImage, 42(2), 568–581.
Dice, L.R. (1945). Measures of the amount of ecologic association between species. Ecology, 26 (3), 297–302.
Dodero, L., Vascon, S., Murino, V., Bifone, A., Gozzi, A., & Sona, D. (2015). Automated multi-subject fiber clustering of mouse brain using dominant sets. Frontiers in Neuroinformatics, 8, 1–12. https://doi.org/10.3389/fninf.2014.00087.
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.
Gerig, G., Gouttard, S., & Corouge, I. (2004). Analysis of brain white matter via fiber tract modeling. In Engineering in Medicine and Biology Society, 2004. IEMBS ’04. 26th Annual International Conference of the IEEE, (Vol. 2 pp. 4421–4424).
Hendricks, W.A., & Robey, K.W. (2008). The Sampling Distribution of the Coefficient of Variation. Annals of Mathematical Statistics, 7(3), 129–132. https://doi.org/10.1214/193940307000000455.
Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of classification, 2(1), 193–218.
Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., & Smith, S.M. (2012). FSL. NeuroImage, 62, 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015.
Kulis, B., & Jordan, M.I. (2012). Revisiting k-means: New algorithms via bayesian nonparametrics. In Proceedings of the 29th International Conference on Machine Learning (ICML-12) (pp. 513–520).
Leemans, A., & Jones, D.K. (2009). The B-matrix must be rotated when correcting for subject motion in DTI data. Magnetic resonance in medicine, 61(6), 1336–49. https://doi.org/10.1002/mrm.21890.
Lenglet, C., Campbell, J.S.W., Descoteaux, M., Haro, G., Savadjiev, P., Wassermann, D., Anwander, Deriche, R., Pike, G.B., Sapiro, G., Siddiqi, K., & Thompson, P.M. (2009). Mathematical methods for diffusion MRI processing. NeuroImage, 45(1 Suppl), S111–22. https://doi.org/10.1016/j.neuroimage.2008.10.054.
Maddah, M., Grimson, W.E.L., Warfield, S.K., & Wells, W.M. (2008). A unified framework for clustering and quantitative analysis of white matter fiber tracts. Medical Image Analysis, 12(2), 191–202.
Mauch, S. (2000). A fast algorithm for computing the closest point and distance transform. Go online to http://www.acm.caltech.edu/seanm/software/cpt/cpt.pdf.
Moberts, B., Vilanova, A., & van Wijk, J.J. (2005). Evaluation of fiber clustering methods for diffusion tensor imaging. In VIS 05. IEEE Visualization, 2005., IEEE (pp. 65–72).
Moya, M.M., & Hush, D.R. (1996). Network constraints and multi-objective optimization for one-class classification. Neural Networks, 9(3), 463–474.
O’Donnell, L.J., & Westin, C.F. (2007). Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas. IEEE Transactions on Medical Imaging, 26(11), 1562–1575.
O’Donnell, L.J., Golby, A.J., & Westin, C.-F. (2013). Fiber clustering versus the parcellation-based connectome. NeuroImage, 80, 283–9. https://doi.org/10.1016/j.neuroimage.2013.04.066.
O’Donnell, L.J., & Schultz, T. (2015). Statistical and machine learning methods for neuroimaging: examples, challenges, and extensions to diffusion imaging data. In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data (pp. 299–319): Springer.
Pierpaoli, C., & Basser, P.J. (1996). Toward a Quantitative Assessment of Diffusion Anisotropy. Magnetic resonance in Medicine, 36(6), 893–906.
R Core Team. (2015). R: A language and environment for statistical computing, R Foundation for Statistical Computing. Vienna, Austria.
Saalfeld, A. (1999). Topologically consistent line simplification with the Douglas-Peucker algorithm. Cartography and Geographic Information Science, 26(1), 7–18.
Sima, C., & Dougherty, E.R. (2008). The peaking phenomenon in the presence of feature-selection. Pattern Recognition Letters, 29(11), 1667–1674.
Tarassenko, L., Hayton, P., Cerneaz, N., & Brady, M. (1995). Novelty detection for the identification of masses in mammograms. In Artificial Neural Networks, 1995., Fourth International Conference on, IET (pp. 442–447).
Tax, DM. (2001). One-class classification. TU Delft, Delft University of Technology.
Wang, Q., Yap, P.-T., Wu, G., & Shen, D. (2011). Fiber modeling and clustering based on neuroanatomical features. MICCAI, 14(Pt 2), 17–24.
Wang, X., Grimson, W.E.L., & Westin, C.F. (2011). Tractography segmentation using a hierarchical Dirichlet processes mixture model. NeuroImage, 54(1), 290–302.
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.
Wickham, H. (2009). ggplot2: elegant graphics for data analysis. New York: Springer.
Wolak, M.E., Fairbairn, D.J., & Paulsen, Y.R. (2012). Guidelines for estimating repeatability. Methods in Ecology and Evolution, 3(Boake 1989), 129–137. https://doi.org/10.1111/j.2041-210X.2011.00125.x.
Yendiki, A., Panneck, P., Srinivasan, P., Stevens, A., Zöllei, L, Augustinack, J., Wang, R., Salat, D., Ehrlich, S., Behrens, T., Jbabdi, S., Gollub, R., & Fischl, B. (2011). Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Frontiers in neuroinformatics, 5(October), 23. https://doi.org/10.3389/fninf.2011.00023.
Zhang, H., Yushkevich, P., Rueckert, D., & Gee, J.C. (2007). Unbiased white matter atlas construction using diffusion tensor images. MICCAI, 10(Pt 2), 211–8.
Zhang, S., Correia, S., & Laidlaw, D.H. (2008). Identifying White-Matter Fiber Bundles in DTI Data Using an Automated Proximity-Based Fiber-Clustering Method. IEEE Transactions on Visualization and Computer Graphics, 14(5), 1044–1053. https://doi.org/10.1109/TVCG.2008.52.
Zhang, S., Demiralp, C., & Laidlaw, D.H. (2003). Visualizing diffusion tensor mr images using streamtubes and streamsurfaces. Visualization and Computer Graphics, IEEE Transactions on, 9(4), 454–462.
Zhang, Y., Zhang, J., Oishi, K., Faria, A.V., & Jiang, H. (2010). Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy. NeuroImage, 52(4), 1289–1301. https://doi.org/10.1016/j.neuroimage.2010.05.049.Atlas-Guided.
Acknowledgments
This work was supported by the Brown Institute for Brain Science Graduate Research Award and NIH National Institute of Biomedical Imaging and Bioengineering grant P41 EB015922-23. The authors have no conflict of interest to report.
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Cabeen, R.P., Toga, A.W. & Laidlaw, D.H. Tractography Processing with the Sparse Closest Point Transform. Neuroinform 19, 367–378 (2021). https://doi.org/10.1007/s12021-020-09488-2
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DOI: https://doi.org/10.1007/s12021-020-09488-2