A Prototype Representation to Approximate White Matter Bundles with Weighted Currents
Quantitative and qualitative analysis of white matter fibers resulting from tractography algorithms is made difficult by their huge number. To this end, we propose an approximation scheme which gives as result a more concise but at the same time exhaustive representation of a fiber bundle. It is based on a novel computational model for fibers, called weighted currents, characterised by a metric that considers both the pathway and the anatomical locations of the endpoints of the fibers. Similarity has therefore a twofold connotation: geometrical and related to the connectivity. The core idea is to use this metric for approximating a fiber bundle with a set of weighted prototypes, chosen among the fibers, which represent ensembles of similar fibers. The weights are related to the number of fibers represented by the prototypes. The algorithm is divided into two steps. First, the main modes of the fiber bundle are detected using a modularity based clustering algorithm. Second, a prototype fiber selection process is carried on in each cluster separately. This permits to explain the main patterns of the fiber bundle in a fast and accurate way.
KeywordsWhite Matter Approximation Scheme Fiber Bundle Gaussian Mixture Model White Matter Tract
- 1.Marrakchi-Kacem, L., Delmaire, C., Guevara, P., Poupon, F., Lecomte, S., Tucholka, A., Roca, P., Yelnik, J., Durr, A., Mangin, J., Lehéricy, S., Poupon, C.: Mapping Cortico-Striatal Connectivity onto the Cortical Surface: A New Tractography-Based Approach to Study Huntington Disease. PLoS One 8, e53135 (2013)Google Scholar
- 5.Gerig, G., Gouttard, S., Corouge, I.: Analysis of Brain White Matter via Fiber Tract Modeling. In: 26th IEEE EMBS, vol. 2, pp. 4421–4424. IEEE Press, New York (2004)Google Scholar
- 9.Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10, P10008 (2008)Google Scholar
- 13.Liu, M., Vemuri, B.C., Deriche, R.: Unsupervised Automatic White Matter Fiber Clustering Using a Gaussian Mixture Model. In: 9th IEEE ISBI, pp. 522–525. IEEE Press, New York (2012)Google Scholar
- 14.Tunç, B., Smith, A.R., Wasserman, D., Pennec, X., Wells, W.M., Verma, R., Pohl, K.M.: Multinomial probabilistic fiber representation for connectivity driven clustering. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 730–741. Springer, Heidelberg (2013)CrossRefGoogle Scholar