%A Garyfallidis,Eleftherios %A Brett,Matthew %A Correia,Marta %A Williams,Guy %A Nimmo-Smith,Ian %D 2012 %J Frontiers in Neuroscience %C %F %G English %K tractography,diffusion MRI,Fiber clustering,White matter Segmentation,dimensionality reduction,clustering algorithms,DTI.,streamline clustering %Q %R 10.3389/fnins.2012.00175 %W %L %M %P %7 %8 2012-December-11 %9 Original Research %+ Mr Eleftherios Garyfallidis,University of Cambridge,Wolfson College,Cambridge,United Kingdom,garyfallidis@gmail.com %+ Mr Eleftherios Garyfallidis,MRC,Cognition and Brain Sciences Unit,Cambridge,United Kingdom,garyfallidis@gmail.com %# %! QuickBundles, a method for tractography simplification %* %< %T QuickBundles, a Method for Tractography Simplification %U https://www.frontiersin.org/articles/10.3389/fnins.2012.00175 %V 6 %0 JOURNAL ARTICLE %@ 1662-453X %X Diffusion MR data sets produce large numbers of streamlines which are hard to visualize, interact with, and interpret in a clinically acceptable time scale, despite numerous proposed approaches. As a solution we present a simple, compact, tailor-made clustering algorithm, QuickBundles (QB), that overcomes the complexity of these large data sets and provides informative clusters in seconds. Each QB cluster can be represented by a single centroid streamline; collectively these centroid streamlines can be taken as an effective representation of the tractography. We provide a number of tests to show how the QB reduction has good consistency and robustness. We show how the QB reduction can help in the search for similarities across several subjects.