QuickBundles, a Method for Tractography Simplification
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 ove...
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2012
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pubmed-35188232012-12-17 QuickBundles, a Method for Tractography Simplification Garyfallidis, Eleftherios Brett, Matthew Correia, Marta Morgado Williams, Guy B. Nimmo-Smith, Ian Neuroscience 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. Frontiers Media S.A. 2012-12-11 /pmc/articles/PMC3518823/ /pubmed/23248578 http://dx.doi.org/10.3389/fnins.2012.00175 Text en Copyright © 2012 Garyfallidis, Brett, Correia, Williams and Nimmo-Smith. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc. |
repository_type |
Open Access Journal |
institution_category |
Foreign Institution |
institution |
US National Center for Biotechnology Information |
building |
NCBI PubMed |
collection |
Online Access |
language |
English |
format |
Online |
author |
Garyfallidis, Eleftherios Brett, Matthew Correia, Marta Morgado Williams, Guy B. Nimmo-Smith, Ian |
spellingShingle |
Garyfallidis, Eleftherios Brett, Matthew Correia, Marta Morgado Williams, Guy B. Nimmo-Smith, Ian QuickBundles, a Method for Tractography Simplification |
author_facet |
Garyfallidis, Eleftherios Brett, Matthew Correia, Marta Morgado Williams, Guy B. Nimmo-Smith, Ian |
author_sort |
Garyfallidis, Eleftherios |
title |
QuickBundles, a Method for Tractography Simplification |
title_short |
QuickBundles, a Method for Tractography Simplification |
title_full |
QuickBundles, a Method for Tractography Simplification |
title_fullStr |
QuickBundles, a Method for Tractography Simplification |
title_full_unstemmed |
QuickBundles, a Method for Tractography Simplification |
title_sort |
quickbundles, a method for tractography simplification |
description |
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. |
publisher |
Frontiers Media S.A. |
publishDate |
2012 |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3518823/ |
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1611939330840854528 |