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Atlas-Guided Cluster Analysis of Large Tractography Datasets

Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles...

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Autores principales: Ros, Christian, Güllmar, Daniel, Stenzel, Martin, Mentzel, Hans-Joachim, Reichenbach, Jürgen Rainer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3875498/
https://www.ncbi.nlm.nih.gov/pubmed/24386292
http://dx.doi.org/10.1371/journal.pone.0083847
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author Ros, Christian
Güllmar, Daniel
Stenzel, Martin
Mentzel, Hans-Joachim
Reichenbach, Jürgen Rainer
author_facet Ros, Christian
Güllmar, Daniel
Stenzel, Martin
Mentzel, Hans-Joachim
Reichenbach, Jürgen Rainer
author_sort Ros, Christian
collection PubMed
description Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles for multiple datasets is still challenging, we present a novel atlas-guided clustering framework for exploratory data analysis of large tractography datasets. The framework uses an hierarchical cluster analysis approach that exploits the inherent redundancy in large datasets to time-efficiently group fiber tracts. Structural information of a white matter atlas can be incorporated into the clustering to achieve an anatomically correct and reproducible grouping of fiber tracts. This approach facilitates not only the identification of the bundles corresponding to the classes of the atlas; it also enables the extraction of bundles that are not present in the atlas. The new technique was applied to cluster datasets of 46 healthy subjects. Prospects of automatic and anatomically correct as well as reproducible clustering are explored. Reconstructed clusters were well separated and showed good correspondence to anatomical bundles. Using the atlas-guided cluster approach, we observed consistent results across subjects with high reproducibility. In order to investigate the outlier elimination performance of the clustering algorithm, scenarios with varying amounts of noise were simulated and clustered with three different outlier elimination strategies. By exploiting the multithreading capabilities of modern multiprocessor systems in combination with novel algorithms, our toolkit clusters large datasets in a couple of minutes. Experiments were conducted to investigate the achievable speedup and to demonstrate the high performance of the clustering framework in a multiprocessing environment.
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spelling pubmed-38754982014-01-02 Atlas-Guided Cluster Analysis of Large Tractography Datasets Ros, Christian Güllmar, Daniel Stenzel, Martin Mentzel, Hans-Joachim Reichenbach, Jürgen Rainer PLoS One Research Article Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles for multiple datasets is still challenging, we present a novel atlas-guided clustering framework for exploratory data analysis of large tractography datasets. The framework uses an hierarchical cluster analysis approach that exploits the inherent redundancy in large datasets to time-efficiently group fiber tracts. Structural information of a white matter atlas can be incorporated into the clustering to achieve an anatomically correct and reproducible grouping of fiber tracts. This approach facilitates not only the identification of the bundles corresponding to the classes of the atlas; it also enables the extraction of bundles that are not present in the atlas. The new technique was applied to cluster datasets of 46 healthy subjects. Prospects of automatic and anatomically correct as well as reproducible clustering are explored. Reconstructed clusters were well separated and showed good correspondence to anatomical bundles. Using the atlas-guided cluster approach, we observed consistent results across subjects with high reproducibility. In order to investigate the outlier elimination performance of the clustering algorithm, scenarios with varying amounts of noise were simulated and clustered with three different outlier elimination strategies. By exploiting the multithreading capabilities of modern multiprocessor systems in combination with novel algorithms, our toolkit clusters large datasets in a couple of minutes. Experiments were conducted to investigate the achievable speedup and to demonstrate the high performance of the clustering framework in a multiprocessing environment. Public Library of Science 2013-12-30 /pmc/articles/PMC3875498/ /pubmed/24386292 http://dx.doi.org/10.1371/journal.pone.0083847 Text en © 2013 Ros et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Ros, Christian
Güllmar, Daniel
Stenzel, Martin
Mentzel, Hans-Joachim
Reichenbach, Jürgen Rainer
Atlas-Guided Cluster Analysis of Large Tractography Datasets
title Atlas-Guided Cluster Analysis of Large Tractography Datasets
title_full Atlas-Guided Cluster Analysis of Large Tractography Datasets
title_fullStr Atlas-Guided Cluster Analysis of Large Tractography Datasets
title_full_unstemmed Atlas-Guided Cluster Analysis of Large Tractography Datasets
title_short Atlas-Guided Cluster Analysis of Large Tractography Datasets
title_sort atlas-guided cluster analysis of large tractography datasets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3875498/
https://www.ncbi.nlm.nih.gov/pubmed/24386292
http://dx.doi.org/10.1371/journal.pone.0083847
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