date: 2009-09-14T10:52:16Z
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pdf:docinfo:title: Quantifying Brain Connectivity: A Comparative Tractography Study
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subject: In this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwMRI) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. As criterion for the comparison, we quantify the connectivity computed with the different methods. We provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (PAS) along with deterministic and probabilistic tractography algorithms on a human DWI dataset. The connectivity is presented for a representative selection of regions in the brain in matrices and connectograms.Our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. Probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.
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pdf:docinfo:custom:bibtex/booktitle: MICCAI
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dc:title: Quantifying Brain Connectivity: A Comparative Tractography Study
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cp:subject: In this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwMRI) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. As criterion for the comparison, we quantify the connectivity computed with the different methods. We provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (PAS) along with deterministic and probabilistic tractography algorithms on a human DWI dataset. The connectivity is presented for a representative selection of regions in the brain in matrices and connectograms.Our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. Probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.
pdf:docinfo:custom:bibtex/bibsource: DBLP, http://dblp.uni-trier.de
pdf:docinfo:subject: In this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwMRI) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. As criterion for the comparison, we quantify the connectivity computed with the different methods. We provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (PAS) along with deterministic and probabilistic tractography algorithms on a human DWI dataset. The connectivity is presented for a representative selection of regions in the brain in matrices and connectograms.Our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. Probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.
pdf:docinfo:creator: Ting-Shuo Yo and Alfred Anwander and Maxime Descoteaux and Pierre Fillard and Cyril Poupon and Thomas Knösche
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Author: Ting-Shuo Yo
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dc:description: In this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwMRI) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. As criterion for the comparison, we quantify the connectivity computed with the different methods. We provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (PAS) along with deterministic and probabilistic tractography algorithms on a human DWI dataset. The connectivity is presented for a representative selection of regions in the brain in matrices and connectograms.Our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. Probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.
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dc:creator: Ting-Shuo Yo
description: In this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwMRI) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. As criterion for the comparison, we quantify the connectivity computed with the different methods. We provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (PAS) along with deterministic and probabilistic tractography algorithms on a human DWI dataset. The connectivity is presented for a representative selection of regions in the brain in matrices and connectograms.Our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. Probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.
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title: Quantifying Brain Connectivity: A Comparative Tractography Study
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