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Information-theoretic approach for automated white matter fiber tracts reconstruction

Fiber tracking is the most popular technique for creating white matter connectivity maps from diffusion tensor imaging (DTI). This approach requires a seeding process which is challenging because it is not clear how and where the seeds have to be placed. On the other hand, to enhance the interpretation of fiber maps, segmentation and clustering techniques are applied to organize fibers into anatomical structures. In this paper, we propose a new approach to automatically obtain bundles of fibers grouped into anatomical regions. This method applies an information-theoretic split-and-merge algorithm that considers fractional anisotropy and fiber orientation information to automatically segment white matter into volumes of interest (VOIs) of similar FA and eigenvector orientation. For each VOI, a number of planes and seeds is automati- cally placed in order to create the fiber bundles. The proposed approach avoids the need for the user to define seeding or selection regions. The whole process requires less than a minute and minimal user interaction. The agreement between the automated and manual approaches has been measured for 10 tracts in a DTI brain atlas and found to be almost perfect (kappa > 0.8) and substantial (kappa > 0.6). This method has also been evaluated on real DTI data considering 5 tracts. Agreement was substantial (kappa > 0.6) in most of the cases

This work has been supported by TIN2010-21089-C03-01 and 2009 SGR 643 and FIS PS09/00596 of I+D+I 2009-2012

© Neuroinformatics, 2012, vol. 10, núm. 3, p. 305-318

Springer Verlag

Manager: Ministerio de Ciencia e Innovación (Espanya)
Generalitat de Catalunya. Agència de Gestió d’Ajuts Universitaris i de Recerca
Author: Prados Carrasco, Ferran
Boada, Imma
Feixas Feixas, Miquel
Prats Galino, Alberto
Blasco Solà, Gerard
Puig Alcántara, Josep
Pedraza Gutiérrez, Salvador
Date: 2012
Abstract: Fiber tracking is the most popular technique for creating white matter connectivity maps from diffusion tensor imaging (DTI). This approach requires a seeding process which is challenging because it is not clear how and where the seeds have to be placed. On the other hand, to enhance the interpretation of fiber maps, segmentation and clustering techniques are applied to organize fibers into anatomical structures. In this paper, we propose a new approach to automatically obtain bundles of fibers grouped into anatomical regions. This method applies an information-theoretic split-and-merge algorithm that considers fractional anisotropy and fiber orientation information to automatically segment white matter into volumes of interest (VOIs) of similar FA and eigenvector orientation. For each VOI, a number of planes and seeds is automati- cally placed in order to create the fiber bundles. The proposed approach avoids the need for the user to define seeding or selection regions. The whole process requires less than a minute and minimal user interaction. The agreement between the automated and manual approaches has been measured for 10 tracts in a DTI brain atlas and found to be almost perfect (kappa > 0.8) and substantial (kappa > 0.6). This method has also been evaluated on real DTI data considering 5 tracts. Agreement was substantial (kappa > 0.6) in most of the cases
This work has been supported by TIN2010-21089-C03-01 and 2009 SGR 643 and FIS PS09/00596 of I+D+I 2009-2012
Format: application/pdf
Citation: 022007
ISSN: 1539-2791 (versió paper)
1559-0089 (versió electrònica)
Document access: http://hdl.handle.net/10256/11677
Language: eng
Publisher: Springer Verlag
Collection: MICINN/PN 2011-2013/TIN2010-21089-C03-01
AGAUR/2009-2014/2009 SGR-643
Reproducció digital del document publicat a: http://dx.doi.org/10.1007/s12021-012-9148-z
Articles publicats (D-IMA)
Is part of: © Neuroinformatics, 2012, vol. 10, núm. 3, p. 305-318
Rights: Tots els drets reservats
Subject: Imatgeria per ressonància magnètica
Magnetic resonance imaging
Imatges -- Processament -- Tècniques digitals
Image processing -- Digital techniques
Imatgeria mèdica
Imaging systems in medicine
Title: Information-theoretic approach for automated white matter fiber tracts reconstruction
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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