NDLI: Information-Theoretic Approach for Automated White Matter Fiber Tracts Reconstruction

Content Provider	Springer Nature Link
Author	Prados, Ferran Boada, Imma Feixas, Miquel Prats Gali, Alberto Blasco, Gerard Puig, Josep Pedraza, Salvador
Copyright Year	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 automatically 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.
Starting Page	305
Ending Page	318
Page Count	14
File Format	PDF
ISSN	15392791
Journal	Neuroinformatics
Volume Number	10
Issue Number	3
e-ISSN	15590089
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-03-31
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Diffusion MRI Tractography Seeding White matter Computer Application in Life Sciences Neurology Computational Biology/Bioinformatics Bioinformatics Neurosciences
Content Type	Text
Resource Type	Article
Subject	Neuroscience Information Systems Software

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