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UNC-Utah NA-MIC framework for DTI fiber tract analysis

Overview of attention for article published in Frontiers in Neuroinformatics, January 2014
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Title
UNC-Utah NA-MIC framework for DTI fiber tract analysis
Published in
Frontiers in Neuroinformatics, January 2014
DOI 10.3389/fninf.2013.00051
Pubmed ID
Authors

Audrey R. Verde, Francois Budin, Jean-Baptiste Berger, Aditya Gupta, Mahshid Farzinfar, Adrien Kaiser, Mihye Ahn, Hans Johnson, Joy Matsui, Heather C. Hazlett, Anuja Sharma, Casey Goodlett, Yundi Shi, Sylvain Gouttard, Clement Vachet, Joseph Piven, Hongtu Zhu, Guido Gerig, Martin Styner

Abstract

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 3 3%
Cuba 2 2%
Canada 2 2%
Brazil 1 1%
Unknown 91 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 31%
Student > Ph. D. Student 15 15%
Student > Doctoral Student 9 9%
Professor 7 7%
Other 7 7%
Other 19 19%
Unknown 11 11%
Readers by discipline Count As %
Psychology 19 19%
Neuroscience 16 16%
Medicine and Dentistry 14 14%
Computer Science 10 10%
Engineering 10 10%
Other 12 12%
Unknown 18 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 March 2014.
All research outputs
#15,295,786
of 22,747,498 outputs
Outputs from Frontiers in Neuroinformatics
#551
of 743 outputs
Outputs of similar age
#189,983
of 305,224 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#17
of 22 outputs
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