UNC-Utah NA-MIC DTI framework: atlas based fiber tract analysis with application to a study of nicotine smoking addiction

Audrey R. Verde; Jean-Baptiste Berger; Aditya Gupta; Mahshid Farzinfar; Adrien Kaiser; Vicki W. Chanon; Charlotte Boettiger; Hans Johnson; Joy Matsui; Anuja Sharma; Casey Goodlett; Yundi Shi; Hongtu Zhu; Guido Gerig; Sylvain Gouttard; Clement Vachet; Martin Styner

doi:10.1117/12.2007093

13 March 2013 UNC-Utah NA-MIC DTI framework: atlas based fiber tract analysis with application to a study of nicotine smoking addiction

Audrey R. Verde, Jean-Baptiste Berger, Aditya Gupta, Mahshid Farzinfar, Adrien Kaiser, Vicki W. Chanon, Charlotte Boettiger, Hans Johnson, Joy Matsui, Anuja Sharma, Casey Goodlett, Yundi Shi, Hongtu Zhu, Guido Gerig, Sylvain Gouttard, Clement Vachet, Martin Styner

Author Affiliations +

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86692D (2013) https://doi.org/10.1117/12.2007093
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Purpose: The UNC-Utah NA-MIC DTI framework represents a coherent, open source, atlas fiber tract based DTI analysis framework that addresses the lack of a standardized fiber tract based DTI analysis workflow in the field. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. Data: We illustrate the use of our framework on a 54 directional DWI neuroimaging study contrasting 15 Smokers and 14 Controls. Method(s): At the heart of the framework is a set of tools anchored around the multi-purpose image analysis platform 3D-Slicer. Several workflow steps are handled via external modules called from Slicer in order to provide an integrated approach. Our workflow starts with conversion from DICOM, followed by thorough automatic and interactive quality control (QC), which is a must for a good DTI study. Our framework is centered around a DTI atlas that is either provided as a template or computed directly as an unbiased average atlas from the study data via deformable atlas building. Fiber tracts are defined via interactive tractography and clustering on that atlas. DTI fiber profiles are extracted automatically using the atlas mapping information. These tract parameter profiles are then analyzed using our statistics toolbox (FADTTS). The statistical results are then mapped back on to the fiber bundles and visualized with 3D Slicer. Results: This framework provides a coherent set of tools for DTI quality control and analysis. Conclusions: This framework will provide the field with a uniform process for DTI quality control and analysis.

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Audrey R. Verde, Jean-Baptiste Berger, Aditya Gupta, Mahshid Farzinfar, Adrien Kaiser, Vicki W. Chanon, Charlotte Boettiger, Hans Johnson, Joy Matsui, Anuja Sharma, Casey Goodlett, Yundi Shi, Hongtu Zhu, Guido Gerig, Sylvain Gouttard, Clement Vachet, and Martin Styner "UNC-Utah NA-MIC DTI framework: atlas based fiber tract analysis with application to a study of nicotine smoking addiction", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692D (13 March 2013); https://doi.org/10.1117/12.2007093

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