Abstract
There are many approaches for extracting useful visual and quantitative information from diffusion MRI imaging data reconstructed using the diffusion tensor imaging (DTI) method, including whole-brain, regional, and voxel-based approaches. However, the broad range of analysis approaches available and heterogeneous functionality in software tools developed to apply them make analyzing and interpreting DTI data particularly challenging.
This chapter provides an overview of the major DTI analysis techniques, including region-of-interest, tractography, and voxel-based analysis (VBA), as well as their strengths and weaknesses. Additionally, the chapter provides practical guidance on considerations that should be taken into account when choosing how to analyze DTI data, supported by a visual decision scheme and software checklist.
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Jones DK, Knösche TR, Turner R. White matter integrity, fibre count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage. 2013;73:239–54.
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Acknowledgments
We would like to thank Ann Van De Winckel, Thibo Billiet, Alexander Leemans, and Dirk Smeets for providing us with some of the images and data sets that were used to create the figures in this chapter.
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Van Hecke, W., Emsell, L. (2016). Strategies and Challenges in DTI Analysis. In: Van Hecke, W., Emsell, L., Sunaert, S. (eds) Diffusion Tensor Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3118-7_8
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DOI: https://doi.org/10.1007/978-1-4939-3118-7_8
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