Abstract
Diffusion tensor imaging (DTI) is sensitive to micron scale displacement of water molecules, providing unique insight into microstructural tissue architecture. The tensors provide a compact way to describe the average of these displacements that occur within a voxel. However, current practical image resolution is in the millimeter scale, and thus diffusivities from many tissue compartments are averaged in each voxel, reducing the specificity of the measurement to subtle pathologies. In this chapter we review the free-water model, and use it to derive diffusion tensors following the elimination of the free-water component, that is assumed to originate from the extracellular space. Doing so, the resulting diffusion tensors and their derived indices measure the tissue itself, and are more sensitive to the geometry of the tissue, increasing the specificity to pathologies that affect brain tissue.
Keywords
- Fractional Anisotropy
- Diffusion Tensor Imaging
- Kalman Filter
- Radial Diffusivity
- Diffusion Tensor Imaging Data
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgements
This work was supported by the following grants: Department of Defense X81XWH-07-CC-CSDoD; NIH P41RR013218, P41EB015902, NIH R01MH074794; VA Merit Award. OP is partly supported by a National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Grant from the Brain and Behavior Research Foundation.
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Pasternak, O., Maier-Hein, K., Baumgartner, C., Shenton, M.E., Rathi, Y., Westin, CF. (2014). The Estimation of Free-Water Corrected Diffusion Tensors. In: Westin, CF., Vilanova, A., Burgeth, B. (eds) Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54301-2_11
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