Abstract
We present a technique for mapping dispersion anisotropy of neurites in the human brain in vivo. Neurites are the structural substrate of the brain that support its function. Measures of their morphology from histology provide the gold standard for diagnosing various brain disorders. Some of these measures, e.g. neurite density and orientation dispersion, can now be mapped in vivo using diffusion MRI, enabling their use in clinical applications. However, in vivo methods for estimating more sophisticated measures, such as dispersion anisotropy, have yet to be demonstrated. Dispersion anisotropy allows more refined characterisation of the complex neurite configurations such as fanning or bending axons; its quantification in vivo can offer new imaging markers. The aim of this work is to develop a method to estimate dispersion anisotropy in vivo. Our approach builds on the Neurite Orientation Dispersion and Density Imaging (NODDI), an existing clinically feasible diffusion MRI technique. The estimation of dispersion anisotropy is achieved by incorporating Bingham distribution as the neurite orientation distribution function, with no additional acquisition requirements. We show the first in vivo maps of dispersion anisotropy and demonstrate that it can be estimated accurately with a clinically feasible protocol. We additionally show that the original NODDI is robust to the effects of dispersion anisotropy, when the the new parameter is not of interest.
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Keywords
- Orientation Distribution
- Orientation Distribution Function
- Focal Cortical Dysplasia
- Bayesian Information Criterion
- Dominant Orientation
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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Tariq, M., Schneider, T., Alexander, D.C., Wheeler-Kingshott, C.A.M., Zhang, H. (2014). In vivo Estimation of Dispersion Anisotropy of Neurites Using Diffusion MRI. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8675. Springer, Cham. https://doi.org/10.1007/978-3-319-10443-0_31
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DOI: https://doi.org/10.1007/978-3-319-10443-0_31
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