Abstract
The brain microstructure consists of the complex organization of cellular structures and extra-cellular space. Insights into this microstructure can be gained in vivo by means of diffusion-weighted imaging that is sensitive to the local patterns of diffusion of water molecules throughout the brain. Diffusion compartment imaging (DCI) provides a separate parameterization for the diffusion signal arising from each compartment of water molecules at each voxel. Their use in population studies and longitudinal monitoring of diseases hold promise for unraveling alterations of the brain microstructure in various disorders and conditions. Yet, to analyze multi-compartment models, high-level operations commonly used in scalar images need to be generalized. We present a framework that enables interpolation, averaging, filtering, spatial normalization and statistical analyses of multi-compartment data with a focus on multi-tensor representations. This framework is based on the generalization of linear combinations of voxel values through mixture simplification. We illustrate the impact of this framework in registration, atlas construction, tractography and population studies.
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Taquet, M., Scherrer, B., Warfield, S.K. (2015). A Framework for the Analysis of Diffusion Compartment Imaging (DCI). In: Hotz, I., Schultz, T. (eds) Visualization and Processing of Higher Order Descriptors for Multi-Valued Data. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-15090-1_14
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DOI: https://doi.org/10.1007/978-3-319-15090-1_14
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