Abstract
To relate diffusion-weighted MRI-signal to the underlying tissue structure remains one of the major challenges in interpreting experimental data, in particular for reconstruction of the structural connectivity in the human brain. Various ideas to tackle this problem are around, either model-based or model-free. We proceed on a third way by proposing a method that automatically determines the basis components of diffusion-weighted MRI signal without any usage of prior knowledge. The resulted components can be well associated with white matter, gray matter and cerebrospinal fluid, respectively. The performance of our method is demonstrated on two DSI datasets and one multi-shell acquisition.
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Acknowledgements
We want to thank Alessandro Daducci from the Ecole Polytechnique Federale de Lausanne for kindly providing the QSI measurements. The work of Marco Reisert is supported by the DFG (Deutsche Forschungsgemeinschaft) grant RE 32-86/2-1.
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Reisert, M., Skibbe, H., Kiselev, V.G. (2014). The Diffusion Dictionary in the Human Brain Is Short: Rotation Invariant Learning of Basis Functions. In: Schultz, T., Nedjati-Gilani, G., Venkataraman, A., O'Donnell, L., Panagiotaki, E. (eds) Computational Diffusion MRI and Brain Connectivity. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-02475-2_5
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DOI: https://doi.org/10.1007/978-3-319-02475-2_5
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