Abstract
The reconstruction of neural pathways is a challenging problem in case of crossing or kissing neuronal fibers. High angular resolution diffusion imaging models are required to identify multiple fiber orientations in a voxel. Disadvantage of those models is that they require a multitude of acquired gradient directions, otherwise these models become inaccurate. We present a new approach to derive the fiber orientation distribution function using a Deep Convolutional Neural Network, which remains stable, even if less gradient directions are acquired. In addition, the Convolutional Neural Network is able to improve the signal in a voxel by extracting useful information of surrounding neighboring voxels. Subsequently, the functionality of the network is evaluated using 100 different brain datasets from the Human Connectome Project.
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Acknowledgements
This work was supported by the International Research Training Group (IRTG 2150) of the German Research Foundation (DFG).
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54 MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
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Koppers, S., Friedrichs, M., Merhof, D. (2017). Reconstruction of Diffusion Anisotropies Using 3D Deep Convolutional Neural Networks in Diffusion Imaging. In: Schultz, T., Özarslan, E., Hotz, I. (eds) Modeling, Analysis, and Visualization of Anisotropy. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-61358-1_17
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DOI: https://doi.org/10.1007/978-3-319-61358-1_17
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