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Reconstruction of Diffusion Anisotropies Using 3D Deep Convolutional Neural Networks in Diffusion Imaging

  • Simon Koppers
  • Matthias Friedrichs
  • Dorit Merhof
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

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.

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Simon Koppers
    • 1
  • Matthias Friedrichs
    • 1
  • Dorit Merhof
    • 1
  1. 1.Institute of Imaging & Computer VisionRWTH Aachen UniversityAachenGermany

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