Reconstruction of diffusion anisotropies using 3d deep convolutional neural networks in diffusion imaging
Koppers S, Friedrichs M, Merhof D (2017)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2017
Publisher: Springer Heidelberg
Series: Mathematics and Visualization
Book Volume: 0
Pages Range: 393-404
DOI: 10.1007/978-3-319-61358-1_17
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.
Involved external institutions
Germany (DE)How to cite
APA:
Koppers, S., Friedrichs, M., & Merhof, D. (2017). Reconstruction of diffusion anisotropies using 3d deep convolutional neural networks in diffusion imaging. In (pp. 393-404). Springer Heidelberg.
MLA:
Koppers, Simon, Matthias Friedrichs, and Dorit Merhof. "Reconstruction of diffusion anisotropies using 3d deep convolutional neural networks in diffusion imaging." Springer Heidelberg, 2017. 393-404.
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