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

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


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.



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.


  1. 1.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). Software available from
  2. 2.
    Alexander, D.C., Zikic, D., Zhang, J., Zhang, H., Criminisi, A.: Image Quality Transfer via Random Forest Regression: Applications in Diffusion MRI, pp. 225–232. Springer International Publishing, Cham (2014)Google Scholar
  3. 3.
    Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., Descoteaux, M., Nimmo-Smith, I.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8(8), 1–8 (2014)Google Scholar
  4. 4.
    Golkov, V., Dosovitskiy, A., Sämann, P., Sperl, J., Sprenger, T., Czisch, M., Menzel, M., Gómez, P., Haase, A., Brox, T., Cremers, D.: q-Space deep learning for twelve-fold shorter and model-free diffusion MRI scans. In: MICCAI, pp. 37–44. Springer International Publishing, Cham (2015)Google Scholar
  5. 5.
    Jeurissen, B., Leemans, A., Tournier, J.D., Jones, D.K., Sijbers, J.: Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain. Mapp. 34(11), 2747–2766 (2013)CrossRefGoogle Scholar
  6. 6.
    Koppers, S., Haarburger, C., Merhof, D.: Diffusion MRI signal augmentation - from single shell to multi shell with deep learning. In: MICCAI Workshop on Computational Diffusion MRI. Springer International Publishing, Cham (2016)Google Scholar
  7. 7.
    Koppers, S., Merhof, D.: Direct estimation of fiber orientations using deep learning in diffusion imaging. In: MICCAI Workshop on Machine Learning in Medical Imaging. Springer International Publishing, Cham (2016)CrossRefGoogle Scholar
  8. 8.
    Koppers, S., Merhof, D.: Qualitative Comparison of Reconstruction Algorithms for Diffusion Imaging, chap. 4, pp. 51–67. Der Andere Verlag, Uelvesbüll (2016)Google Scholar
  9. 9.
    Schultz, T.: Learning a reliable estimate of the number of fiber directions in diffusion MRI. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012: 15th International Conference, Nice, October 1–5, 2012, Proceedings, Part III, pp. 493–500. Springer, Berlin, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Schultz, T., Westin, C.F., Kindlmann, G.: Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010: 13th International Conference, Beijing, September 20–24, 2010, Proceedings, Part I, pp. 674–681. Springer, Berlin, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472 (2007). doi:10.1016/j.neuroimage.2007.02.016CrossRefGoogle Scholar
  12. 12.
    Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23(3), 1176–1185 (2004). doi:10.1016/j.neuroimage.2004.07.037CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations