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Conclusion

We presented a method to augment single-shell dMRI signals to predict additional shells via a spherical harmonics representation based on a DNN. Our evaluation on both synthetic and human data shows that this augmentation is hardly influenced by the number of gradient directions, but rather depends on the noise level. The presented approach constitutes a first step towards multi-shell HARDI acquisitions in clinical scenarios.

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; 1Ш4 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.

References

  • 1. 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)
  • 2. Basser, PJ., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259 (1994)
  • 3. Canales-Rodriguez, E.J., Melie-Garcia, L., Iturria-Medina, Y.: Mathematical description of q-space in spherical coordinates: exact q-ball imaging. Magn. Reson. Med. 61(6), 1350-1367 (2009)
  • 4. Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2(4), 303-314 (1989)
  • 5. Delalleau, O., Bengio, Y.: Shallow vs. deep sum-product networks. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24, pp. 666-674. Curran Associates, Northampton, MA (2011)
  • 6. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Apparent diffusion coefficients from high angular resolution diffusion imaging: estimation and applications. Magn. Reson. Med. 56(2), 395-410 (2006)
  • 7. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121-2159 (2011)
  • 8. 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), 8 (2014)
  • 9. Golkov, V., Dosovitskiy, A., Samann, P., Sperl, J.I., Sprenger, T., Czisch, M., Menzel, M.I., Gomez, P.A., Haase, A., Brox, T., Cremers, D.: q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans, pp. 37-44. Springer International Publishing, Cham (2015)
  • 10. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251-257 (1991)
  • 11. 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)
  • 12. Ozarslan, E., Koay, C.G., Shepherd, T.M., Komlosh, M.E., Irfanoglu, M.O., Pierpaoli, C., Basser, P.J.: Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. NeuroImage 78, 16-32 (2013)
  • 13. Scherrer, B., Warfield, S.K.: Why multiple b-values are required for multi-tensor models. Evaluation with a constrained log-euclidean model. In: Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 1389-1392. IEEE, New York (2010)
  • 14. Schultz, T.: Learning a Reliable Estimate of the Number of Fiber Directions in Diffusion MRI, pp. 493-500. Springer, Berlin, Heidelberg (2012)
  • 15. Tuch, D.S., Reese, T.G., Wiegell, M.R., Makris, N., Belliveau, J.W., Wedeen, V.J.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48(4), 577-582 (2002)
 
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