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Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks

  • Enes Albay
  • Ugur Demir
  • Gozde Unal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11121)

Abstract

Spatial resolution is one of the main constraints in diffusion Magnetic Resonance Imaging (dMRI). Increasing resolution leads to a decrease in SNR of the diffusion images. Acquiring high resolution images without reducing SNRs requires larger magnetic fields and long scan times which are typically not applicable in the clinical settings. Currently feasible voxel size is around 1 mm\( ^{3} \) for a diffusion image. In this paper, we present a deep neural network based post-processing method to increase the spatial resolution in diffusion MRI. We utilize Generative Adversarial Networks (GANs) to obtain a higher resolution diffusion MR image in the spatial dimension from lower resolution diffusion images. The obtained real data results demonstrate a first time proof of concept that GANs can be useful in super-resolution problem of diffusion MRI for upscaling in the spatial dimension.

Keywords

Magnetic resonance imaging (MRI) Diffusion MRI (dMRI) Super resolution Generative adversarial networks (GANs) 

References

  1. 1.
    Calamante, F., Tournier, J.D., Heidemann, R.M., Anwander, A., Jackson, G.D., Connelly, A.: Track density imaging (TDI): validation of super resolution property. Neuroimage 56(3), 1259–1266 (2011)CrossRefGoogle Scholar
  2. 2.
    Descoteaux, M., Deriche, R., Le Bihan, D., Mangin, J.F., Poupon, C.: Multiple q-shell diffusion propagator imaging. Med. Image Anal. Front. Neuroinformatics 15(4), 603–621 (2011)CrossRefGoogle Scholar
  3. 3.
    Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10593-2_13CrossRefGoogle Scholar
  4. 4.
    Garyfallidis, E.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinformatics 8, 8 (2014)CrossRefGoogle Scholar
  5. 5.
    Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates, Inc. (2014)Google Scholar
  6. 6.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arxiv (2016)Google Scholar
  7. 7.
    Johansen-Berg, H., Behrens, T.E.: Diffusion MRI: From Quantitative Measurement to In Vivo Neuroanatomy. Academic Press (2013)Google Scholar
  8. 8.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46475-6_43CrossRefGoogle Scholar
  9. 9.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
  10. 10.
    Le Bihan, D., Basser, P.J.: Molecular diffusion and nuclear magnetic resonance. In: Diffusion and Perfusion Magnetic Resonance Imaging, pp. 5–17 (1995)Google Scholar
  11. 11.
    Le Bihan, D., Iima, M.: Diffusion magnetic resonance imaging: what water tells us about biological tissues. PLoS Biol. 13(7), e1002203 (2015)CrossRefGoogle Scholar
  12. 12.
    Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. CoRR abs/1609.04802 (2016)Google Scholar
  13. 13.
    Odena, A., Dumoulin, V., Olah, C.: Deconvolution and checkerboard artifacts. Distill (2016).  https://doi.org/10.23915/distill.00003, http://distill.pub/2016/deconv-checkerboard
  14. 14.
    Peled, S., Yeshurun, Y.: Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging. Magn. Reson. Med. 45(1), 29–35 (2001)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Van Essen, D.C., et al.: The human connectome project: a data acquisition perspective. Neuroimage 62(4), 2222–2231 (2012)CrossRefGoogle Scholar
  18. 18.
    Yang, Z., He, P., Zhou, J., Wu, X.: Non-local diffusion-weighted image super-resolution using collaborative joint information. Exp. Ther. Med. 15(1), 217–225 (2018)Google Scholar
  19. 19.
    Yap, P.-T., An, H., Chen, Y., Shen, D.: A generative model for resolution enhancement of diffusion MRI data. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 527–534. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40760-4_66CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Istanbul Technical UniversityIstanbulTurkey

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