Advertisement

Non-local MRI Library-Based Super-Resolution: Application to Hippocampus Subfield Segmentation

  • Jose E. Romero
  • Pierrick Coupé
  • Jose V. Manjón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9993)

Abstract

Magnetic Resonance Imaging (MRI) has become one of the most used techniques in research and clinical settings. One of the limiting factors of the MRI is the relatively low resolution for some applications. Although new high resolution MR sequences have been proposed recently, usually these acquisitions require long scanning times which is not always possible neither desirable. Recently, super-resolution techniques have been proposed to alleviate this problem by inferring the underlying high resolution images from low resolution acquisitions. We present a new super-resolution technique that takes benefit from the self-similarity properties of the images and the use of a high resolution image library. The proposed method is compared with related state-of-the-art methods showing a significant reconstruction improvement. Finally, we show the advantage of the proposed framework compared to classic interpolation when used for segmentation of hippocampus subfields.

Keywords

High Resolution Image Segmentation Accuracy Montreal Neurological Institute Space Single Magnetic Resonance Imaging Correct Intensity Inhomogeneity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was partially supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”.

References

  1. 1.
    Braak, H., Braak, E.: Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82(4), 239–259 (1991)CrossRefGoogle Scholar
  2. 2.
    Thévenaz, P., Blu, T., Unser, M.: Interpolation revisited. IEEE Trans. Med. Imaging 19(7), 739–758 (2000)CrossRefGoogle Scholar
  3. 3.
    Carmi, E., Liu, S., Alon, N., Fiat, A., Fiat, D.: Resolution enhancement in MRI. Magn. Reson. Imaging 24(2), 133–154 (2006)CrossRefGoogle Scholar
  4. 4.
    Manjón, J.V., Coupé, P., Buades, A., Fonov, V., Collins, D.L., Robles, M.: Non-local MRI upsampling. Med. Image Anal. 14, 784–792 (2010)CrossRefGoogle Scholar
  5. 5.
    Manjón, J.V., Coupé, P., Buades, A., Collins, D.L., Robles, M.: MRI superresolution using self-similarity and image priors. Int. J. Biomed. Imaging 2010, 17 (2010)CrossRefGoogle Scholar
  6. 6.
    Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 60–65 (2005)Google Scholar
  7. 7.
    Coupé, P., Manjon, J.V., Chamberland, M., Descoteaux, M.: Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage 83, 245–261 (2013)CrossRefGoogle Scholar
  8. 8.
    Manjón, J.V., Coupé, P., Martí-Bonmatí, L., Robles, M., Collins, L.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010)CrossRefGoogle Scholar
  9. 9.
    Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRefGoogle Scholar
  10. 10.
    Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight J. 2, 1–35 (2009)Google Scholar
  11. 11.
    Giraud, R., Ta, V.-T., Papadakis, N., Manjón, J.V., Collins, D.L., Coupé, P., ADNI: An optimized PatchMatch for multi-scale and multi-feature label fusion. NeuroImage 124, 770–782 (2016)CrossRefGoogle Scholar
  12. 12.
    Winterburn, L., Pruessner, J.C., Chavez, S., Schira, M., Lobaugh, N., Voineskos, A.M., Chakravarty, M.: A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging. NeuroImage 74, 254–265 (2013)CrossRefGoogle Scholar
  13. 13.
    Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13(4), 716–724 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jose E. Romero
    • 1
  • Pierrick Coupé
    • 2
    • 3
  • Jose V. Manjón
    • 1
  1. 1.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones AvanzadasUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.University of Bordeaux, LaBRI, UMR 5800, PICTURATalenceFrance
  3. 3.CNRS, LaBRI, UMR 5800, PICTURATalenceFrance

Personalised recommendations