Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 10–17Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Parametric Dictionary Learning for Modeling EAP and ODF in Diffusion MRI

Parametric Dictionary Learning for Modeling EAP and ODF in Diffusion MRI

  • Sylvain Merlet19,
  • Emmanuel Caruyer19 &
  • Rachid Deriche19 
  • Conference paper
  • 4333 Accesses

  • 14 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)

Abstract

In this work, we propose an original and efficient approach to exploit the ability of Compressed Sensing (CS) to recover Diffusion MRI (dMRI) signals from a limited number of samples while efficiently recovering important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF). Some attempts to sparsely represent the diffusion signal have already been performed. However and contrarly to what has been presented in CS dMRI, in this work we propose and advocate the use of a well adapted learned dictionary and show that it leads to a sparser signal estimation as well as to an efficient reconstruction of very important diffusion features. We first propose to learn and design a sparse and parametric dictionary from a set of training diffusion data. Then, we propose a framework to analytically estimate in closed form two important diffusion features : the EAP and the ODF. Various experiments on synthetic, phantom and human brain data have been carried out and promising results with reduced number of atoms have been obtained on diffusion signal reconstruction, thus illustrating the added value of our method over state-of-the-art SHORE and SPF based approaches.

Keywords

  • Sparse Representation
  • Compressed Sensing
  • Orientation Distribution Function
  • Radial Order
  • Angular Information

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.

Download conference paper PDF

References

  1. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006)

    CrossRef  Google Scholar 

  2. Assemlal, H., Tschumperl, D., Brun, L.: Efficient and robust computation of PDF features from diffusion MR signal. Medical Image Analysis 13(5), 715–729 (2009)

    CrossRef  Google Scholar 

  3. Cands, E., Wakin, M.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)

    CrossRef  Google Scholar 

  4. Cheng, J., Jiang, T., Deriche, R.: Theoretical analysis and practical insights on EAP estimation via a unified HARDI framework. In: MICCAI Workshop CDMRI (2011)

    Google Scholar 

  5. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast, and robust analytical q-ball imaging. Mag. Res. in Med. 58(3), 497–510 (2007)

    CrossRef  Google Scholar 

  6. Elad, M., Matalon, B., Shtok, J., Zibulevsky, M.: A wide-angle view at iterated shrinkage algorithms. In: SPIE - Wavelet XII, vol. 6701, pp. 26–29 (2007)

    Google Scholar 

  7. Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., Yo, T., Mangin, J.F., Poupon, C.: Quantitative analysis of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 56(1), 220–234 (2011)

    CrossRef  Google Scholar 

  8. Ozarslan, E., Koay, C., Shepherd, T., Blackband, S., Basser, P.: Simple harmonic oscillator based reconstruction and estimation for three-dimensional q-space MRI. In: ISMRM, p. 1396 (2009)

    Google Scholar 

  9. Rathi, Y., Michailovich, O., Setsompop, K., Bouix, S., Shenton, M.E., Westin, C.-F.: Sparse Multi-Shell Diffusion Imaging. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 58–65. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  10. Tristán-Vega, A., Westin, C.-F.: Probabilistic ODF Estimation from Reduced HARDI Data with Sparse Regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 182–190. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Athena Project-Team, INRIA Sophia Antipolis, Méditerranée, France

    Sylvain Merlet, Emmanuel Caruyer & Rachid Deriche

Authors
  1. Sylvain Merlet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Caruyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rachid Deriche
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Merlet, S., Caruyer, E., Deriche, R. (2012). Parametric Dictionary Learning for Modeling EAP and ODF in Diffusion MRI. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_2

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33454-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33453-5

  • Online ISBN: 978-3-642-33454-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature