Advertisement

Non-local Robust Detection of DTI White Matter Differences with Small Databases

  • Olivier Commowick
  • Aymeric Stamm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)

Abstract

Diffusion imaging, through the study of water diffusion, allows for the characterization of brain white matter, both at the population and individual level. In recent years, it has been employed to detect brain abnormalities in patients suffering from a disease, e.g. from multiple sclerosis (MS). State-of-the-art methods usually utilize a database of matched (age, sex, ...) controls, registered onto a template, to test for differences in the patient white matter. Such approaches however suffer from two main drawbacks. First, registration algorithms are prone to local errors, thereby degrading the comparison results. Second, the database needs to be large enough to obtain reliable results. However, in medical imaging, such large databases are hardly available. In this paper, we propose a new method that addresses these two issues. It relies on the search for samples in a local neighborhood of each pixel to increase the size of the database. Then, we propose a new test based on these samples to perform a voxelwise comparison of a patient image with respect to a population of controls. We demonstrate on simulated and real MS patient data how such a framework allows for an improved detection power and a better robustness and reproducibility, even with a small database.

Keywords

Multiple Sclerosis Multiple Sclerosis Patient Registration Error Registration Algorithm Small Database 
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.

References

  1. 1.
    Rovaris, M., Gass, A., Bammer, R., Hickman, S.J., Ciccarelli, O., Miller, D., Filippi, M.: Diffusion MRI in multiple sclerosis. Neurology 65(10), 1526–1532 (2005)CrossRefGoogle Scholar
  2. 2.
    Filippi, M., Cercignani, M., Inglese, M., Comi, M.H.G.: Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 56, 304–311 (2001)CrossRefGoogle Scholar
  3. 3.
    Lepore, N., Brun, C.A., Chou, Y.Y., Chiang, M.C., et al.: Generalized tensor-based morphometry of HIV/AIDS using multivariate statistics on deformation tensors. IEEE Transactions on Medical Imaging 27(1), 129–141 (2008)CrossRefGoogle Scholar
  4. 4.
    Whitcher, B., Wisco, J.J., Hadjikhani, N., Tuch, D.S.: Statistical group comparison of diffusion tensors via multivariate hypothesis testing. Magnetic Resonance in Medicine (57), 1065–1074 (2007)CrossRefGoogle Scholar
  5. 5.
    Commowick, O., Fillard, P., Clatz, O., Warfield, S.K.: Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 975–982. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Goh, A., Lenglet, C., Thompson, P., Vidal, R.: A nonparametric riemannian framework for processing high angular resolution diffusion images and its applications to ODF-based morphometry. Neuroimage 56, 1181–1201 (2011)CrossRefGoogle Scholar
  7. 7.
    Guimond, A., Meunier, J., Thirion, J.P.: Average brain models: A convergence study. Computer Vision and Image Understanding 77(2), 192–210 (2000)CrossRefGoogle Scholar
  8. 8.
    Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise non-local means denoising filter for 3D magnetic resonance images. IEEE Transactions on Medical Imaging 27(4), 325–441 (2008)CrossRefGoogle Scholar
  9. 9.
    Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRefGoogle Scholar
  10. 10.
    Buades, A., Coll, B., Morel, J.M.: A review of image denoising, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magnetic Resonance in Medicine 56(2), 411–421 (2006)CrossRefGoogle Scholar
  12. 12.
    Anderson, T.: An introduction to multivariate statistical analysis. Wiley (2003)Google Scholar
  13. 13.
    Ourselin, S., Roche, A., Prima, S., Ayache, N.: Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 557–566. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Rueckert, D., Sonoda, L.L., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  15. 15.
    Commowick, O., Warfield, S.K.: A continuous STAPLE for scalar, vector and tensor images: An application to DTI analysis. IEEE Transactions on Medical Imaging 28(6), 838–846 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Olivier Commowick
    • 1
  • Aymeric Stamm
    • 1
  1. 1.VISAGES: INSERM U746 - CNRS UMR6074 - INRIA - Univ. of Rennes IFrance

Personalised recommendations