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Rician Noise Removal in Diffusion Tensor MRI

  • Saurav Basu
  • Thomas Fletcher
  • Ross Whitaker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

Abstract

Rician noise introduces a bias into MRI measurements that can have a significant impact on the shapes and orientations of tensors in diffusion tensor magnetic resonance images. This is less of a problem in structural MRI, because this bias is signal dependent and it does not seriously impair tissue identification or clinical diagnoses. However, diffusion imaging is used extensively for quantitative evaluations, and the tensors used in those evaluations are biased in ways that depend on orientation and signal levels. This paper presents a strategy for filtering diffusion tensor magnetic resonance images that addresses these issues. The method is a maximum a posteriori estimation technique that operates directly on the diffusion weighted images and accounts for the biases introduced by Rician noise. We account for Rician noise through a data likelihood term that is combined with a spatial smoothing prior. The method compares favorably with several other approaches from the literature, including methods that filter diffusion weighted imagery and those that operate directly on the diffusion tensors.

Keywords

Fractional Anisotropy Gradient Direction Error Metrics Diffusion Tensor Magnetic Resonance Image Likelihood Term 
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.
    Pierpaoli, C., Basser, P.: Toward a quantitative assessment of diffusion anisotropy. Magnetic Resonance in Medicine 36(6), 893–906 (1996)CrossRefGoogle Scholar
  2. 2.
    Anderson, A.W.: Theoretical analysis of the effects of noise on diffusion tensor imaging. Magnetic Resonance in Medicine 46(6), 1174–1188 (2001)CrossRefGoogle Scholar
  3. 3.
    Skare, S., Li, T., Nordell, B., Ingvar, M.: Noise considerations in the determination of diffusion tensor anisotropy. Magnetic Resonance Imaging 18(6), 659–669 (2000)CrossRefGoogle Scholar
  4. 4.
    Parker, G.J.: Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging. J. Magn. Reson. Imaging 11(6), 702–710 (2000)CrossRefGoogle Scholar
  5. 5.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis Machine Intelligence 17(4), 629–639 (1990)CrossRefGoogle Scholar
  6. 6.
    Wang, Z., Vemuri, B., Chen, Y., Mareci, T.: A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI. IEEE Transactions on Medical Imaging 23(8), 930–939 (2004)CrossRefGoogle Scholar
  7. 7.
    Martin Fernandez, C.F., Westin, C.A.L.: 3d bayesian regularization of diffusion tensor mri using multivariate gaussian markov random fields. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 351–359. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Pennec, X., Fillard, P., Ayache, N.: A riemannian framework for tensor computing. International Journal of Computer Vision 66(1), 41–66 (2006)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Fillard, P., Arsigny, V., Pennec, X., Ayache, N.: Clinical DT-MRI estimation, smoothing and fiber tracking with log-Euclidean metrics. In: Proceedings of the Third IEEE International Symposium on Biomedical Imaging (ISBI 2006), Crystal Gateway Marriott, Arlington, Virginia, USA, pp. 786–789 (2006)Google Scholar
  10. 10.
    Basser, P., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Mag Res. 111(3), 209–219 (1996)CrossRefGoogle Scholar
  11. 11.
    Sijbers, J., den Dekker, A., Scheunders, P., Dyck, D.V.: Maximum-Likelihood Estimation of Rician Distribution Parameters. IEEE Transactions on Medical Imaging 17(3), 357–361 (1998)CrossRefGoogle Scholar
  12. 12.
    Kindlmann, G.: Superquadric tensor glyphs. In: Proceedings of IEEE TVCG/EG Symposium on Visualization 2004, pp. 147–154 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Saurav Basu
    • 1
  • Thomas Fletcher
    • 1
  • Ross Whitaker
    • 1
  1. 1.School of ComputingUniversity of UtahSalt Lake CityUSA

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