Improving Diffusion Tensor Estimation Using Adaptive and Optimized Filtering Based on Local Similarity

  • Andrés F. López-Lopera
  • Mauricio A. Álvarez
  • Álvaro Á. Orozco
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)

Abstract

The diffusion-weighted magnetic resonance imaging (DW-MRI) has been used to diagnose anomalies in human brain by describing the magnitude and directionality of water diffusion per voxel. Such information can be represented alternatively in diffusion tensor imaging (DT-MRI), yielding images of normal and abnormal white matter fiber structures, and maps of brain connectivity through fiber tracking. A DW-MRI study is usually characterized by a low signal to noise ratio, which may reflect in the poor estimation of DT-MRI. Filters based on local similarity have been receiving increasing attention, but they have been barely studied for DT-MRI. In this proposal we introduce adaptive and optimized filtering techniques based on local similarity for MRI to remove the biasing in both DW-MRI filtering and DT-MRI estimation, evidencing a better performance respect to classical filters and robust DT estimation algorithms. We estimate the DT-MRI extracting metrics computed from the DT to evaluate the filtering performance.

Keywords

Adaptive and optimized filtering Diffusion tensor imaging Diffusion-weighted magnetic resonance imaging 
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Notes

Acknowledgment

This work was funded by COLCIENCIAS under the project 1110-569-34461. Authors were also supported by the 617 agreement, “Jóvenes Investigadores e Innovadores”, funded by COLCIENCIAS. Finally, the authors are thankful to the research group in Automática ascribed to the engineering program at the Universidad Tecnológica de Pereira, and M.Sc. H.F. García for technical support.

References

  1. 1.
    Manjón, J.V., Coupé, P., Martí-Bonmatí, L., Collins, D.L., Robles, M.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31(1), 192–203 (2010)CrossRefGoogle Scholar
  2. 2.
    Manjón, J.V., Coupé, P., Concha, L., Buades, A., Collins, D.L., Robles, M.: Diffusion weighted image denoising using overcomplete local PCA. PLoS ONE 8(9), 1–12 (2013)CrossRefGoogle Scholar
  3. 3.
    Butson, C.R., Cooper, S.E., Henderson, J.M., Wolgamuth, B., McIntyre, C.C.: Probabilistic analysis of activation volumes generated during deep brain stimulation. NeuroImage 54(3), 2096–2104 (2011)CrossRefGoogle Scholar
  4. 4.
    Lee, J.E., Chung, M.K., Alexander, A.L.: Evaluation of anisotropic filters for diffusion tensor imaging. In: ISBI, pp. 77–78. IEEE (2006)Google Scholar
  5. 5.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  6. 6.
    Manjón, J.V., Coupé, P., Buades, A., Louis Collins, D., Robles, M.: New methods for MRI denoising based on sparseness and self-similarity. Med. Image Anal. 16(1), 18–27 (2012)CrossRefGoogle Scholar
  7. 7.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. In: CVPR 2005, vol. 2, pp. 60–65, June 2005Google Scholar
  8. 8.
    Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S.P., Barillot, C.: Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: applications to DT-MRI. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 171–179. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  9. 9.
    Guleryuz, O.: Weighted averaging for denoising with overcomplete dictionaries. IEEE Trans. Image Process. 16(12), 3020–3034 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Coupé, P., Manjón, J.V., Gedamu, E., Arnold, D., Robles, M., Collins, D.L.: Robust rician noise estimation for MR images. Med. Image Anal. 14(4), 483–493 (2010)CrossRefGoogle Scholar
  11. 11.
    Phillip, K.P., Wei-Ren, Ng., Varun, S.: Image denoising with singular value decomposition and principal component analysis. The University of Arizona, pp. 1–29 (2009). http://www.u.arizona.edu/~ppoon/ImageDenoisingWithSVD.pdf
  12. 12.
    Niethammer, M., Estepar, R., Bouix, S., Shenton, M., Westin, C.F.: On diffusion tensor estimation. In: EMBS 2006, pp. 2622–2625, August 2006Google Scholar
  13. 13.
    Stejskal, E.O., Tanner, J.E.: Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42(1), 288–292 (1965)CrossRefGoogle Scholar
  14. 14.
    Barmpoutis, A.: Tutorial on Diffusion Tensor MRI using Matlab. University of Florida (2010)Google Scholar
  15. 15.
    Chang, L.C., Jones, D.K., Pierpaoli, C.: Restore: Robust estimation of tensors by outlier rejection. Magn. Reson. Med. 53, 1088–1095 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrés F. López-Lopera
    • 1
  • Mauricio A. Álvarez
    • 1
  • Álvaro Á. Orozco
    • 1
  1. 1.Electrical Engineering ProgramUniversidad Tecnológica de PereiraPereiraColombia

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