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Towards Regional Elastography of Intracranial Aneurysms

  • Simone Balocco
  • Oscar Camara
  • Alejandro F. Frangi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

Weak spots in the aneurysm could be identified estimating the regional stiffness of the wall. Our approach consists in defining a parametric biomechanical model of the vessel which, given the patient’s vascular morphology and the blood in- and outflow obtained from non-invasive imaging as well as parameters describing the local elasticity of the wall, enables the computation of the theoretical deformed wall position. The distance between this latter and the one obtained from the aneurysm pulsation is iteratively minimized in order to estimate the optimal set of stiffness parameters. In order to reduce the number of variables to estimate, the aneurysm morphology is clustered into a limited number of regions with uniform stiffness. A random noise perturbation (<5mm) is applied to the reference deformations and strains, showing that the robustness of the clustering decreases to 75% and errors of the stiffness estimates remain below 10% of the reference values.

Keywords

Intracranial Aneurysm Principal Strain Biomechanical Model Systolic Peak Aneurysm Surface 
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.
    Hayakawa, M., Katada, K., Anno, H., Imizu, S., Hayashi, J., Irie, K., Negoro, M., Kato, Y., Kanno, T., Sano, H.: CT Angiography with Electrocardiographically Gated Reconstruction for Visualizing Pulsation of Intracranial Aneurysms: Identification of Aneurysmal Protuberance Presumably Associated with Wall Thinning. Am. J. Neuroradiol. 26, 1366–1369 (2005)Google Scholar
  2. 2.
    Zhang, X., Kinnick, R., Fatemi, M., Greenleaf, J.: Noninvasive Method for Estimation of Complex Elastic Modulus of Arterial Vessels. IEEE Trans. Ultrason. Ferroel. Freq. Contr. 52, 642–652 (2005)CrossRefGoogle Scholar
  3. 3.
    Kroon, M., Holzapfel, G.A.: Estimation Of The Distributions Of Anisotropic, Elastic Properties And Wall Stresses Of Saccular Cerebral Aneurysms By Inverse Analysis. Proc. R. Soc. Lond. A. (in press)Google Scholar
  4. 4.
    Argyris, J.H., Fried, I., Scharpf, D.W.: The TUBA family of plate elements for the matrix displacement method. J. Roy. Aeronaut. 72, 701–709 (1968)Google Scholar
  5. 5.
    Hartigan, J.A.: Clustering Algorithms. John Wiley & Sons, New York (1975)zbMATHGoogle Scholar
  6. 6.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, New York (1990)Google Scholar
  7. 7.
    Nelder, J.A., Mead, R.: A Simplex Method for Function Minimization. Computer Journal 7, 308–313 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Schweiger, M., Camara-Rey, O., Crum, W.R., Lewis, E., Schnabel, J.A., Arridge, S.R., Hill, D.L.G., Fox, N.C.: An Inverse Problem Approach to the Estimation of Volume Change. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 616–623. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Hernandez, M., Frangi, A.F.: Non-parametric Geodesic Active Regions: Method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA. Med. Image Anal. 11, 224–241 (2007)CrossRefGoogle Scholar
  10. 10.
    Alastruey, J., Parker, K., Peiró, J., Byrd, S., Sherwin, S.: Modelling the circle of Willis to assess the effects of anatomical variations and occlusions on cerebral flows. J. Biomech. 40, 1794–1805 (2007)CrossRefGoogle Scholar
  11. 11.
    MacDonald, D.J., Finlay, H.M., Canham, P.B.: Directional Wall Strength in Saccular Brain Aneurysms from Polarized Light Microscopy. Annals of Biomedical Engineering 28, 533–542 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Simone Balocco
    • 1
    • 2
  • Oscar Camara
    • 1
    • 2
  • Alejandro F. Frangi
    • 1
    • 2
  1. 1.Center for Computational Imaging & Simulation Technologies in BiomedicineUniversitat Pompeu FabraBarcelonaSpain
  2. 2.Networking Center on Biomedical Research (CIBER-BBN)BarcelonaSpain

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