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Cerebral Aneurysms: A Patient-Specific and Image-Based Management Pipeline

  • M. C. Villa-UriolEmail author
  • I. Larrabide
  • J. M. Pozo
  • M. Kim
  • M. De Craene
  • O. Camara
  • C. Zhang
  • A. J. Geers
  • H. Bogunović
  • H. Morales
  • A. F. Frangi
Chapter
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 19)

Abstract

This work presents an image- and biomechanics-based data processing pipeline able to build patient-specific models of cerebral aneurysms. The pipeline also contemplates the virtual modeling and release of endovascular devices such as stents and coils. As a result of the morphological, morphodynamic, hemodynamic and structural analyses, a set of complex descriptors relevant for aneurysm’s diagnosis and prognosis is derived. On the one hand these will bring an insight into the processes behind aneurysm genesis, growth and rupture. On the other one, the inclusion of virtual devices enables the in silicopersonalized evaluation of alternative treatment scenarios before intervention and constitutes a valuable tool for the industrial design of more effective devices. Several of its components have been evaluated in terms of robustness and accuracy. The next step should comprehensively assess the complete pipeline, also proving its clinical value. This pipeline illustrates some of the ideas behind the Virtual Physiological Human (VPH) and the integration of complex data for a better understanding of human physiology in health, disease and its treatment.

Keywords

Cerebral aneurysms Image segmentation Image processing Morphology Morphodynamics Haemodynamics Computational physiology Structural mechanics Virtual treatment Virtual physiological human 

Notes

Acknowledgements

This work was partially supported by the @neurIST Integrated Project (co-financed by the European Commission through the contract no. IST-027703), the CDTI CENIT-CDTEAM grant funded by the Spanish Ministry of Science and Innovation (MICINN-CDTI) and Philips Healthcare (Best, The Netherlands). The authors would also like to thank the support provided by ANSYS Inc.(Canonsburg, PA, USA).

References

  1. 1.
    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(8), 1794–1805 (2007)CrossRefGoogle Scholar
  2. 2.
    Antiga, L., Piccinelli, M., Botti, L., Ene-Iordache, B., Remuzzi, A., Steinman, D.: An image-based modeling framework for patient-specific computational hemodynamics. Med. Biol. Eng. Comput. 46(11), 1097–1112 (2008)CrossRefGoogle Scholar
  3. 3.
    Arbona, A., Benkner, S., Engelbrecht, G., Fingberg, J., Hofmann, M., Kumpf, K., Lonsdale, G., Woehrer, A.: A service-oriented grid infrastructure for biomedical data and compute services. IEEE Transactions on NanoBioscience 6(2), 136–141 (2007)CrossRefGoogle Scholar
  4. 4.
    Arbona, A., Benkner, S., Fingberg, J., Frangi, A.F., Hofmann, M., Hose, D.R., Lonsdale, G., Ruefenacht, D., Viceconti, M.: Outlook for grid service technologies within the @neurIST eHealth environment. Stud. Health. Technol. Informat. 120, 401–404 (2006)Google Scholar
  5. 5.
    Balocco, S., Camara, O., Frangi, A.F.: Towards regional elastography of intracranial aneurysms. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI. Lecture Notes on Computer Science, vol.5242, pp.131–138. Springer, Berlin, Heidelberg, New York, USA (2008)Google Scholar
  6. 6.
    Balocco, S., Camara, O., Vivas, E., Sola, T., Guimaraens, L., Gratama van Adel, H., Majoie, C., Pozo, J., Bijnens, B.H., Frangi, A.F.: Feasibility of estimating regional mechanical properties of cerebral aneurysms in vivo. Med. Phys. 37, 1689–1706 (2010)Google Scholar
  7. 7.
    Blanco, P.J., Feijóo, R.A., Urquiza, S.A.: A unified variational approach for coupling 3D-1D models and its blood flow applications. Comput. Meth. Appl. Math. 196(41–44), 4391–4410 (2007)zbMATHGoogle Scholar
  8. 8.
    Bogunović, H., Radaelli, A., De Craene, M., Delgado, D., Frangi, A.F.: Image intensity standardization in 3D rotational angiography and its application to vascular segmentation. In: SPIE Medical Imaging 2008: Image Processing, vol.6914, p.691419 (2008)Google Scholar
  9. 9.
    Boussel, L., Rayz, V., McCulloch, C., Martin, A., Acevedo-Bolton, G., Lawton, M., Higashida, R., Smith, W.S., Young, W.L., Saloner, D.: Aneurysm growth occurs at region of low wall shear stress: Patient-specific correlation of hemodynamics and growth in a longitudinal study. Stroke 39(11), 2997–3002 (2008)CrossRefGoogle Scholar
  10. 10.
    Brisman, J., Song, J., Newell, D.: Medical progress: cerebral aneurysms. New Engl. J. Med. 355(9), 928–939 (2006)CrossRefGoogle Scholar
  11. 11.
    Brisman, J., Song, J., Niimi, Y., Berenstein, A.: Treatment options for wide-necked intracranial aneurysms using a self-expandable hydrophilic coil and a self-expandable stent combination. Am. J. Neuroradiol. 26(5), 1237–1240 (2005)Google Scholar
  12. 12.
    Calamante, F., Yim, P., Cebral, J.R.: Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics. Neuroimage 19(2), 341–353 (2003)CrossRefGoogle Scholar
  13. 13.
    Castro, M.A., Putman, C.M., Cebral, J.R.: Patient-specific computational modeling of cerebral aneurysms with multiple avenues of flow from 3D rotational angiography images. Acad. Radiol. 13(7), 811–821 (2006)CrossRefGoogle Scholar
  14. 14.
    Cebral, J.R., Castro, M.A., Appanaboyina, S., Putman, C.M., Millan, D., Frangi, A.F.: Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity. IEEE Trans. Med. Imag. 24(4), 457–467 (2005)CrossRefGoogle Scholar
  15. 15.
    Cebral, J.R., Castro, M.A., Satoh, T., Burgess, J.: Evaluation of image-based CFD models of cerebral aneurysm using MRI. In: ISMRM Flow Motion Workshop, Zurich, Switzerland, pp.11–13 (2004)Google Scholar
  16. 16.
    Cebral, J.R., Löhner, R.: Efficient simulation of blood flow past complex endovascular devices using an adaptive embedding technique. IEEE Trans. Med. Imag. 24(4), 468–476 (2005)CrossRefGoogle Scholar
  17. 17.
    Cebral, J.R., Pergolizzi, R., Putman, C.M.: Computational fluid dynamics modeling of intracranial aneurysms: qualitatively comparison with cerebral angiography. Acad. Radiol. 14(7), 804–813 (2007)CrossRefGoogle Scholar
  18. 18.
    Chang, H.H., Duckwiler, G.R., Valentino, D.J., Chu, W.C.: Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for computational fluid dynamics modeling. Med. Phys. 36(12), 5612–5621 (2009)CrossRefGoogle Scholar
  19. 19.
    De Craene, M., Camara, O., Bijnens, B.H., Frangi, A.F.: Non-stationary diffeomorphic registration: application to endovascular treatment monitoring. In: SPIE Medical Imaging 2009: Image Processing, vol.7259, p.72591F (2009)Google Scholar
  20. 20.
    De Craene, M., Pozo, J.M., Villa-Uriol, M.C., Vivas, E., Sola, T., Guimaraens, L., Blasco, J., Macho, J., Frangi, A.F.: Coil compaction and aneurysm growth: image-based quantification using non-rigid registration. In: SPIE Medical Imaging 2008: Computer-Aided Diagnosis, vol.6915, p.69151R (2008)Google Scholar
  21. 21.
    Delingette, H.: General object reconstruction based on simplex meshes. Int. J. Comput. Vis. 32(2), 111–146 (1999)CrossRefGoogle Scholar
  22. 22.
    Dempere-Marco, L., Oubel, E., Castro, M.A., Putman, C.M., Millan, R.D., Frangi, A.F.: CFD analysis incorporating the influence of wall motion: application to intracranial aneurysms. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI, Lecture Notes on Computer Science, vol.4191, pp.438–445. Springer, Berlin, Heidelberg, New York, USA (2006)Google Scholar
  23. 23.
    Dunlop, R., Arbona, A., Rajasekaran, H., Lo Iacono, L., Fingberg, J., Summers, P., Benkner, S., Engelbrecht, G., Chiarini, A., Friedrich, C., Moore, B., Bijlenga, P., Iavindrasana, J., Hose, R., Frangi, A.F.: @neurIST – Chronic disease management through integration of heterogeneous data and computer-interpretable guideline services. Stud. Health. Technol. Inform. 138, 173–177 (2008)Google Scholar
  24. 24.
    Fenner, J., Brook, B., Clapworthy, G., Coveney, P., Feipel, V., Gregersen, H., Hose, D., Kohl, P., Lawford, P., McCormack, K., Pinney, D., Thomas, S., Van Sint Jan, S., Waters, S., Viceconti,M.: The EuroPhysiome, STEP and a roadmap for the virtual physiological human. Proc. R. Soc. A 366(1878), 2979–2999 (2008)Google Scholar
  25. 25.
    Flore, E., Larrabide, I., Petrini, L., Pennati, G., Frangi, A.F.: Stent deployment in aneurysmatic cerebral vessels: Assessment and quantification of the differences between Fast Virtual Stenting and Finite Element Analysis. In: CI2BM09 – MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modelling, vol. 5242, pp. 790–797, Springer, Berlin, Heidelberg, London (2009)Google Scholar
  26. 26.
    Ford, M.D., Alperin, N., Lee, S., Holdsworth, D., Steinman, D.: Characterization of volumetric flow rate waveforms in the normal internal carotid and vertebral arteries. Physiol. Meas. 26(4), 477–488 (2005)CrossRefGoogle Scholar
  27. 27.
    Ford, M.D., Nikolov, H.N., Milner, J.S., Lownie, S.P., DeMont, E.M., Kalata, W., Loth, F., Holdsworth, D.W., Steinman, D.A.: PIV-measured versus CFD-predicted flow dynamics in anatomically realistic cerebral aneurysm models. J. Biomech. Eng. 130(2), 021015 (2008)CrossRefGoogle Scholar
  28. 28.
    Ford, M.D., Stuhne, G., Nikolov, H., Habets, D., Lownie, S., Holdsworth, D., Steinman, D.: Virtual angiography for visualization and validation of computational models of aneurysm hemodynamics. IEEE Trans. Med. Imag. 24(12), 1586–1592 (2005)CrossRefGoogle Scholar
  29. 29.
    Friedrich, C.M., Dach, H., Gattermayer, T., Engelbrecht, G., Benkner, S., Hofmann-Apitius, M.: @neuLink: a service-oriented application for biomedical knowledge discovery. Stud Health Technol Inform 138, 165–172 (2008)Google Scholar
  30. 30.
    Geers, A., Larrabide, I., Radaelli, A.G., Bogunović, H., Gratama van Andel, H.A.F., Majoie, C.B., Frangi, A.F.: Reproducibility of image-based computational hemodynamics in intracranial aneurysms: comparison of CTA and 3DRA. In: IEEE Int. Symp. Biomed. Imag. pp.610–613. IEEE Press, Piscataway, NJ, USA, Boston, MA, USA (2009)Google Scholar
  31. 31.
    Guglielmi, G., Viñuela, F., Dion, J., Duckwiler, G.: Electrothrombosis of saccular aneurysms via endovascular approach. Part 2: Preliminary clinical experience. J. Neurosurg. 75(1), 8–14 (1991)Google Scholar
  32. 32.
    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(3), 224–241 (2007)CrossRefGoogle Scholar
  33. 33.
    Hoi, Y., Woodward, S., Kim, M., Taulbee, D., Meng, H.: Validation of CFD simulations of cerebral aneurysms with implication of geometric variations. J. Biomech. Eng. 128(6), 844–851 (2006)CrossRefGoogle Scholar
  34. 34.
    Iavindrasana, J., Lo Iacono, L., Müller, H., Periz, I., Summers, P., Wright, J., Friedrich, C., Dach, H., Gattermayer, T., Engelbrecht, G., Benkner, S., Hofmann-Apitius, M., Dunlop, R., Arbona, A., Rajasekaran, H., Fingberg, J., Chiarini, A., Moore, B., Bijlenga, P., Hose, R., Frangi, A.F.: The @neurIST project. Stud. Health Technol. Informat. 138, 161–164 (2008)Google Scholar
  35. 35.
    Ishida, F., Ogawa, H., Simizu, T., Kojima, T., Taki, W.: Visualizing the dynamics of cerebral aneurysms with four-dimensional computed tomographic angiography. Neurosurgery 57(3), 460–471 (2005)CrossRefGoogle Scholar
  36. 36.
    Jou, L.D., Quick, C.M., Young, W.L., Lawton, M.T., Higashida, R.T., Martin, A., Saloner, D.: Computational approach to quantifying hemodynamic forces in giant cerebral aneurysms. Am. J. Neuroradiol. 24(9), 1804–1810 (2003)Google Scholar
  37. 37.
    Jou, L.D., Saloner, D., Higashida, R.: Determining intra-aneurysmal flow for coiled cerebral aneurysm with digital fluoroscopy. Biomed. Eng. Appl. Basis Comm. 16(2), 43–48 (2004)CrossRefGoogle Scholar
  38. 38.
    Juvela, S.: Prehemorrhage risk factors for fatal intracranial aneurysm rupture. Stroke 34(8), 1852–1858 (2003)CrossRefGoogle Scholar
  39. 39.
    Kakalis, N.M., Mitsos, A.P., Byrne, J.V., Ventikos, Y.: The haemodynamics of endovascular aneurysm treatment: a computational modelling approach for estimating the influence of multiple coil deployment. IEEE Trans. Med. Imag. 27(6), 814–824 (2008)CrossRefGoogle Scholar
  40. 40.
    Kataoka, K., Taneda, M., Asai, T., Kinoshita, A., Ito, M., Kuroda, R.: Structural fragility and inflammatory response of ruptured cerebral aneurysms. A comparative study between ruptured and unruptured cerebral aneurysms. Stroke 30(7), 1396–1401 (1999)Google Scholar
  41. 41.
    Kayembe, K., Sasahara, M., Hazama, F.: Cerebral aneurysms and variations in the circle of Willis. Stroke 15(5), 846–850 (1984)Google Scholar
  42. 42.
    Kim, M., Larrabide, I., Villa-Uriol, M.C., Frangi, A.F.: Hemodynamic alterations of a patient-specific intracranial aneurysm induced by virtual deployment of stents in various axial orientation. In: IEEE International Symposium on Biomedical Imaging, pp.1215–1218. IEEE Press, Piscataway, NJ, USA, Boston, MA, USA (2009)Google Scholar
  43. 43.
    Kim, M., Taulbee, D., Tremmel, M., Meng, H.: Comparison of two stents in modifying cerebral aneurysm hemodynamics. Ann. Biomed. Eng., 36, 726–741 (2008)CrossRefGoogle Scholar
  44. 44.
    Krings, T., Willems, P., Barfett, J., Ellis, M., Hinojosa, N., Blobel, J., Geibprasert, S.: Pulsatility of an intracavernous aneurysm demonstrated by dynamic 320-detector row CTA at high temporal resolution. Cent. Eur. Neurosurg. 70, 214–218 (2009)CrossRefGoogle Scholar
  45. 45.
    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. A 464(2092), 807–825 (2008)CrossRefMathSciNetzbMATHGoogle Scholar
  46. 46.
    Larrabide, I., Kim, M., Augsburger, L., Villa-Uriol, M., Rüfenacht, D., Frangi, A.: Fast virtual deployment of self-expandable stents: Method and in-vitro validation for intracranial aneurysmal stenting. Med. Image Anal. doi:10.1016/j.media.2010.04.009. http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6Y-50297P9-1&_user=1517318&_coverDate=05\%2F11\%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000053451&_version=1&_urlVersion=0&_ userid=1517318&md5=5d3dad8469974f524b33d553ffa8aa13&searchtype=a (2010)
  47. 47.
    Larrabide, I., Radaelli, A.G., Frangi, A.F.: Fast virtual stenting with deformable meshes: Application to intracranial aneurysms. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI, Lecture Notes in Computer Science, vol.5242, pp.790–797. Springer, Berlin, Heidelberg, New York, USA (2008)Google Scholar
  48. 48.
    Liou, T.M., Li, Y.C.: Effects of stent porosity on hemodynamics in a sidewall aneurysm model. J. Biomech. Eng. 41(6), 1174–1183 (2008)CrossRefGoogle Scholar
  49. 49.
    Lo, C., Don, H.: 3-D moments forms: Their construction and application to object identification and positioning. IEEE Trans. Pattern. Anal. Mach. Intell. 11(10), 1053–1064 (1989)CrossRefGoogle Scholar
  50. 50.
    Lylyk, P., Ferrario, A., Pasbon, B., Miranda, C., Doroszuk, G.: Buenos Aires experience with the Neuroform self-expanding stent for the treatment of intracranial aneurysms. J. Neurosurg. 102(2), 235–241 (2005)CrossRefGoogle Scholar
  51. 51.
    Ma, B., Harbaugh, R.E., Raghavan, M.L.: Three-dimensional geometrical characterization of cerebral aneurysms. Ann. Biomed. Eng. 32(2), 264–273 (2004)CrossRefGoogle Scholar
  52. 52.
    Millan, R., Dempere-Marco, L., Pozo, J.M., Cebral, J.R., Frangi, A.F.: Morphological characterization of intracranial aneurysms using 3-D moment invariants. IEEE Trans. Med. Imag. 26(9), 1270–1282 (2007)CrossRefGoogle Scholar
  53. 53.
    Morales, H., Kim, M., Villa-Uriol, M.C., Vivas, E., Frangi, A.F.: Influence of coil packing rate and configuration on intracranial aneurysm hemodynamics. In: Dössel, O., Schlegel, W.C. (eds.) 11th International Congress of the IUPESM, Medical Physics and Biomedical Engineering, World Congress 2009, IFMBE Proceedings, vol.25/4, pp.2291–2294. Springer, Berlin, Heidelberg, Munich, Germany (2009)Google Scholar
  54. 54.
    Narracott, A., Smith, S., Lawford, P., Liu, H., Himeno, R., Wilkinson, I., Griffiths, P., Hose, R.: Development and validation of models for the investigation of blood clotting in idealized stenoses and cerebral aneurysms. J. Artif. Organs 8, 56–62 (2005)CrossRefGoogle Scholar
  55. 55.
    @neurIST Consortium (2010) Integrated biomedical informatics for the management of cerebral aneurysms. http://www.aneurist.org
  56. 56.
    Novotni, M., Klein, R.: Shape retrieval using 3D Zernike descriptors. Comput. Aided Des. 36, 1047–1062 (2004)CrossRefGoogle Scholar
  57. 57.
    Olufsen, M.S., Nadim, A., Lipsitz, L.A.: Dynamics of cerebral blood flow regulation explained using a lumped parameter model. Am. J. Physiol., Reg. Int. Comp. Physiol. 282, R611–R622 (2002)Google Scholar
  58. 58.
    Oubel, E., De Craene, M., Putman, C.M., Cebral, J.R., Frangi, A.F.: Analysis of intracranial aneurysm wall motion and its effects on hemodynamic patterns. In: SPIE Medical Imaging: Physics of Medical Imaging Image Reconstruction, vol.6511, p.65112A (2007)Google Scholar
  59. 59.
    Piotin, M., Mandai, S., Murphy, K.J., Sugiu, K., Gailloud, P., Martin, J.B., Rüfenacht, D.A.: Dense packing of cerebral aneurysms: an in vitro study with detachable platinum coils. Am. J. Neuroradiol. 21, 757–760 (2000)Google Scholar
  60. 60.
    Pozo, J.M., Villa-Uriol, M., Frangi, A.F.: Efficient 3D Geometric and Zernike moments computation from unstructured surface meshes. IEEE Trans. Pattern. Anal. Mach. Intell. http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.139 April (2011)
  61. 61.
    Radaelli, A., Augsburger, L., Cebral, J., Ohta, M., Rüfenacht, D., Balossino, R., Benndorf, G., Hose, D., Marzo, A., Metcalfe, R., Mortier, P., Mut, F., Reymond, P., Socci, L., Verhegghe, B., Frangi, A.F.: Reproducibility of haemodynamical simulations in a subject-specific stented aneurysm model – A report on the Virtual Intracranial Stenting Challenge 2007. J. Biomech. 41(10), 2069–2081 (2008)CrossRefGoogle Scholar
  62. 62.
    Raghavan, M.L., Ma, B., Harbaugh, R.E.: Quantified aneurysm shape and aneurysm rupture. J. Neurosurg. 102(2), 355–362 (2005)CrossRefGoogle Scholar
  63. 63.
    Raymond, J., Guilbert, F., Weill, A., Georganos, S.A., Juravsky, L., Lambert, A., Lamoureux, J., Chagnon, M., Roy, D.: Long-term angiographic recurrences after selective endovascular treatment of aneurysms with detachable coils. Stroke 34, 1398–1403 (2003)CrossRefGoogle Scholar
  64. 64.
    Rohde, S., Lahmann, K., Beck, J., Nafe, R., Yan, B., Raabe, A., Berkefeld, J.: Fourier analysis of intracranial aneurysms: towards an objective and quantitative evaluation of the shape of aneurysms. Neuroradiology 47, 121–126 (2005)CrossRefGoogle Scholar
  65. 65.
    Satoh, T., Onoda, K., Tsuchimoto, S.: Visualization of intraaneurysmal flow patterns with transluminal flow images of 3D MR angiograms in conjunction with aneurysmal configurations. Am. J. Neuroradiol. 24(7), 1436–1445 (2004)Google Scholar
  66. 66.
    Schievink, W.: Intracranial aneurysms. New Engl. J. Med. 336, 28–41 (1997)CrossRefGoogle Scholar
  67. 67.
    Singh, P., Marzo, A., Coley, S., Berti, G., Bijlenga, P., Lawford, P., Villa-Uriol M.C., Rüfenacht, D., McCormack, K., Frangi, A.F., Patel, U., Hose, D.R.: The role of computational fluid dynamics in the management of unruptured intracranial aneurysms: a clinicians’ view. Comput. Intell. Neurosci. 2009(760364), 1–12 (2009)CrossRefGoogle Scholar
  68. 68.
    Sluzewski, M., van Rooij, W.J., Slob, M.J., Bescós, J.O., Slump, C.H., Wijnalda, D.: Relation between aneurysm volume, packing, and compaction in 145 cerebral aneurysms treated with coils. Radiology 231, 653–658 (2004)CrossRefGoogle Scholar
  69. 69.
    Steinman, D., Milner, J., Norley, C., Lownie, S., Holdsworth, D.: Image-based computational simulation of flow dynamics in a giant intracranial aneurysm. Am. J. Neuroradiol. 24, 559–566 (2003)Google Scholar
  70. 70.
    STEP Consortium (2007) Seeding the EuroPhysiome: A roadmap to the Virtual Physiological Human. http://www.europhysiome.org/roadmap
  71. 71.
    Stuhne, G.R., Steinman, D.A.: Finite-element modeling of the hemodynamics of stented aneurysms. J. Biomech. Eng. 126(3), 382–387 (2004)CrossRefGoogle Scholar
  72. 72.
    Taylor, C., Humphrey, J.: Open problems in computational vascular biomechanics: Hemodynamics and arterial wall mechanics. Comput. Meth. Appl. Mech. Eng. 198, 3514–3523 (2009)CrossRefMathSciNetzbMATHGoogle Scholar
  73. 73.
    Ujiie, H., Tachibana, H., Hiramatsu, O., Hazel, A.L., Matsumoto, T., Ogasawara, Y., Nakajima, H., Hori, T., Takakura, K., Kajiya, F.: Effects of size and shape (aspect ratio) on the hemodynamics of saccular aneurysms: A possible index for surgical treatment of intracranial aneurysms. Neurosurgery 45(1), 119–130 (1999)CrossRefGoogle Scholar
  74. 74.
    Viceconti, M., Clapworthy, G., Van Sint Jan, S.: The Virtual Physiological Human – a European initiative for in silico human modelling –. J. Physiol. Sci. 58(7), 441–447 (2008)Google Scholar
  75. 75.
    White, J.B., Ken, C.G., Cloft, H.J., Kallmes, D.F.: Coils in a nutshell: a review of coil physical properties. Am. J. Neuroradiol. 29(7), 1242–1246 (2008)CrossRefGoogle Scholar
  76. 76.
    Wiebers, D.: The international study of unruptured intracranial aneurysms investigators. Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment. Lancet 362(9378), 103–110 (2003)Google Scholar
  77. 77.
    Zhang, C., De Craene, M., Villa-Uriol, M.C., Pozo, J.M., Bijnens, B.H., Frangi, A.F.: Estimating continuous 4D wall motion of cerebral aneurysms from 3D rotational angiography. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI, Lecture Notes on Computer Science, vol.5761, pp.140–147. Springer, Berlin, Heidelberg, London, UK (2009)Google Scholar
  78. 78.
    Zhang, C., Villa-Uriol, M.C., De Craene, M., Pozo, J.M., Frangi, A.F.: Morphodynamic analysis of cerebral aneurysm pulsation from time-resolved rotational angiography. IEEE Trans. Med. Imag. 28(7), 1105–1116 (2009)CrossRefGoogle Scholar
  79. 79.
    Zhang, C., Villa-Uriol, M.C., Frangi, A.F.: Evaluation of an efficient GPU implementation of digitally reconstructed radiographs in 3D/2D image registration. In: SPIE Medical Imaging: Image Processing, p. 762333 (2010)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • M. C. Villa-Uriol
    • 1
    Email author
  • I. Larrabide
    • 2
  • J. M. Pozo
    • 1
  • M. Kim
    • 1
  • M. De Craene
    • 1
  • O. Camara
    • 1
  • C. Zhang
    • 1
  • A. J. Geers
    • 1
  • H. Bogunović
    • 1
  • H. Morales
    • 1
  • A. F. Frangi
    • 1
  1. 1.Center for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)Universitat Pompeu FabraBarcelonaSpain
  2. 2.Center for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), InstitucióCatalana de Recerca i Estudis Avançats (ICREA)Universitat Pompeu FabraBarcelonaSpain

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