Computing and Visualization in Science

, Volume 14, Issue 5, pp 207–216 | Cite as

Non-standard bone simulation: interactive numerical analysis by computational steering

  • Zhengxiong Yang
  • Stefan KollmannsbergerEmail author
  • Alexander Düster
  • Martin Ruess
  • Eduardo Grande Garcia
  • Rainer Burgkart
  • Ernst Rank


Numerous numerical methods have been developed in an effort to accurately predict stresses in bones. The largest group are variants of the h-version of the finite element method (h-FEM), where low order Ansatz functions are used. By contrast, we3 investigate a combination of high order FEM and a fictitious domain approach, the finite cell method (FCM). While the FCM has been verified and validated in previous publications, this article proposes methods on how the FCM can be made computationally efficient to the extent that it can be used for patient specific, interactive bone simulations. This approach is called computational steering and allows to change input parameters like the position of an implant, material or loads and leads to an almost instantaneous change in the output (stress lines, deformations). This direct feedback gives the user an immediate impression of the impact of his actions to an extent which, otherwise, is hard to obtain by the use of classical non interactive computations. Specifically, we investigate an application to pre-surgical planning of a total hip replacement where it is desirable to select an optimal implant for a specific patient. Herein, optimal is meant in the sense that the expected post-operative stress distribution in the bone closely resembles that before the operation.


Surgical planning Computational steering Finite cell method Total hip replacement 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baca V., Horak Z., Mikulenka P., Dzupa V.: Comparison of an inhomogeneous orthotropic and isotropic material models used for fe analyses. Med. Eng. Phys. 30, 924–930 (2008)CrossRefGoogle Scholar
  2. 2.
    Bongini D., Carfagni M., Governi L.: A semiautomatic computer program for selecting hip prosthesis femoral components. Comput. Methods Programs Biomed. 63, 105–115 (2000)CrossRefGoogle Scholar
  3. 3.
    Borrmann, A., Wenisch, P., Egger, M., van Treeck, C., Rank, E.: Collaborative computational steering: interactive collaborative design of ventilation and illumination of operating theatres. ICE08, Plymouth (2008)Google Scholar
  4. 4.
    Dick, C., Georgii, J., Burgkart, R., Westermann, R.: Computational steering for patient-specific implant planning in orthopedics. In: Proceedings of Visual Computing for Biomedicine, pp. 83–92 (2008)Google Scholar
  5. 5.
    Dick C., Georgii J., Burgkart R., Westermann R.: Stress tensor field visualization for implant planning in orthopedics. IEEE Trans. Vis. Comput. Graph. 15, 1399–1406 (2009)CrossRefGoogle Scholar
  6. 6.
    DiGioia A.M., Simon D., Jaramaz B., Blackwell M.: The value of preoperative planning for total hip arthroplasty. Comput. Assist. Orthop. Surg. Symp. 80, 382 (1995)Google Scholar
  7. 7.
    Dongarra J.J., Du Croz J., Duff I.S., Hammarling S.: Algorithm 679: a set of level 3 basic linear algebra subprograms. ACM Trans. Math. Softw. 16, 18–28 (1990)CrossRefzbMATHGoogle Scholar
  8. 8.
    Düster A., Parvizian J., Yang Z., Rank E.: The finite cell method for three-dimensional problems of solid mechanics. Comput. Methods Appl. Mech. Eng. 197, 3768–3782 (2008)CrossRefzbMATHGoogle Scholar
  9. 9.
    Effenberger H., Heiland A., Ramsauer T., Plitz W., Dorn U.: A model for assessing the rotational stability of uncemented femoral implants. Arch. Orthop. Trauma Surg. 121, 60–64 (2000)CrossRefGoogle Scholar
  10. 10.
    Günter T., Merz B., Mericske-Stern R., Schmitt J., Leppek R., Lengsfeld M.: Testing dental implants with an in vivo finite element model. Biomed. Eng. (Biomedizinische Technik) 45, 272–276 (2000)CrossRefGoogle Scholar
  11. 11.
    Kak A.C., Slaney M.: Principles of Computerized Tomographic Imaging. Society for Industrial Mathematics, Philadelphia (2001)CrossRefGoogle Scholar
  12. 12.
    Keyak J.H., Falkinstein Y.: Comparison of in situ and in vitro CT scan-based finite element model predictions of proximal femoral fracture load. Med. Eng. Phys. 25, 781–787 (2003)CrossRefGoogle Scholar
  13. 13.
    Keyak J.H., Lee I.Y., Skinner H.B.: Correlations between orthogonal mechanical properties and density of trabecular bone: use of different densitometric measures. J. Biomed. Mater. Res. 28(11), 1329–1336 (1994)CrossRefGoogle Scholar
  14. 14.
    Kuhl E., Balle F.: Computational modeling of hip replacement surgery: total hip replacement vs. hip resurfacing. Technische Mechanik 25, 107–114 (2005)Google Scholar
  15. 15.
    MacWilliams B.A., Wilson D.R., DesJardins J.D., Romero J., Chao E.Y.: Hamstrings cocontraction reduces internal rotation, anterior translation, and anterior cruciate ligament load in weight-bearing flexion. J. Orthop. Res. 17, 817–822 (1999)CrossRefGoogle Scholar
  16. 16.
    McCarthy E.F., Khurana J.S., Zhang P.J.: Essentials in Bone and Soft-Tissue Pathology. Springer, New York (2009)Google Scholar
  17. 17.
    Mow V.C., Huiskes R.: Basic Orthopaedic Biomechanics and Mechano-biology. Lippincott Williams & Wilkins, Philadelphia (2005)Google Scholar
  18. 18.
    Mulder J., Wijk J., Liere R.: A survey of computational steering environments. Futur. Gener. Comput. Syst. 15(1), 119–129 (1999)CrossRefGoogle Scholar
  19. 19.
    Niggl, A., Rank, E., Mundani, R.P., Bungartz, H.J.: Organizing a p-Version finite element computation by an octree-based hierarchy. In: Proceedings of the International Conference on Adaptive Modeling and Simulation (2005)Google Scholar
  20. 20.
    Parvizian J., Düster A., Rank E.: Finite cell method—h- and p-extension for embedded domain problems in solid mechanics. Comput. Mech. 41, 121–133 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Riedel, M., Frings, W., Eickermann, T.H., Habbinga, S., Gibbon, P., Mallmann, D., Streit, A., Wolf, F., Lippert, T.H.: Collaborative Interactivity in Parallel HPC Applications. Springer US (2010)Google Scholar
  22. 22.
    Rietbergen B.V.: Computational strategies for iterative solutions of large FEM applications employing voxel data. Int. J. Numer. Methods Eng. 39, 2743–2764 (1996)CrossRefzbMATHGoogle Scholar
  23. 23.
    Ruess, M., Tal, D., Trabelsi, N., Yosibash, Z., Rank, E.: The finite cell method for bone simulations: verification and validation. Biomech. Model. Mechanobiol., pp. 1–13 (2011).
  24. 24.
    Saha S., Roychowdhury A.: Application of the finite element method in orthopedic implant design. J. Long-term Eff. Med. Implant. 19[1], 55–82 (2009)Google Scholar
  25. 25.
    Schenk O., Gärtner K.: Solving unsymmetric sparse systems of linear equations with PARDISO. J. Futur. Gener. Comput. Syst. 20, 475–487 (2004)CrossRefGoogle Scholar
  26. 26.
    Schenk O., Gärtner K.: On fast factorization pivoting methods for symmetric indefinite systems. Electron. Trans. Numer. Anal. 23, 158–179 (2006)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Schillinger, D., Rank, E.: An unfitted hp adaptive finite element method based on hierarchical B-splines for interface problems of complex geometry. Comput. Methods Appl. Mech. Eng. (submitted) (2011)Google Scholar
  28. 28.
    Süli E., Mayers D.: An Introduction to Numerical Analysis. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  29. 29.
    Trabelsi N., Yosibash Z., Wutte C., Augat P., Eberle S.: Patient-specific finite element analysis of the human femu: a double-blinded biomechanical validation. J. Biomech. 44, 1666–1672 (2011)CrossRefGoogle Scholar
  30. 30.
    Verdonschot N., Huiskes R.: Mechanical effects of stem-cement interface characteristics in total hip replacement. Clin. Orthop. Relat. Res. 329, 326–336 (1996)CrossRefGoogle Scholar
  31. 31.
    Vetter, J., Schwan, K.: High performance computational steering of physical simulations. In: Proceedings of the 11th International Symposium on Parallel Processing, IPPS 97, pp. 128–132 (1997)Google Scholar
  32. 32.
    Weinans H., Huiskes R.: Trends of mechanical consequence and modeling of a fibrous membrane around femoral hip prostheses. IEEE Comput. Sci. Eng. 23, 991–1000 (1990)Google Scholar
  33. 33.
    Wenisch P., van Treeck C., Borrmann A., Rank E., Wenisch O.: Computational steering on distributed systems: indoor comfort simulations as a case study of inter-active cfd on supercomputers. Int. J. Parallel Emergent Distrib. Syst. 22, 275–291 (2007)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Wolff J.: On the inner architecture of bones and its importance for bone growth. Clin. Orthop. Relat. Res. 468, 1056–1065 (2010)CrossRefGoogle Scholar
  35. 35.
    Yang, Z.: The finite cell method for geometry-based structural simulation. Ph.D. thesis, Computation in Engineering, Fakultät für Bauingenieur- und Vermessungswesen, Technische Universität München (2011)Google Scholar
  36. 36.
    Yosibash Z., Padan R., Joscowicz L., Milgrom C.: A CT-based high-order finite element analysis of the human proximal femur compared to in-vitro experiments. ASME J. Biomech. Eng. 129, 297–309 (2007)CrossRefGoogle Scholar
  37. 37.
    Yosibash Z., Trabelsi N., Milgrom C.: Reliable simulations of the human proximal femur by high-order finite element analysis validated by experimental observations. J. Biomech. 40, 3688–3699 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Zhengxiong Yang
    • 1
  • Stefan Kollmannsberger
    • 1
    Email author
  • Alexander Düster
    • 2
  • Martin Ruess
    • 1
  • Eduardo Grande Garcia
    • 3
  • Rainer Burgkart
    • 3
  • Ernst Rank
    • 1
  1. 1.Chair for Computation in Engineering, Faculty of Civil Engineering and GeodesyTechnische Universität MünchenMunichGermany
  2. 2.Numerische Strukturanalyse mit Anwendungen in der Schiffstechnik (M-10)Technische Universität Hamburg-HarburgHamburgGermany
  3. 3.Klinik u. Poliklinik für Orthopädie u. Sportorthopädie am Klinikum Rechts der IsarTechnische Universität MünchenMunichGermany

Personalised recommendations