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Multimodal composition of the digital patient: a strategy for the knee articulation

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Abstract

Creating virtual bodies of real patients and using them for diagnosis and treatment planning offer the potential to further empower clinical decision making by medical experts. Virtual patient modeling allows to examine the mechanical and physiological conditions under which articulations are operating in a variety of activities without putting the patient in hazard. The continuous scientific progress has led to an increased range of musculoskeletal data and knowledge being available, covering multiple scales of the musculoskeletal system. A fuller integration of these modalities can broaden the scientific basis of virtual articulation modeling in patients, but poses challenges for data fusion and coupling of simulations. Here, we present a multimodal strategy to compose virtual models of the knee articulation based on a complementary spectrum of data that enables simulations on different scales.

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Notes

  1. http://www.openmesh.org.

  2. http://www.itk.org.

  3. http://bulletphysics.org/.

References

  1. Rossi, R., Dettoni, F., Bruzzone, M., Cottino, U., D’Elicio, D.G., Bonasia, D.E.: Clinical examination of the knee: know your tools for diagnosis of knee injuries. BMC Sports Sci. Med. Rehabil. 3(1), 25 (2011)

    Google Scholar 

  2. Viceconti, M., Testi, D., Taddei, F., Martelli, S., Clapworthy, G., Jan, S.: Biomechanics modeling of the musculoskeletal apparatus: status and key issues. Proc. IEEE 94(4), 725–739 (2006)

    Article  Google Scholar 

  3. Magnenat-Thalmann, N., Schmid, J., Assassi, L., Volino, P.: A comprehensive methodology to visualize articulations for the physiological human. In: 2010 International Conference on Cyberworlds (CW), pp. 1–8 (2010)

  4. Scheys, L., Desloovere, K., Spaepen, A., Suetens, P., Jonkers, I.: Calculating gait kinematics using MR-based kinematic models. Gait Posture 33(2), 158–164 (2011)

    Article  Google Scholar 

  5. Kalliokoski, K.K., Boushel, R., Langberg, H., Scheede-Bergdahl, C., Ryberg, A.K., Dossing, S., Kjaer, A., Kjaer, M.: Differential glucose uptake in quadriceps and other leg muscles during one-legged dynamic submaximal knee-extension exercise. Front Physiol. 2, 75 (2011). doi:10.3389/fphys.2011.00075

    Article  Google Scholar 

  6. McKee, C.T., Last, J.A., Russell, P., Murphy, C.J.: Indentation versus tensile measurements of young’s modulus for soft biological tissues. Tissue Eng. Part B. Rev. 17(3), 155–164 (2011)

    Article  Google Scholar 

  7. Bredno, J., Lehmann, T.M.T., Spitzer, K.: A general discrete contour model in two, three, and four dimensions for topology-adaptive multichannel segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 550–563 (2003)

    Article  Google Scholar 

  8. Becker, M., Magnenat-Thalmann, N.: Deformable Models in Medical Image Segmentation. In: 3D Multiscale Physiological Human, 1st edn., chap. 4, pp. 81–106. Springer-Verlag, London (2014)

  9. Chan, T.F., Sandberg, B., Vese, L.A.: Active contours without edges for vector-valued images. J. Vis. Commun. Image Represent. 11(2), 130–141 (2000)

    Article  Google Scholar 

  10. Angelini, E.D., Imielinska, C., Jin, Y., Laine, A.F.: Improving statistics for hybrid segmentation of high-resolution multichannel images. In: Medical Imaging 2002: Image Processing, vol. 4684, pp. 401–411 (2002)

  11. Geremia, E., Menze, B.H., Clatz, O., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel MR images. In: Proceedings MICCAI 2010, pp. 111–118. Springer, Berlin Heidelberg (2010)

  12. Gilles, B., Magnenat-Thalmann, N.: Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med. Image Anal. 14(3), 291–302 (2010)

    Google Scholar 

  13. Baudin, P.Y., Azzabou, N., Carlier, P.G., Paragios, N.: Prior knowledge, random walks and human skeletal muscle segmentation. In: Proceedings MICCAI 2012, pp. 569–576. Springer, Berlin Heidelberg (2012)

  14. Pandy, M.G., Andriacchi, T.P.: Muscle and joint function in human locomotion. Ann. Rev. Biomed. Eng. 12(1), 401–433 (2010)

    Article  Google Scholar 

  15. Lee, D., Glueck, M., Khan, A., Fiume, E., Jackson, K.: Modeling and simulation of skeletal muscle for computer graphics: a survey. Found Trends Comput. Graph. Vis. 7(4), 229–276 (2012)

    Article  Google Scholar 

  16. Blemker, S.S., Delp, S.L.: Three-dimensional representation of complex muscle architectures and geometries. Ann. Biomed. Eng. 33(5), 661–673 (2005)

    Article  Google Scholar 

  17. Maurice, X., Sandholm, A., Pronost, N., Boulic, R., Thalmann, D.: A subject-specific software solution for the modeling and the visualization of muscles deformations. Vis. Comput. 25(9), 835–842 (2009)

    Article  Google Scholar 

  18. Fernandez, J.W., Hunter, P.J.: An anatomically based patient-specific finite element model of patella articulation: towards a diagnostic tool. Biomech. Model Mechanobiol. 4(1), 20–38 (2005)

    Article  Google Scholar 

  19. Pena, E., Calvo, B., Martínez, M., Doblaré, M.: A three-dimensional finite element analysis of the combined behavior of ligaments and menisci in the healthy human knee joint. J. Biomech. 39(9), 1686–1701 (2006)

    Article  Google Scholar 

  20. John, D., Pinisetty, D., Gupta, N.: Image based model development and analysis of the human knee joint. Biomedical Imaging and Computational Modeling in Biomechanics. Lecture Notes in Computational Vision and Biomechanics, vol. 4, pp. 55–79. Springer, Netherlands (2013)

  21. Yang, N.H., Nayeb-Hashemi, H., Canavan, P.K., Vaziri, A.: Effect of frontal plane tibiofemoral angle on the stress and strain at the knee cartilage during the stance phase of gait. J. Orthop. Res. 28(12), 1539–1547 (2010)

    Article  Google Scholar 

  22. Shim, V., Mithraratne, K., Anderson, I., Hunter, P.: Simulating in-vivo knee kinetics and kinematics of tibio-femoral articulation with a subject-specific finite element model. In: World Congress on Medical Physics and Biomedical Engineering, IFMBE Proceedings 25(4), 2315–2318 (2010)

  23. Sibole, S.C., Erdemir, A.: Chondrocyte deformations as a function of tibiofemoral joint loading predicted by a generalized high-throughput pipeline of multi-scale simulations. PLoS One 7(5), e37538 (2012)

    Article  Google Scholar 

  24. Heimann, T., Chung, F., Lamecker, H., Delingette, H.: Subject-specific ligament models: Toward real-time simulation of the knee joint. In: Computational Biomechanics for Medicine, pp. 107–119. Springer, New York (2010)

  25. Erdemir, A.: Open knee: a pathway to community driven modeling and simulation in joint biomechanics. In: Proceedings ASME/FDA 2013 1st Annual Frontiers in Medical Devices. Washington, DC, USA (2013)

  26. Choi, H.F., Blemker, S.S.: Skeletal muscle fascicle arrangements can be reconstructed using a laplacian vector field simulation. Plos One 8(10), e77576 (2013)

    Article  Google Scholar 

  27. Kazemi, M., Dabiri, Y., Li, L.P.: Recent advances in computational mechanics of the human knee joint. Comput. Math. Methods. Med. 2013 (2013). doi:10.1155/2013/718423

  28. Pelechano, N., Allbeck, J.M., Badler, N.I.: Controlling individual agents in high-density crowd simulation. In: Proceedings 2007 ACM SIGGRAPH/Eurographics Symposium on Computer, Animation, pp. 99–108 (2007)

  29. Maas, S.A., Ellis, B.J., Ateshian, G.A., Weiss, J.A.: FEBio: finite elements for biomechanics. J. Biomech. Eng. 134, 011005–1, 011005–10 (2013)

  30. Kadaba, M.P., Ramakrishnan, H.K., Wootten, M.E.: Measurement of lower extremity kinematics during level walking. J. Orthop. Res. 8(3), 383–392 (1990)

    Google Scholar 

  31. Kupper, J., Loitz-Ramage, B., Corr, D., Hart, D., Ronsky, J.: Measuring knee joint laxity: a review of applicable models and the need for new approaches to minimize variability. Clin. Biomech. 22(1), 1–13 (2007)

    Google Scholar 

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Acknowledgments

This work has been funded by the EU FP7 Marie-Curie ITN project MultiScaleHuman under Grant number 289897. We thank the University Hospital of Geneva in Switzerland, for providing the medical images, and the biomechanics laboratory LBB-MHH of the medical school in Hanover, Germany, for the experimental data of knee displacement. One of the authors, Nadia Magnenat Thalmann, is grateful to Humboldt Foundation to have allowed her to spend some time in Germany for collaboration with LBB-MHH and the Leibniz University in Hanover.

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  1. MIRALab, University of Geneva, Geneva, Switzerland

    Hon Fai Choi, Andra Chincisan, Matthias Becker & Nadia Magnenat-Thalmann

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  1. Hon Fai Choi
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Correspondence to Hon Fai Choi.

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Choi, H.F., Chincisan, A., Becker, M. et al. Multimodal composition of the digital patient: a strategy for the knee articulation. Vis Comput 30, 739–749 (2014). https://doi.org/10.1007/s00371-014-0983-9

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