Abstract
This paper describes an approach to personalized musculoskeletal modelling, in which the muscle represented by its triangular mesh is subject to deformation, based on a modified position-based dynamic (PBD) method, followed by decomposition of its volume into a set of muscle fibres. The PBD was enhanced by respecting some muscle-specific features, mainly its anisotropy. The proposed method builds no internal structures and works only with the muscle surface model. It runs in real-time on commodity hardware while maintaining visual plausibility of the resulting deformation. For decomposition, the state-of-the-art Kukačka method is used. Experiments with the gluteus maximus, gluteus medius, iliacus and adductor brevis deforming during the simulation of the hip flexion and decomposed into 100 fibres of 15 line segments show that the approach is capable of achieving promising results comparable with those in the literature, at least in the term of muscle fibre lengths.
Keywords
This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic, project SGS-2019-016 and project PUNTIS (LO1506).
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Arnold, E.M., Ward, S.R., Lieber, R.L., Delp, S.L.: A model of the lower limb for analysis of human movement. Ann. Biomed. Eng. 38(2), 269–279 (2009). https://doi.org/10.1007/s10439-009-9852-5. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2903973/
Audenaert, A., Audenaert, E.: Global optimization method for combined spherical-cylindrical wrapping in musculoskeletal upper limb modelling. Comput. Methods Programs Biomed. 92(1), 8–19 (2008). https://doi.org/10.1016/j.cmpb.2008.05.005. http://www.ncbi.nlm.nih.gov/pubmed/18606476
Bolsterlee, B., Veeger, D.H.E.J., Chadwick, E.K.: Clinical applications of musculoskeletal modelling for the shoulder and upper limb. Med. Biol. Eng. Comput. 51(9), 953–963 (2013). https://doi.org/10.1007/s11517-013-1099-5
Carbone, V., van der Krogt, M., Koopman, H., Verdonschot, N.: Sensitivity of subject-specific models to errors in musculo-skeletal geometry. J. Biomech. 45(14), 2476–2480 (2012). https://doi.org/10.1016/j.jbiomech.2012.06.026
Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: MeshLab: an open-source mesh processing tool. Computing 1, 129–136 (2008). https://doi.org/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2008/129-136
Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp, E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Trans. Biomed. Eng. 37(8), 757–767 (1990). https://doi.org/10.1109/10.102791
Delp, S.: Three-dimensional representation of complex muscle architectures and geometries 1. Ann. Biomed. Eng. 33, 1134 (2005). https://doi.org/10.1007/s10439-005-1433-7
Delp, S.L., Loan, J.P.: A computational framework for simulating and analyzing human and animal movement. Comput. Sci. Eng. 2(5), 46–55 (2000)
Červenka, M., Kohout, J.: Fast and realistic approach to virtual muscle deformation. In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies. SCITEPRESS - Science and Technology Publications (2020). https://doi.org/10.5220/0009129302170227
Fukuda, N., et al.: Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers. Int. J. Comput. Assist. Radiol. Surg. 12(5), 733–742 (2017). https://doi.org/10.1007/s11548-016-1519-8
Garner, B., Pandy, M.: The obstacle-set method for representing muscle paths in musculoskeletal models. Comput. Methods Biomech. Biomed. Eng. 3(1), 1–30 (2000)
Herteleer, M., et al.: Variation of the clavicle’s muscle insertion footprints - a cadaveric study. Sci. Rep. 9(1), 1–8 (2019). https://doi.org/10.1038/s41598-019-52845-8
Hong, M., Jung, S., Choi, M.H., Welch, S.: Fast volume preservation for a mass-spring system. IEEE Comput. Graph. Appl. 26, 83–91 (2006). https://doi.org/10.1109/MCG.2006.104
Janák, T., Kohout, J.: Deformable muscle models for motion simulation. In: Proceedings of the 9th International Conference on Computer Graphics Theory and Applications. pp. 301–311. SCITEPRESS - Science and and Technology Publications (2014). https://doi.org/10.5220/0004678903010311
Ju, T., Schaefer, S., Warren, J.: Mean value coordinates for closed triangular meshes. ACM Trans. Graph. 24(3), 561–566 (2005). http://portal.acm.org/citation.cfm?doid=1073204.1073229
Kellnhofer, P., Kohout, J.: Time-convenient deformation of musculoskeletal system. In: ALGORITMY 2012, 19th Conference on Scientific Computing, Vysoke Tatry, Slovakia, 09–14 Sep 2012, pp. 239–249. Slovak Univ Technology, Bratislava (2012)
Kohout, J., et al.: Patient-specific fibre-based models of muscle wrapping. Interface Focus 3(2), 20120062 (2013). https://doi.org/10.1098/rsfs.2012.0062
Kohout, J., Kukačka, M.: Real-time modelling of fibrous muscle. Comput. Graph. Forum 33(8), 1–15 (2014). https://doi.org/10.1111/cgf.12354
Kohout, J., Kukačka, M.: Real-time modelling of fibrous muscle. In: Computer Graphics Forum [18], pp. 1–15. https://doi.org/10.1111/cgf.12354
Kohout, J., Cholt, D.: Automatic reconstruction of the muscle architecture from the superficial layer fibres data. Comput. Methods Programs Biomed. 150, 85–95 (2017). https://doi.org/10.1016/j.cmpb.2017.08.002
Kohout, J., Cholt, D.: Automatic reconstruction of the muscle architecture from the superficial layer fibres data. In: Computer Methods and Programs in Biomedicine [20], pp. 85–95. https://doi.org/10.1016/j.cmpb.2017.08.002
Kohout, J., Clapworthy, G.J., Martelli, S., Viceconti, M.: Fast realistic modelling of muscle fibres. In: Csurka, G., Kraus, M., Laramee, R.S., Richard, P., Braz, J. (eds.) VISIGRAPP 2012. CCIS, vol. 359, pp. 33–47. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38241-3_3
Kojic, M., Mijailovic, S., Zdravkovic, N.: Modelling of muscle behaviour by the finite element method using Hill’s three-element model. Int. J. Numer. Meth. Eng. 43(5), 941–953 (1998). https://doi.org/10.1002/(SICI)1097-0207(19981115)43:5<941::AID-NME435>3.0.CO;2-3
Kotsalos, C., Latt, J., Chopard, B.: Bridging the computational gap between mesoscopic and continuum modeling of red blood cells for fully resolved blood flow. J. Comput. Phys. 398, 108905 (2019). https://doi.org/10.1016/j.jcp.2019.108905. cited By 0
Macklin, M., et al.: Small steps in physics simulation. In: Proceedings of the 18th Annual ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2019, pp. 2:1–2:7. ACM, New York (2019). https://doi.org/10.1145/3309486.3340247
Modenese, L., Gopalakrishnan, A., Phillips, A.: Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector. J. Biomech. 46(6), 1193–1200 (2013). https://doi.org/10.1016/j.jbiomech.2012.11.045
Modenese, L., Kohout, J.: Automated generation of three-dimensional complex muscle geometries for use in personalised musculoskeletal models. Ann. Biomed. Eng. 48, 1793–1804 (2020). https://doi.org/10.1007/s10439-020-02490-4
Müller, M., Heidelberger, B., Hennix, M., Ratcliff, J.: Position based dynamics. J. Vis. Commun. Image Represent. 18, 109–118 (2007). https://doi.org/10.1016/j.jvcir.2007.01.005
Oberhofer, K., Mithraratne, K., Stott, N.S., Anderson, I.A.: Anatomically-based musculoskeletal modeling: prediction and validation of muscle deformation during walking. Vis. Comput. 25(9), 843–851 (2009). https://doi.org/10.1007/s00371-009-0314-8
Otake, Y., et al.: Patient-specific skeletal muscle fiber modeling from structure tensor field of clinical CT images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 656–663. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_75
Pellikaan, P., et al.: Evaluation of a morphing based method to estimate muscle attachment sites of the lower extremity. J. Biomech. 47(5), 1144–1150 (2014). https://doi.org/10.1016/j.jbiomech.2013.12.010
Romeo, M., Monteagudo, C., Sánchez-Quirós, D.: Muscle and fascia simulation with extended position based dynamics. Comput. Graph. Forum 39(1), 134–146 (2019). https://doi.org/10.1111/cgf.13734
Shao, X., Liao, E., Zhang, F.: Improving SPH fluid simulation using position based dynamics. IEEE Access 5, 13901–13908 (2017). https://doi.org/10.1109/ACCESS.2017.2729601
Valente, G., Martelli, S., Taddei, F., Farinella, G., Viceconti, M.: Muscle discretization affects the loading transferred to bones in lower-limb musculoskeletal models. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 226(2), 161–169 (2012)
Van Sint Jan, S.: Introducing anatomical and physiological accuracy in computerized anthropometry for increasing the clinical usefulness of modeling systems. Crit. Rev. Phys. Rehabil. Med. 17, 149–174 (2005). https://doi.org/10.1615/CritRevPhysRehabilMed.v17.i4.10
Viceconti, M., Clapworthy, G., Van Sint Jan, S.: The virtual physiological human - a European initiative for in silico human modelling. J. Physiol. Sci.: JPS 58, 441–446 (2008). https://doi.org/10.2170/physiolsci.RP009908
Weinhandl, J.T., Bennett, H.J.: Musculoskeletal model choice influences hip joint load estimations during gait. J. Biomech. 91, 124–132 (2019). https://doi.org/10.1016/j.jbiomech.2019.05.015
Zhao, Y., et al.: Laplacian musculoskeletal deformation for patient-specific simulation and visualisation. In: 2013 17th International Conference on Information Visualisation. IEEE (2013). https://doi.org/10.1109/iv.2013.67
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Authors would like to thank their colleagues and students for valuable discussion, worthful suggestions and constructive comments. Authors would like to thank also anonymous reviewers for their hints and notes provided.
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Kohout, J., Červenka, M. (2021). Muscle Deformation Using Position Based Dynamics. In: Ye, X., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_24
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