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A comparison of projection-based model reduction concepts in the context of nonlinear biomechanics

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Abstract

Computational assistance gains increasing importance in the field of medical surgery. As an example, in the present work, we look at functional endoscopic sinus surgery. Simulations for surgery training programs or online support during surgeries require simulation tools which are characterized by a preferably short simulation time (real time) and a high degree of accuracy. The nonlinear finite element method is most suitable to yield qualitatively and quantitatively reliable results. The problem is, however, to achieve such results in real time. One possibility to reach both, short computational time and high accuracy, is to combine model reduction and finite element techniques. Therefore, in this paper, various projection-based model reduction methods are discussed and compared with respect to their possible application in biomechanics. The modal basis, the load-dependent Ritz and the proper orthogonal decomposition (POD) method were used to reduce the model of a cube under compression considering different material nonlinearities and large deformations. The POD method led to the lowest errors in displacement and stress while providing the largest reduction in CPU time. Further, the influence of different POD parameters was investigated. According to this study, the snapshots upon which the POD is based had to agree as closely as possible with the original deformation of the reduced system. The POD method applied to the finite element model of an inferior turbinate led to an adequate accuracy for surgery simulations within less than one-third of the computational time of the unreduced finite element simulation.

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References

  1. Amsallem D., Cortial J., Carlberg K., Farhat C.: A method for interpolating on manifolds structural dynamics reduced-order models. Int. J. Numer. Methods Eng. 80(9), 1241–1258 (2009). doi:10.1002/nme.2681

    Article  MATH  Google Scholar 

  2. Barbič J., James D.L.: Real-time subspace integration for St. Venant-Kirchhoff deformable models. ACM Trans. Graph. 24(3), 982–990 (2005). doi:10.1145/1073204.1073300

    Article  Google Scholar 

  3. Basdogan, C.: Real-time simulation of dynamically deformable finite element models using modal analysis and spectral Lanczos decomposition methods. Medicine Meets Virtual Reality, pp. 46–52. http://hdl.handle.net/2014/16481 (2001)

  4. Berkley J., Turkiyyah G., Berg D., Ganter M., Weghorst S.: Real-time finite element modeling for surgery simulation: an application to virtual suturing. Vis. Comput. Graph. IEEE Trans. 10(3), 314–325 (2004). doi:10.1109/TVCG.2004.1272730

    Article  Google Scholar 

  5. Bolzon G., Buljak V.: An effective computational tool for parametric studies and identification problems in materials mechanics. Comput. Mech. 48, 675–687 (2011). doi:10.1007/s00466-011-0611-8

    Article  MATH  Google Scholar 

  6. Breuer K.S., Sirovich L.: The use of the Karhunen-Loéve procedure for the calculation of linear eigenfunctions. J. Comput. Phys. 96(2), 277–296 (1991). doi:10.1016/0021-9991(91)90237-F

    Article  MathSciNet  MATH  Google Scholar 

  7. Bro-Nielsen, M.: Finite element modeling in surgery simulation. In: Proceedings of the IEEE, pp. 490–503 (1998)

  8. Bro-Nielsen M., Cotin S.: Real-time volumetric deformable models for surgery simulation using finite elements and condensation. Comput. Graph. Forum 15(3), 57–66 (1996). doi:10.1111/1467-8659.1530057

    Article  Google Scholar 

  9. Carlberg K., Bou-Mosleh C., Farhat C.: Efficient non-linear model reduction via a least-squares Petrov/Galerkin projection and compressive tensor approximations. Int. J. Numer. Methods Eng. 0, 1–25 (2009)

    Google Scholar 

  10. Chatterjee A.: An introduction to the proper orthogonal decomposition. Curr. Sci. 78(7), 808–817 (2000)

    Google Scholar 

  11. Chaturantabut, S.: Nonlinear model reduction via discrete empirical interpolation. PhD thesis, Rice University (2011)

  12. Chaturantabut, S., Sorensen, D.: Discrete empirical interpolation for nonlinear model reduction. In: CDC/CCC 2009. Proceedings of the 48th IEEE Conference on Decision and Control (CDC) and the Chinese Control Conference (CCC), pp. 4316–4321 (2009). doi:10.1109/CDC.2009.5400045

  13. Cotin S., Delingette H., Ayache N.: Real-time elastic deformations of soft tissues for surgery simulation. IEEE Trans. Vis. Comput. Graph. 5, 62–73 (1998). doi:10.1109/2945.764872

    Article  Google Scholar 

  14. Cotin, S., Delingette, H., Ayache, N.: A hybrid elastic model for real-time cutting, deformations, and force feedback for surgery training and simulation. Vis. Comput. 16, 437–452 (2000). doi:10.1007/PL00007215

  15. Courtecuisse, H., Jung, H., Allard, J., Duriez, C., Lee, D.Y., Cotin, S.: Gpu-based real-time soft tissue deformation with cutting and haptic feedback. Prog. Biophys. Mol. Biol. 103(23), 159–168 (2010). doi:10.1016/j.pbiomolbio.2010.09.016. <ce:title>Special Issue on Biomechanical Modelling of Soft Tissue Motion</ce:title>

  16. Cover S., Ezquerra N., O’Brien J., Rowe R., Gadacz T., Palm E.: Interactively deformable models for surgery simulation. Comput. Graph. Appl. IEEE 13(6), 68–75 (1993). doi:10.1109/38.252559

    Article  Google Scholar 

  17. De, S., Kim, J., Srinivasan, M.A.: A meshless numerical technique for physically based real-time medical simulations. In: Westwood, J. (ed.) Proceedings of Medicine Meets Virtual Reality, vol. 81, pp. 113–118. IOS Press, Amsterdam (2001)

  18. De S., Deo D., Sankaranarayanan G., Arikatla V.S.: A physics-driven neural networks-based simulation system (phynness) for multimodal interactive virtual environments involving nonlinear deformable objects. Presence Teleoper Virtual Environ. 20(4), 289–308 (2011). doi:10.1162/PRES_a_00054

    Article  Google Scholar 

  19. Debunne, G., Desbrun, M., Cani, M.P., Barr, A.H.: Dynamic real-time deformations using space & time adaptive sampling. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’01, pp. 31–36. ACM, New York (2001). doi:10.1145/383259.383262

  20. Delingette H.: Toward realistic soft-tissue modeling in medical simulation. Proc. IEEE 86(3), 512–523 (1998). doi:10.1109/5.662876

    Article  Google Scholar 

  21. Delingette, H.: Biquadratic and quadratic springs for modeling St Venant Kirchhoff materials. In: Bello, F., Edwards, P. (eds.) Biomedical Simulation, Lecture Notes in Computer Science, vol. 5104, pp. 40–48. Springer, Berlin (2008). doi:10.1007/978-3-540-70521-5_5

  22. Delingette, H., Subsol, G., Cotin, S., Pignon, J.M.: A craniofacial surgers simulation testbed. INRIA, Research Report no. 2199, pp. 1–14 (1994)

  23. Dogan F., Serdar Celebi M.: Real-time deformation simulation of non-linear viscoelastic soft tissues. Simulation 87, 179–187 (2011). doi:10.1177/0037549710364532

    Article  Google Scholar 

  24. Edmond C.V.: Impact of the endoscopic sinus surgical simulator on operating room performance. Laryngoscope 112(7), 1148–1158 (2002). doi:10.1097/00005537-200207000-00002

    Article  Google Scholar 

  25. Eichhorn K., Tingelhoff K., Wagner I., Westphal R., Rilk M., Kunkel M., Wahl F., Bootz F.: Sensorbasierte Messung mechanischer Kräfte am Endoskop während FESS. HNO 56(8), 789–794 (2008). doi:10.1007/s00106-007-1647-0

    Article  Google Scholar 

  26. Fukunaga K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1990)

    MATH  Google Scholar 

  27. Gibson, S., Samosky, J., Mor, A., Fyock, C., Grimson, E., Kanade, T., Kikinis, R., Lauer, H., McKenzie, N., Nakajima, S., Ohkami, H., Osborne, R., Sawada, A.: Simulating arthroscopic knee surgery using volumetric object representations, real-time volume rendering and haptic feedback. In: Troccaz, J., Grimson, E., Msges, R. (eds.) CVRMed-MRCAS’97, Lecture Notes in Computer Science, vol. 1205, pp. 367–378. Springer, Berlin (1997). doi:10.1007/BFb0029258

  28. Gibson, S.F.: 3d chainmail: a fast algorithm for deforming volumetric objects. In: Proceedings of the 1997 Symposium on Interactive 3D Graphics, I3D ’97, pp. 149–ff. ACM, New York (1997). doi:10.1145/253284.253324

  29. Holzapfel, G.A.: Nonlinear Solid Mechanics: A Continuum Approach for Engineering. Wiley, Baffins Lane (2000)

  30. Idelsohn S.R., Cardona A.: A reduction method for nonlinear structural dynamic analysis. Comput. Methods Appl. Mech. Eng. 49(3), 253–279 (1985). doi:10.1016/0045-7825(85)90125-2

    Article  MATH  Google Scholar 

  31. James, D.L., Pai, D.K.: Artdefo: accurate real time deformable objects. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’99, pp. 65–72. ACM Press/Addison-Wesley Publishing Co., New York (1999). doi:10.1145/311535.311542

  32. Joldes, G., Wittek, A., Couton, M., Warfield, S., Miller, K.: Real-time prediction of brain shift using nonlinear finite element algorithms. In: Yang, G.Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) Medical Image Computing and Computer-Assisted Intervention MICCAI 2009, Lecture Notes in Computer Science, vol. 5762, pp. 300–307. Springer, Berlin (2009). doi:10.1007/978-3-642-04271-3_37

  33. Kapania, R.K., Byun, C.: Reductionmethods based on eigenvectors and Ritz vectors for nonlinear transient analysis.Comput. Mech. 11, 65–82 (1993). doi:10.1007/BF00370072

    Google Scholar 

  34. Kerfriden P., Gosselet P., Adhikari S., Bordas S.: Bridging proper orthogonal decomposition methods and augmented Newton-Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems. Comput. Methods Appl. Mech. Eng. 200(58), 850–866 (2011). doi:10.1016/j.cma.2010.10.009

    Article  MathSciNet  MATH  Google Scholar 

  35. Kerschen G., Golinval J.C., Vakakis A., Bergman L.: The method of proper orthogonal decomposition for dynamical characterization and order reduction of mechanical systems: an overview. Nonlinear Dyn. 41(1–3), 147–169 (2005). doi:10.1007/s11071-005-2803-2

    Article  MathSciNet  MATH  Google Scholar 

  36. Kline K.A.: Dynamic analysis using a reduced basis of exact modes and Ritz vectors. AIAA J. 24(12), 2022–2029 (1986)

    Article  MATH  Google Scholar 

  37. Koch, R.M., Gross, M.H., Carls, F.R., von Büren, D.F., Fankhauser, G., Parish, Y.I.H.: Simulating facial surgery using finite element models. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’96, pp. 421–428. ACM, New York (1996). doi:10.1145/237170.237281

  38. Krysl, P., Lall, S., Marsden, J.E.: Dimensional model reduction in nonlinear finite element dynamics of solids and structures. Int. J. Numer. Methods Eng. 51(4), 479–504 (2001). doi:10.1002/nme.167

    Google Scholar 

  39. Kühnapfel U., Cakmak H.K., Maa H.: Endoscopic surgery training using virtual reality and deformable tissue simulation. Comput. Graph. 24, 671–682 (2000)

    Article  Google Scholar 

  40. Lenaerts, V., Kerschen, G., Golinval, J., Chevreuils, C.D.: Proper orthogonal decomposition for model updating of nonlinear mechanical systems. In: GOLINVAL 2001 Mechanical Systems and Signal Processing, pp. 31–43 (2001)

  41. Liang Y., Lee H., Lim S., Lin W., Lee K., Wu C.: Proper orthogonal decomposition and its applications part I: theory. J. Sound Vib. 252(3), 527–544 (2002). doi:10.1006/jsvi.2001.4041

    Article  MathSciNet  MATH  Google Scholar 

  42. Lim, Y.J., De, S.: Real time simulation of nonlinear tissue response in virtual surgery using the point collocation-based method of finite spheres. Comput. Methods Appl. Mech. Eng. 196(3132), 3011–3024 (2007). doi:10.1016/j.cma.2006.05.015, <ce:title>Computational Bioengineering</ce:title>

  43. Lloyd B., Székely G., Harders M.: Identification of spring parameters for deformable object simulation. Vis. Comput. Graph. IEEE Trans. 13(5), 1081–1094 (2007). doi:10.1109/TVCG.2007.1055

    Article  Google Scholar 

  44. Lloyd, B.A., Kirac, S., Székely, G., Harders, M.: Identification of dynamic mass spring parameters for deformable body simulation. In: Mania, K., Reinhard, E. (eds.) Eurographics 2008—Short Papers, pp. 131–134 (2008)

  45. Loève, M.: Probability theory. The University Series in Higher Mathematics (1963)

  46. Lumley J.L., Holmes P., Berkooz G.: Turbulence, Coherent Structures. Dynamical Systems and Symmetry. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  47. Mahvash M., Hayward V.: High-fidelity haptic synthesis of contact with deformable bodies. Comput. Graph. Appl. IEEE 24(2), 48–55 (2004). doi:10.1109/MCG.2004.1274061

    Article  Google Scholar 

  48. Meier U., Lpez O., Monserrat C., Juan M., Alcaiz M.: Real-time deformable models for surgery simulation: a survey. Comput. Methods Programs Biomed. 77(3), 183–197 (2005). doi:10.1016/j.cmpb.2004.11.002

    Article  Google Scholar 

  49. Messerklinger, W.: Zur Endoskopietechnik des mittleren Nasenganges. Eur. Arch. Oto-Rhino-Laryngol. 221, 297–305 (1978). doi:10.1007/BF00491466

  50. Meyer M., Matthies H.G.: Efficient model reduction in non-linear dynamics using the Karhunen-Loève expansion and dual-weighted-residual methods. Comput. Mech. 31, 179–191 (2003)

    Article  MATH  Google Scholar 

  51. Miller, K., Joldes, G., Lance, D., Wittek, A.: Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation. Commun. Numer. Methods Eng. 23(2), 121–134 (2007). doi:10.1002/cnm.887

  52. Miller K., Wittek A., Joldes G.: Biomechanics of the brain for computer-integrated surgery. Biomech. Brain Comput. Integr. Surg. 12(2), 25–37 (2010)

    Google Scholar 

  53. Mollemans, W., Schutyser, F., Nadjmi, N., Suetens, P.: Very fast soft tissue predictions with mass tensor model for maxillofacial surgery planning systems. Int. Congr. Ser. 1281, 491–496 (2005). doi:10.1016/j.ics.2005.03.048, cARS 2005: Computer Assisted Radiology and Surgery

    Google Scholar 

  54. Monserrat C., Meier U., Alcaiz M., Chinesta F., Juan M.: A new approach for the real-time simulation of tissue deformations in surgery simulation. Comput. Methods Programs Biomed. 64(2), 77–85 (2001). doi:10.1016/S0169-2607(00)00093-6

    Article  Google Scholar 

  55. Mourelatos Z.: An efficient crankshaft dynamic analysis using substructuring with Ritz vectors. J. Sound Vib. 238(3), 495–527 (2000). doi:10.1006/jsvi.2000.3208

    Article  Google Scholar 

  56. Natsupakpong S., Glu M.C.C.: Determination of elasticity parameters in lumped element (mass-spring) models of deformable objects. Graph. Models 72(6), 61–73 (2010). doi:10.1016/j.gmod.2010.10.001

    Article  Google Scholar 

  57. Nickell R.: Nonlinear dynamics by mode superposition. Comput. Methods Appl. Mech. Eng. 7(1), 107–129 (1976). doi:10.1016/0045-7825(76)90008-6

    Article  MathSciNet  MATH  Google Scholar 

  58. Niroomandi S., Alfaro I., Cueto E., Chinesta F.: Real-time deformable models of non-linear tissues by model reduction techniques. Comput. Methods Programs Biomed. 91(3), 223–231 (2008). doi:10.1016/j.cmpb.2008.04.008

    Article  Google Scholar 

  59. Noor A.K., Peters J.M.: Reduced basis technique for nonlinear analysis of structures. AIAA J. 18(4), 455–462 (1980). doi:10.2514/3.50778

    Article  Google Scholar 

  60. Picinbono, G., Delingette, H., Ayache, N.: Non-linear and anisotropic elastic soft tissue models for medical simulation. In: Proceedings of the 2001 IEEE, International Conference on Robotics 8 Automation, Seoul, Korea (2001)

  61. Radermacher, A., Reese, S.: Model reduction for complex continua at the example of modeling soft tissue in the nasal area. In: Markert, B. (ed.) Advances in Extended and Multifield Theories for Continua, Lecture Notes in Applied and Computational Mechanics, vol. 59, pp. 197–217. Springer, Berlin (2011). doi:10.1007/978-3-642-22738-7_10

  62. Reese S.: On a physically stabilized one point finite element formulation for three-dimensional finite elasto-plasticity. Comput. Methods Appl. Mech. Eng. 194(45–47), 4685–4715 (2005). doi:10.1016/j.cma.2004.12.012

    Article  MATH  Google Scholar 

  63. Reese, S., Wriggers, P., Reddy, B.: A new locking-free brick element technique for large deformation problems in elasticity. Comput. Struct. 75(3), 291–304 (2000). doi:10.1016/S0045-7949(99)00137-6

    Google Scholar 

  64. Remke, J., Rothert, H.: Eine modale Reduktionsmethode zur geometrisch nichtlinearen statischen und dynamischen Finite-Element-Berechnung. Arch. Appl. Mech. 63, 101–115 (1993). doi:10.1007/BF00788916

  65. Remseth S.: Nonlinear static and dynamic analysis of framed structures. Comput. Struct. 10(6), 879–897 (1979). doi:10.1016/0045-7949(79)90057-9

    Article  MATH  Google Scholar 

  66. Rickelt-Rolf, C.: Modellreduktion und Substrukturtechnik zur effizienten Simulation dynamischer, teilgeschädigter Systeme. PhD thesis, Technische Universität Carolo-Wilhelmina zu Braunschweig (2009)

  67. Rudman D.T., Stredney D., Sessanna D., Yagel R., Crawfis R., Heskamp D., Edmond C.V., Wiet G.J.: Functional endoscopic sinus surgery training simulator. Laryngoscope 108(11), 1643–1647 (1998). doi:10.1097/00005537-199811000-00010

    Article  Google Scholar 

  68. Ryckelynck D., Benziane D.M.: Multi-level a priori hyper-reduction of mechanical models involving internal variables. Comput. Methods Appl. Mech. Eng. 199(1720), 1134–1142 (2010). doi:10.1016/j.cma.2009.12.003

    Article  MATH  Google Scholar 

  69. Schwartz J.M., Denninger M., Rancourt D., Moisan C., Laurendeau D.: Modelling liver tissue properties using a non-linear visco-elastic model for surgery simulation. Med. Image Anal. 9, 103–112 (2005)

    Article  Google Scholar 

  70. Simo J.: On a fully three-dimensional finite-strain viscoelastic damage model: formulation and computational aspects. Comput. Methods Appl. Mech. Eng. 60(2), 153–173 (1987). doi:10.1016/0045-7825(87)90107-1

    Article  MathSciNet  MATH  Google Scholar 

  71. Solyar A., Cuellar H., Sadoughi B., Olson T.R., Fried M.P.: Endoscopic sinus surgery simulator as a teaching tool for anatomy education. Am. J. Surg. 196(1), 120–124 (2008). doi:10.1016/j.amjsurg.2007.06.026

    Article  Google Scholar 

  72. Spiess H., Wriggers P.: Reduction methods for FE analysis in nonlinear structural dynamics. PAMM 5(1), 135–136 (2005). doi:10.1002/pamm.200510048

    Article  Google Scholar 

  73. Stammberger H., Posawetz W.: Clinical review—functional endoscopic sinus surgery—concept, indications and results of the Messerklinger technique. Eur. Arch. Oto-Rhino-Laryngol. 247, 63–76 (1990)

    Article  Google Scholar 

  74. Taylor, R.: Feap—a Finite Element Analysis Program, Version 8.3 User manual. University of California at Berkeley, wwwceberkeleyedu/projects/feap (2011)

  75. Taylor Z., Cheng M., Ourselin S.: High-speed nonlinear finite element analysis for surgical simulation using graphics processing units. Med. Imaging IEEE Trans. 27(5), 650–663 (2008). doi:10.1109/TMI.2007.913112

    Article  Google Scholar 

  76. Terzopoulost D., Platt J., Barr A., Fleischert K.: Elastically deformable models. Comput. Graph. 21, 205–214 (1987)

    Article  Google Scholar 

  77. Volkwein S.: Optimal control of a phase-field model using proper orthogonal decomposition. ZAMM J. Appl. Math. Mech. 81(2), 83–97 (2001). doi:10.1002/1521-4001(200102)81:2<83::AID-ZAMM83>3.0.CO;2-R

    Article  MathSciNet  MATH  Google Scholar 

  78. Volkwein S., Hepberger A.: Impedance identification by POD model reduction techniques. at-Automatisierungstechnik 56(8), 437–446 (2008)

    Article  Google Scholar 

  79. Wilson E.L.: A new method of dynamic analysis for linear and nonlinear systems. Finite Elem. Anal. Design 1(1), 21–23 (1985). doi:10.1016/0168-874X(85)90004-6

    Article  MATH  Google Scholar 

  80. Xu S., Liu X., Zhang H., Hu L.: A nonlinear viscoelastic tensor-mass visual model for surgery simulation. Instrum. Meas. IEEE Trans. 60(1), 14–20 (2011). doi:10.1109/TIM.2010.2065450

    Article  MathSciNet  Google Scholar 

  81. Zhang D., Wang T., Liu D., Lin G.: Vascular deformation for vascular interventional surgery simulation. Int. J. Med. Robotics Comput. Assist. Surg. 6(2), 171–177 (2010). doi:10.1002/rcs.302

    Google Scholar 

  82. Zhuang, Y., Canny, J.: Haptic interaction with global deformations. In: Robotics and Automation, 2000. Proceedings. ICRA ’00. IEEE International Conference on, vol. 3, pp. 2428–2433 (2000). doi:10.1109/ROBOT.2000.846391

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Radermacher, A., Reese, S. A comparison of projection-based model reduction concepts in the context of nonlinear biomechanics. Arch Appl Mech 83, 1193–1213 (2013). https://doi.org/10.1007/s00419-013-0742-9

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