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Biomechanics of the Brain pp 111–136Cite as

Biomechanical Modeling of the Brain for Computer-Assisted Neurosurgery

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Part of the book series: Biological and Medical Physics, Biomedical Engineering ((BIOMEDICAL))

Abstract

During neurosurgery, the brain significantly deforms. Despite the enormous complexity of the brain (see Chap. 2) many aspects of its response can be reasonably described in purely mechanical terms, such as displacements, strains and stresses. They can therefore be analyzed using established methods of continuum mechanics. In this chapter, we discuss approaches to biomechanical modeling of the brain from the perspective of two distinct applications: neurosurgical simulation and neuroimage registration in image-guided surgery. These two challenging applications are described below.1

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Notes

  1. 1.

    Parts of this chapter were previously published in Miller et al. [1] and Wittek et al. [2].

References

  1. Miller, K., Wittek, A., Joldes, G., et al.: Modelling brain deformations for computer-integrated neurosurgery. Int. J. Numer. Methods Biomed. Eng. 26, 117–138 (2010)

    Article  MATH  Google Scholar 

  2. Wittek, A., Joldes, G., Couton, M., et al.: Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time; application to non-rigid neuroimage registration. Prog. Biophys. Mol. Biol. 103, 292–303 (2010)

    Article  Google Scholar 

  3. Dimaio, S.P., Salcudean, S.E.: Interactive simulation of needle insertion models. IEEE Trans. Biomed. Eng. 52, 1167–1179 (2005)

    Article  Google Scholar 

  4. Bucholz, R., MacNeil, W., McDurmont, L.: The operating room of the future. Clin. Neurosurg. 51, 228–237 (2004)

    Google Scholar 

  5. Nakaji, P., Speltzer, R.F.: The marriage of technique, technology, and judgement. Innov. Surg. Approach 51, 177–185 (2004)

    Google Scholar 

  6. Ferrant, M., Nabavi, A., Macq, B., et al.: Serial registration of intraoperative MR images of the brain. Med. Image Anal. 6, 337–359 (2002)

    Article  Google Scholar 

  7. Warfield, S.K., Haker, S.J., Talos, F., et al.: Capturing intraoperative deformations: research experience at Brigham and Women’s Hospital. Med. Image Anal. 9, 145–162 (2005)

    Article  Google Scholar 

  8. Warfield, S.K., Talos, F., Tei, A., et al.: Real-time registration of volumetric brain MRI by biomechanical simulation of deformation during image guided surgery. Comput. Vis. Sci. 5, 3–11 (2002)

    Article  Google Scholar 

  9. Wittek, A., Miller, K., Kikinis, R., et al.: Patient-specific model of brain deformation: application to medical image registration. J. Biomech. 40, 919–929 (2007)

    Article  MATH  Google Scholar 

  10. Joldes, G., Wittek, A., Couton, M., et al.: Real-time prediction of brain shift using nonlinear finite element algorithms. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. Springer, Berlin (2009a)

    Google Scholar 

  11. Owen, S.J.: A survey of unstructured mesh generation technology. In: LAB, S. N., ed. 7th International Meshing Roundtable, Dearborn, Michigan, USA, pp. 239–267, October 1998

    Google Scholar 

  12. Owen, S.J.: Hex-dominant mesh generation using 3D constrained triangulation. Comput. Aided Des. 33, 211–220 (2001)

    Article  MATH  Google Scholar 

  13. Viceconti, M., Taddei, F.: Automatic generation of finite element meshes from computed tomography data. Crit. Rev. Biomed. Eng. 31, 27–72 (2003)

    Article  Google Scholar 

  14. Castellano-Smith, A.D., Hartkens, T., Schnabel, J., et al.: Constructing patient specific models for correcting intraoperative brain deformation. In: 4th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI, Lecture Notes in Computer Science 2208, Utrecht, The Netherlands, pp. 1091–1098 (2001)

    Google Scholar 

  15. Couteau, B., Payan, Y., Lavallée, S.: The Mesh-Matching algorithm: an automatic 3D Mesh generator for finite element structures. J. Biomech. 33, 1005–1009 (2000)

    Article  MATH  Google Scholar 

  16. Luboz, V., Chabanas, M., Swider, P., et al.: Orbital and maxillofacial computer aided surgery: patient-specific finite element models to predict surgical outcomes. Comput. Meth. Biomech. Biomed. Eng. 8, 259–265 (2005)

    Article  Google Scholar 

  17. Clatz, O., Delingette, H., Bardinet, E., et al.: Patient specific biomechanical model of the brain: application to Parkinson’s disease procedure. In: Ayache, N., Delingette, H. (eds.) International Symposium on Surgery Simulation and Soft Tissue Modeling (IS4TM’03), pp. 321–331. Springer, Juan-les-Pins, France (2003)

    Article  Google Scholar 

  18. Ferrant, M., Macq, B., Nabavi, A., et al.: Deformable modeling for characterizing biomedical shape changes. In: Borgefors, I.N.G., Sanniti di Baja, G. (eds.) Discrete Geometry for Computer Imagery: 9th International Conference, Uppsala, Sweden, pp. 235–248. Springer, London (2000)

    Chapter  Google Scholar 

  19. Belytschko, T., Lu, Y.Y., Gu, L.: Element-free Galerkin methods. Int. J. Numer. Methods Eng. 37, 229–256 (1994)

    Article  MATH  Google Scholar 

  20. Horton, A., Wittek, A., Joldes, G., et al.: A meshless Total Lagrangian explicit dynamics algorithm for surgical simulation. Int. J. Numer. Methods Biomed. Eng. 26, 117–138 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  21. Horton, A., Wittek, A., Miller, K.: Computer simulation of brain shift using an element free galerkin method. In: Middleton, J., Jones, M. (eds.) 7th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering CMBEE 2006, Antibes, France (2006a)

    Google Scholar 

  22. Horton, A., Wittek, A., Miller, K.: Towards meshless methods for surgical simulation. In: Computational Biomechanics for Medicine Workshop, Medical Image Computing and Computer-Assisted Intervention MICCAI 2006, Copenhagen, Denmark, pp. 34–42 (2006b)

    Google Scholar 

  23. Horton, A., Wittek, A., Miller, K.: Subject-specific biomechanical simulation of brain indentation using a meshless method. In: Ayache, N., Ourselin, S., Maeder, A., eds. International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2007, Brisbane, Australia, pp. 541–548. Springer, Berlin, 29 October to 2 November (2007)

    Google Scholar 

  24. Li, S., Liu, W.K.: Meshfree Particle Methods. Springer, Berlin (2004)

    MATH  Google Scholar 

  25. Liu, G.R.: Mesh Free Methods: Moving Beyond the Finite Element Method. CRC, Boca Raton (2003)

    MATH  Google Scholar 

  26. Hagemann, A., Rohr, K., Stiehl, H.S., et al.: Biomechanical modeling of the human head for physically based, nonrigid image registration. IEEE Trans. Med. Imaging 18, 875–884 (1999)

    Article  MATH  Google Scholar 

  27. Miga, M.I., Paulsen, K.D., Hoopes, P.J., et al.: In vivo quantification of a homogenous brain deformation model for updating preoperative images during surgery. IEEE Trans. Biomed. Eng. 47, 266–273 (2000)

    Article  Google Scholar 

  28. Dutta-Roy, T., Wittek, A., Miller, K.: Biomechanical modelling of normal pressure hydrocephalus. J. Biomech. 41, 2263–2271 (2008)

    Article  Google Scholar 

  29. Hu, J., Jin, X., Lee, J.B., et al.: Intraoperative brain shift prediction using a 3D inhomogeneous patient-specific finite element model. J. Neurosurg. 106, 164–169 (2007)

    Article  Google Scholar 

  30. Wittek, A., Hawkins, T., Miller, K.: On the unimportance of constitutive models in computing brain deformation for image-guided surgery. Biomech. Model. Mechanobiol. 8, 77–84 (2009)

    Article  Google Scholar 

  31. Wittek, A., Kikinis, R., Warfield, S.K., et al.: Brain shift computation using a fully nonlinear biomechanical model. In: 8th International Conference on Medical Image Computing and Computer Assisted Surgery MICCAI 2005, Palm Springs, California, USA (2005)

    Google Scholar 

  32. Wittek, A., Omori, K.: Parametric study of effects of brain-skull boundary conditions and brain material properties on responses of simplified finite element brain model under angular acceleration in sagittal plane. JSME Int. J. 46, 1388–1398 (2003)

    Article  MATH  Google Scholar 

  33. Joldes, G., Wittek, A., Miller, K.: Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation. Med. Image Anal. 13, 912–919 (2009)

    Article  Google Scholar 

  34. Joldes, G.R., Wittek, A., Miller, K., et al.: Realistic and efficient brain-skull interaction model for brain shift computation. In: Karol Miller, P.M.F.N. (ed.) Computational Biomechanics for Medicine III Workshop, MICCAI 2008, New-York, pp. 95–105 (2008)

    Google Scholar 

  35. Jin, X.: Biomechanical response and constitutive modeling of bovine pia-arachnoid complex. PhD thesis, Wayne State University (2009)

    Google Scholar 

  36. Miller, K., Wittek, A.: Neuroimage registration as displacement – zero traction problem of solid mechanics. In: Miller, K., Poulikakos, D. (eds.) Computational Biomechanics for Medicine MICCAI-associated Workshop, Copenhagen, pp. 1–14 (2006)

    Google Scholar 

  37. Miga, M.I., Sinha, T.K., Cash, D.M., et al.: Cortical surface registration for image-guided neurosurgery using laser-range scanning. IEEE Trans. Med. Imaging 22, 973–985 (2003)

    Article  MATH  Google Scholar 

  38. Miller, K.: How to test very soft biological tissues in extension. J. Biomech. 34, 651–657 (2001)

    Article  Google Scholar 

  39. Miller, K.: Biomechanics without mechanics: calculating soft tissue deformation without differential equations of equilibrium. In: Middleton, J., Shrive, N., Jones, M. (eds.) 5th Symposium on Computer Methods in Biomechanics and Biomedical Engineering CMBBE2004 Madrid, Spain. First Numerics (2005b)

    Google Scholar 

  40. Miller, K.: Method for testing very soft biological tissues in compression. J. Biomech. 38, 153–158 (2005)

    Article  MATH  Google Scholar 

  41. Ferrant, M., Nabavi, A., Macq, B., et al.: Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model. IEEE Trans. Med. Imaging 20, 1384–1397 (2001)

    Google Scholar 

  42. Fung, Y.C.: A First Course in Continuum Mechanics. Prentice-Hall, London (1969)

    Article  Google Scholar 

  43. Ciarlet, P.G.: Mathematical Elasticity. North Hollad, The Netherlands (1988)

    MATH  Google Scholar 

  44. Nowinski, W.L.: Modified Talairach landmarks. Acta Neurochir. 143, 1045–1057 (2001)

    Article  MATH  Google Scholar 

  45. Miller, K.: Biomechanics without mechanics: calculating soft tissue deformation without differential equations of equilibrium. In: Middleton, J., Shrive, N., Jones, M. (eds.) 5th Symposium on Computer Methods in Biomechanics and Biomedical Engineering, Madrid, Spain. First Numerics (2005a)

    Google Scholar 

  46. Dumpuri, P., Thompson, R.C., Dawant, B.M., et al.: An atlas-based method to compensate for brain shift: preliminary results. Med. Image Anal. 11, 128–145 (2007)

    Article  MATH  Google Scholar 

  47. Dutta Roy, T.: Does normal pressure hydrocephalus have mechanistic causes? PhD Thesis, The University of Western Australia (2010)

    Google Scholar 

  48. Miller, K., Chinzei, K.: Constitutive modelling of brain tissue: experiment and theory. J. Biomech. 30, 1115–1121 (1997)

    Article  MATH  Google Scholar 

  49. Bilston, L., Liu, Z., Phan-Tiem, N.: Large strain behaviour of brain tissue in shear: some experimental data and differential constitutive model. Biorheology 38, 335–345 (2001)

    Article  Google Scholar 

  50. Farshad, M., Barbezat, M., Flüeler, P., et al.: Material characterization of the pig kidney in relation with the biomechanical analysis of renal trauma. J. Biomech. 32, 417–425 (1999)

    Google Scholar 

  51. Mendis, K.K., Stalnaker, R.L., Advani, S.H.: A constitutive relationship for large deformation finite element modeling of brain tissue. J. Biomech. Eng. 117, 279–285 (1995)

    Article  Google Scholar 

  52. Miller, K.: Constitutive model of brain tissue suitable for finite element analysis of surgical procedures. J. Biomech. 32, 531–537 (1999)

    Article  Google Scholar 

  53. Miller, K.: Constitutive modelling of abdominal organs. J. Biomech. 33, 367–373 (2000)

    Article  Google Scholar 

  54. Miller, K., Chinzei, K.: Mechanical properties of brain tissue in tension. J. Biomech. 35, 483–490 (2002)

    Article  Google Scholar 

  55. Miller, K., Chinzei, K., Orssengo, G., et al.: Mechanical properties of brain tissue in-vivo: experiment and computer simulation. J. Biomech. 33, 1369–1376 (2000)

    Article  Google Scholar 

  56. Nasseri, S., Bilston, L.E., Phan-Thien, N.: Viscoelastic properties of pig kidney in shear, experimental results and modelling. Rheol. Acta 41, 180–192 (2002)

    Article  Google Scholar 

  57. Walsh, E.K., Schettini, A.: Calculation of brain elastic parameters in vivo. Am. J. Physiol. 247, R637–R700 (1984)

    Article  Google Scholar 

  58. Bilston, L.E., Liu, Z., Phan-Tien, N.: Linear viscoelastic properties of bovine brain tissue in shear. Biorheology 34, 377–385 (1997)

    Google Scholar 

  59. Miller, K.: Biomechanics of Brain for Computer Integrated Surgery. Publishing House of Warsaw University of Technology, Warsaw (2002)

    Article  Google Scholar 

  60. Prange, M.T., Margulies, S.S.: Regional, directional, and age-dependent properties of the brain undergoing large deformation. J. Biomech. Eng. 124, 244–252 (2002)

    Article  MATH  Google Scholar 

  61. Salcudean, S., Turgay, E., Rohling, R.: Identifying the mechanical properties of tissue by ultrasound strain imaging. Ultrasound Med. Biol. 32, 221–235 (2006)

    Article  Google Scholar 

  62. Mccracken, P.J., Manduca, A., Felmlee, J., et al.: Mechanical transient-based magnetic resonance elastography. Magn. Reson. Med. 53, 628–639 (2005)

    Article  Google Scholar 

  63. Sinkus, R., Tanter, M., Xydeas, T., et al.: Viscoelastic shear properties of in vivo breast lesions measured by MR elastography. Magn. Reson. Imaging 23, 159–165 (2005)

    Article  Google Scholar 

  64. Green, M.A., Bilston, L.E., Sinkus, R.: In vivo brain viscoelastic properties measured by magnetic resonance elastography. NMR Biomed. 21(7), 755–764 (2008)

    Article  Google Scholar 

  65. Bathe, K.-J.: Finite Element Procedures. Prentice-Hall, New Jersey (1996)

    Google Scholar 

  66. Ma, J., Wittek, A., Singh, S., et al.: Evaluation of accuracy of non-linear finite element computations for surgical simulation: study using brain phantom. Comput. Methods Biomech. Biomed. Eng. 13, 783–794 (2010)

    Article  Google Scholar 

  67. Wittek, A., Dutta-Roy, T., Taylor, Z., et al.: Subject-specific non-linear biomechanical model of needle insertion into brain. Comput. Methods Biomech. Biomed. Eng. J. 11, 135–146 (2008)

    Article  Google Scholar 

  68. Grosland, N.M., Shivanna, K.H., Magnotta, V.A., et al.: IA-FEMesh: an open-source, interactive, multiblock approach to anatomic finite element model development. Comput. Meth. Programs Biomed. 94, 96–107 (2009)

    Article  Google Scholar 

  69. Ito, Y., Shih, A.M., Soni, B.K.: Octree-based reasonable-quality hexahedral mesh generation using a new set of refinement templates. Int. J. Numer. Methods Eng. 77, 1809–1833 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  70. Shepherd, J.F., Zhang, Y., Tuttle, C.J., et al.: Quality improvement and boolean-like cutting operations in hexahedral meshes. In: 10th Conference of the International Society of Grid Generation, Crete, Greece (2007)

    Google Scholar 

  71. Joldes, G.R., Wittek, A., Miller, K.: Non-locking tetrahedral finite element for surgical simulation. Commun. Numer. Methods Eng. 25, 827–836 (2008)

    Article  MathSciNet  Google Scholar 

  72. Joldes, G.R., Wittek, A., Miller, K.: An efficient hourglass control implementation for the uniform strain hexahedron using the Total Lagrangian formulation. Commun. Numer. Methods Eng. 24, 1315–1323 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  73. Arganda-Carreras, I., Sorzano, S.C.O., Marabini, R., et al.: Consistent and elastic registration of histological sections using vector-spline regularization. International Conference on Computer Vision Approaches to Medical Image Analysis. LNCS. Springer, Setubal, Portugal (2006)

    Google Scholar 

  74. Joldes, G.R., Wittek, A., Miller, K.: Real-time nonlinear finite element computations on GPU – application to neurosurgical simulation. Comput. Methods Appl. Mech. Eng. 199, 3305–3314 (2010)

    Article  ADS  Google Scholar 

  75. Skrinjar, O., Nabavi, A., Duncan, J.: Model-driven brain shift compensation. Med. Image Anal. 6, 361–373 (2002)

    Article  Google Scholar 

  76. Warfield, S.K., Ferrant, M., Gallez, X., et al.: Real-time biomechanical simulation of volumetric brain deformation for image guided neurosurgery. In: SC 2000: High Performance Networking and Compting Conference, Dallas, USA, pp. 1–16 (2000)

    Google Scholar 

  77. Miga, M.I., Roberts, D.W., Kennedy, F.E., et al.: Modeling of retraction and resection for intraoperative updating of images. Neurosurgery 49, 75–85 (2001)

    Google Scholar 

  78. Yeoh, O.H.: Some forms of strain-energy function for rubber. Rubber Chem. Technol. 66, 754–771 (1993)

    Article  Google Scholar 

  79. Chakravarty, M.M., Sadikot, A.F., Germann, J., et al.: Towards a validation of atlas warping techniques. Med. Image Anal. 12, 713–726 (2008)

    Article  Google Scholar 

  80. Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vision 24, 137–154 (1997)

    Article  Google Scholar 

  81. Wells Iii, W.M., Viola, P., Atsumi, H., et al.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1, 35–51 (1996)

    Article  Google Scholar 

  82. Rexilius, J., Warfield, S., Guttmann, C., et al.: A novel nonrigid registration algorithm and applications. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001, Toronto, Cananda (2001)

    Google Scholar 

  83. Miga, M.I., Paulsen, K.D., Lemery, J.M., et al.: Model-updated image guidance: initial clinical experiences with gravity-induced brain deformation. IEEE Trans. Med. Imaging 18, 866–874 (1999)

    Article  Google Scholar 

  84. Oden, J.T., Belytschko, T., Babuska, I., et al.: Research directions in computational mechanics. Comput. Meth. Appl. Mech. Eng. 192, 913–922 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  85. Berger, J., Horton, A., Joldes, G., et al.: Coupling finite element and mesh-free methods for modelling brain defromations in response to tumour growth. In: Miller, K., Nielsen, P.M.F. (eds.) Computational Biomechanics for Medicine III MICCAI-Associated Workshop, 2008. MICCAI, New York (2008)

    Google Scholar 

  86. Miller, K., Taylor, Z., Nowinski, W.L.: Towards computing brain deformations for diagnosis, prognosis and nerosurgical simulation. J. Mech. Med. Biol. 5, 105–121 (2005)

    Article  Google Scholar 

  87. Taylor, Z., Miller, K.: Reassessment of brain elasticity for analysis of biomechanisms of hydrocephalus. J. Biomech. 37, 1263–1269 (2004)

    Article  Google Scholar 

  88. Miller, K., Joldes, G., Lance, D., et al.: Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation. Commun. Numer. Methods Eng. 23, 121–134 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  89. Zienkiewicz, O.C., Taylor, R.L.: The Finite Element Method. McGraw-Hill, London (2000)

    MATH  Google Scholar 

  90. Roberts, D.W., Hartov, A., Kennedy, F.E., et al.: Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43, 749–758 (1998)

    Article  Google Scholar 

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Acknowledgements

The financial support of the Australian Research Council (Grants No. DP0343112, DP0664534 and LX0560460), National Health and Medical Research Council Grant No. 1006031 and NIH (Grant No. 1-RO3-CA126466-01A1) is gratefully acknowledged. We thank our collaborators Dr Ron Kikinis and Dr Simon Warfield from Harvard Medical School and Dr Kiyoyuki Chinzei and Dr Toshikatsu Washio from Surgical Assist Technology Group of AIST, Japan, for help in various aspects of our work.

The medical images used in the present study (provided by Dr Simon Warfield) were obtained in the investigation supported by a research grant from the Whitaker Foundation and by NIH grants R21 MH67054, R01 LM007861, P41 RR13218 and P01 CA67165.

We thank Toyota Central R&D Labs. (Nagakute, Aichi, Japan) for providing the THUMS brain model.

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Authors and Affiliations

  1. Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, 35 Stirling Highway, Crawley/Perth, WA, 6009, Australia

    K. Miller

  2. Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Crawley/Perth, WA, 6009, Australia

    A. Wittek & G. Joldes

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  1. K. Miller
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  2. A. Wittek
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  3. G. Joldes
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Correspondence to K. Miller .

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  1. Intelligent Systems for Medicine Lab., The University of Western Australia, Stirling Highway 35, Crawley/Perth, 6009, West Australia, Australia

    Karol Miller

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Miller, K., Wittek, A., Joldes, G. (2011). Biomechanical Modeling of the Brain for Computer-Assisted Neurosurgery. In: Miller, K. (eds) Biomechanics of the Brain. Biological and Medical Physics, Biomedical Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9997-9_6

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