Annals of Biomedical Engineering

, Volume 41, Issue 11, pp 2409–2425 | Cite as

Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration

  • Ahmed Mostayed
  • Revanth Reddy Garlapati
  • Grand Roman Joldes
  • Adam Wittek
  • Aditi Roy
  • Ron Kikinis
  • Simon K. Warfield
  • Karol Miller
Article

Abstract

In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the BSpline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.

Keywords

Brain Non-rigid registration Intra-operative MRI Biomechanics Edge detection Hausdorff distance Cerebral gliomas 

References

  1. 1.
    ABAQUS, ABAQUS Theory Manual Version 5.2. 1998, Hibbit, Karlsson & Sorensen, Inc.Google Scholar
  2. 2.
    Black, P. Management of malignant glioma: role of surgery in relation to multimodality therapy. J. Neurovirol. 4:227–236, 1998.PubMedCrossRefGoogle Scholar
  3. 3.
    Box, G. E. P., W. G. Hunter, and J. S. Hunter. Statistics for Experimenters. An introduction to Design, Data Analysis, and Model Building. New York: John Wiley & Sons, 1978.Google Scholar
  4. 4.
    Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8:679–698, 1986.PubMedCrossRefGoogle Scholar
  5. 5.
    Fedorov, A., E. Billet, M. Prastawa, G. Gerig, A. Radmanesh, S. K. Warfield, R. Kikinis, and N. Chrisochoides. Evaluation of brain MRI alignment with the Robust Hausdorff distance measures. In: Advances in Visual Computing, Pt I, edited by G. Bebis. 2008, pp. 594–603.Google Scholar
  6. 6.
    Garlapati, R. R., G. Joldes, A. Wittek, J. Lam, N. Weisenfeld, A. Hans, S. K. Warfield, R. Kikinis, and K. Miller. Objective evaluation of accuracy of intra-operative neuroimage registration. In: Computational Biomechanics for Medicine VII: Models, Algorithms and Implementations, edited by A. Wittek, K. Miller, and P. M. F. Nielsen. New York: Springer, 2013, pp. 87–99.CrossRefGoogle Scholar
  7. 7.
    Grosland, N. M., K. H. Shivanna, V. A. Magnotta, N. A. Kallemeyn, N. A. DeVries, S. C. Tadepalli, and C. Lislee. IA-FEMesh: an open-source, interactive, multiblock approach to anatomic finite element model development. Comput. Methods Programs Biomed. 94:96–107, 2009.PubMedCrossRefGoogle Scholar
  8. 8.
    Hill, D. L. G. and P. Batchelor. Registration methodology: concepts and algorithms. In: Medical Image Registration, edited by J. V. Hanjal, D. L. G. Hill, and D. J. Hawkes. CRC Press, 2001, pp. 39–70.Google Scholar
  9. 9.
    Huttenlocher, D. P., G. A. Klanderman, and W. J. Rucklidge. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15:850–863, 1993.CrossRefGoogle Scholar
  10. 10.
    Ji, S. B., X. Y. Fan, D. W. Roberts, and K. D. Paulsen. Cortical surface strain estimation using stereovision. In: Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011, Pt I, edited by G. Fichtinger, A. Martel, and T. Peters. 2011, pp. 412–419.Google Scholar
  11. 11.
    Ji, S. B., D. W. Roberts, A. Hartov, and K. D. Paulsen. Brain-skull contact boundary conditions in an inverse computational deformation model. Med. Image Anal. 13:659–672, 2009.PubMedCrossRefGoogle Scholar
  12. 12.
    Jin, X., G. R. Joldes, K. Miller, K. H. Yang, and A. Wittek. Meshless algorithm for soft tissue cutting in surgical simulation. Comput. Methods Biomech. Biomed. Eng. 2012. doi:10.1080/10255842.2012.716829
  13. 13.
    Jin, J. Y., F. F. Yin, S. E. Tenn, P. M. Medin, and T. D. Solberg. Use of the BrainLAB exactrac X-ray 6D system in image-guided radiotherapy. Med. Dosim. 33:124–134, 2008.PubMedCrossRefGoogle Scholar
  14. 14.
    Joldes, G. R., A. Wittek, M. Couton, S. K. Warfield, and K. Miller. Real-time prediction of brain shift using nonlinear finite element algorithms. In: Medical Image Computing and Computer-Assisted Intervention—Miccai 2009, Pt II, edited by G. Z. Yang, D. Hawkes, D. Rueckert, A. Nobel, and C. Taylor. Berlin/Heidelberg: Springer, 2009, pp. 300–307.CrossRefGoogle Scholar
  15. 15.
    Joldes, G. R., A. Wittek, and K. Miller. Computation of intra-operative brain shift using dynamic relaxation. Comput. Meth. Appl. Mech. Eng. 198:3313–3320, 2009.CrossRefGoogle Scholar
  16. 16.
    Joldes, G. R., A. Wittek, and K. Miller. Non-locking tetrahedral finite element for surgical simulation. Commun. Numer. Methods Eng. 25:827–836, 2009.CrossRefGoogle Scholar
  17. 17.
    Joldes, G. R., A. Wittek, and K. Miller. Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation. Med. Image Anal. 13:912–919, 2009.PubMedCrossRefGoogle Scholar
  18. 18.
    Joldes, G. R., A. Wittek, and K. Miller. Cortical surface motion estimation for brain shift prediction. In: Computational Biomechanics for Medicine, edited by K. Miller, and P. M. F. Nielsen. New York: Springer, 2010, pp. 53–62.CrossRefGoogle Scholar
  19. 19.
    Joldes, G. R., A. Wittek, and K. Miller. Real-time nonlinear finite element computations on GPU—application to neurosurgical simulation. Comput. Meth. Appl. Mech. Eng. 199:3305–3314, 2010.CrossRefGoogle Scholar
  20. 20.
    Joldes, G. R., A. Wittek, and K. Miller. An adaptive dynamic relaxation method for solving nonlinear finite element problems. Application to brain shift estimation. Int. J. Numer. Methods Biomed. Eng. 27:173–185, 2011.CrossRefGoogle Scholar
  21. 21.
    Joldes, G., A. Wittek, K. Miller, and L. Morriss. Realistic and efficient brain-skull interaction model for brain shift computation. In: Computational Biomechanics for Medicine III, edited by K. Miller, and P. M. F. Nielsen. New York: Springer, 2008.Google Scholar
  22. 22.
    Klein, A., J. Andersson, B. A. Ardekani, J. Ashburner, B. Avants, M.-C. Chiang, G. E. Christensen, D. L. Collins, J. Gee, P. Hellier, J. H. Song, M. Jenkinson, C. Lepage, D. Rueckert, P. Thompson, T. Vercauteren, R. P. Woods, J. J. Mann, and R. V. Parsey. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46:786–802, 2009.PubMedCrossRefGoogle Scholar
  23. 23.
    Kybic, J., P. Thevenaz, A. Nirkko, and M. Unser. Unwarping of unidirectionally distorted EPI images. IEEE Trans. Med. Imaging 19:80–93, 2000.PubMedCrossRefGoogle Scholar
  24. 24.
    Kybic, J., and M. Unser. Fast parametric elastic image registration. IEEE Trans. Image Process. 12:1427–1442, 2003.PubMedCrossRefGoogle Scholar
  25. 25.
    Lee, S.-Y., K.-Y. Chwa, and S. Y. Shin. Image metamorphosis using snakes and free-form deformations. In: 22nd Annual Conference on Computer Graphics and Interactive Techniques. ACM, 1995.Google Scholar
  26. 26.
    Lee, S., G. Wolberg, and S. Y. Shin. Scattered data interpolation with multilevel B-splines. IEEE Trans. Visual Comput. Graphics 3:228–244, 1997.CrossRefGoogle Scholar
  27. 27.
    Ma, J. J., A. Wittek, B. Zwick, G. R. Joldes, S. K. Warfield, and K. Miller. On the effects of model complexity in computing brain deformation for image-guided neurosurgery. In: Computational Biomechanics for Medicine: Soft Tissues and the Musculoskeletal System, edited by A. Wittek, P. M. F. Nielsen, and K. Miller. New York: Springer, 2011, pp. 51–61.CrossRefGoogle Scholar
  28. 28.
    Mattes, D., D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank. Nonrigid multimodality image registration. In: Medical Imaging: 2001: Image Processing, Pts 1–3, edited by M. Sonka and K. M. Hanson. pp. 1609–1620, 2001.Google Scholar
  29. 29.
    Mattes, D., D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank. PET-CT image registration in the chest using free-form deformations. IEEE Trans. Med. Imaging 22:120–128, 2003.PubMedCrossRefGoogle Scholar
  30. 30.
    Miller, K. Introduction. In: Biomechanics of the Brain, edited by K. Miller. New York: Springer, 2011, pp. 1–3.CrossRefGoogle Scholar
  31. 31.
    Miller, K., and K. Chinzei. Mechanical properties of brain tissue in tension. J. Biomech. 35:483–490, 2002.PubMedCrossRefGoogle Scholar
  32. 32.
    Miller, K., G. Joldes, D. Lance, and A. Wittek. Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation. Commun. Numer. Methods Eng. 23:121–134, 2007.CrossRefGoogle Scholar
  33. 33.
    Miller, K., and J. Lu. On the prospect of patient-specific biomechanics without patient-specific properties of tissues. J. Mech. Behav. Biomed. 2013. doi:10/1016/j.jmbbm.2013.01.2013.Google Scholar
  34. 34.
    Miller, K., and A. Wittek. Neuroimage registration as displacement—zero traction problem of solid mechanics. In: Computational Biomechanics for Medicine, edited by K. Miller, and P. M. F. Nielsen. New York: Springer, 2006.Google Scholar
  35. 35.
    Nakaji, P., and R. F. Spetzler. Innovations in surgical approach: the marriage of technique, technology, and judgment. Clin. Neur. 51:177–185, 2004.Google Scholar
  36. 36.
    Rohlfing, T., C. R. Maurer, D. A. Bluemke, and M. A. Jacobs. Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Trans. Med. Imaging 22:730–741, 2003.PubMedCrossRefGoogle Scholar
  37. 37.
    Rueckert, D., L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18:712–721, 1999.PubMedCrossRefGoogle Scholar
  38. 38.
    Ruprecht, D., and H. Muller. Free form deformation with scattered data interpolation methods. In: Geometric Modelling, edited by G. Farin, H. Hagen, H. Noltemeier, and W. Knodel. Berlin/Heidelberg: Springer-Verlag, 1993, pp. 267–281.CrossRefGoogle Scholar
  39. 39.
    Schnabel, J., D. Rueckert, M. Quist, J. Blackall, A. Castellano-Smith, T. Hartkens, G. Penney, W. Hall, H. Liu, C. Truwit, F. Gerritsen, D. G. Hill, and D. Hawkes. A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. In: Medical Image Computing and Computer-Assisted Intervention, edited by W. Niessen, and M. Viergever. Berlin/Heidelberg: Springer, 2001, pp. 573–581.Google Scholar
  40. 40.
    Sinkus, R., M. Tanter, T. Xydeas, S. Catheline, J. Bercoff, and M. Fink. Viscoelastic shear properties of in vivo breast lesions measured by MR elastography. Magn. Reson. Imaging 23:159–165, 2005.PubMedCrossRefGoogle Scholar
  41. 41.
    Thevenaz, P. and M. A. Unser. Spline pyramids for intermodal image registration using mutual information. In: Proceedings of SPIE: Wavelet Applications in Signal and Image Processing V. pp. 236–247, 1997.Google Scholar
  42. 42.
    Tokuda, J., G. S. Fischer, X. Papademetris, Z. Yaniv, L. Ibanez, P. Cheng, H. Liu, J. Blevins, J. Arata, A. J. Golby, T. Kapur, S. Pieper, E. C. Burdette, G. Fichtinger, C. M. Tempany, and N. Hata. OpenIGTLink: an open network protocol for image-guided therapy environment. Int. J. Med. Robotics Comput. Assist. Surg. 5:423–434, 2009.CrossRefGoogle Scholar
  43. 43.
    Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29:1310–1320, 2010.PubMedCrossRefGoogle Scholar
  44. 44.
    Ungi, T., P. Abolmaesumi, R. Jalal, M. Welch, I. Ayukawa, S. Nagpal, A. Lasso, M. Jaeger, D. P. Borschneck, G. Fichtinger, and P. Mousavi. Spinal needle navigation by tracked ultrasound snapshots. IEEE Trans. Biomed. Eng. 59:2766–2772, 2012.PubMedCrossRefGoogle Scholar
  45. 45.
    Ungi, T., D. Sargent, E. Moult, A. Lasso, C. Pinter, R. C. McGraw, and G. Fichtinger. Perk Tutor: an open-source training platform for ultrasound-guided needle insertions. IEEE Trans. Biomed. Eng. 59:3475–3481, 2012.PubMedCrossRefGoogle Scholar
  46. 46.
    Warfield, S. K., S. J. Haker, I. F. Talos, C. A. Kemper, N. Weisenfeld, A. U. J. Mewes, D. Goldberg-Zimring, K. H. Zou, C. F. Westin, W. M. Wells, C. M. C. Tempany, A. Golby, P. M. Black, F. A. Jolesz, and R. Kikinis. Capturing intraoperative deformations: research experience at Brigham and Women’s Hospital. Med. Image Anal. 9:145–162, 2005.PubMedCrossRefGoogle Scholar
  47. 47.
    Wittek, A., T. Hawkins, and K. Miller. On the unimportance of constitutive models in computing brain deformation for image-guided surgery. Biomech. Model. Mechanobiol. 8:77–84, 2009.PubMedCrossRefGoogle Scholar
  48. 48.
    Wittek, A., G. Joldes, M. Couton, S. K. Warfield, and K. Miller. 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.PubMedCrossRefGoogle Scholar
  49. 49.
    Wittek, A., K. Miller, R. Kikinis, and S. K. Warfield. Patient-specific model of brain deformation: application to medical image registration. J. Biomech. 40:919–929, 2007.PubMedCrossRefGoogle Scholar
  50. 50.
    Wittek, A., and K. Omori. Parametric study of effects of brain-skull boundary conditions and brain material properties on responses of simplified finite element brain model under angular acceleration impulse in sagittal plane. JSME Int. J. 46:1388–1399, 2003.CrossRefGoogle Scholar
  51. 51.
    Zhang, J. Y., G. R. Joldes, A. Wittek, and K. Miller. Patient-specific computational biomechanics of the brain without segmentation and meshing. Int. J. Numer. Methods Biomed. Eng. 29:293–308, 2013.CrossRefGoogle Scholar
  52. 52.
    Zhao, C. J., W. K. Shi, and Y. Deng. A new Hausdorff distance for image matching. Pattern Recogn. Lett. 26:581–586, 2005.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2013

Authors and Affiliations

  • Ahmed Mostayed
    • 1
  • Revanth Reddy Garlapati
    • 1
  • Grand Roman Joldes
    • 1
  • Adam Wittek
    • 1
  • Aditi Roy
    • 1
  • Ron Kikinis
    • 2
  • Simon K. Warfield
    • 3
  • Karol Miller
    • 1
    • 4
  1. 1.Intelligent Systems for Medicine LaboratoryThe University of Western AustraliaPerthAustralia
  2. 2.Surgical Planning Laboratory, Brigham & Women’s HospitalHarvard Medical SchoolBostonUSA
  3. 3.Computational Radiology Laboratory, Children’s HospitalHarvard Medical SchoolBostonUSA
  4. 4.Institute of Mechanics and Advanced Materials, Cardiff School of EngineeringCardiff UniversityWalesUK

Personalised recommendations