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Doppler Ultrasound Driven Biomechanical Model of the Brain for Intraoperative Brain-Shift Compensation: A Proof of Concept in Clinical Conditions

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Soft Tissue Biomechanical Modeling for Computer Assisted Surgery

Part of the book series: Studies in Mechanobiology, Tissue Engineering and Biomaterials ((SMTEB,volume 11))

Abstract

Accurate localization of the target is essential to reduce morbidity during brain tumor removal interventions. Yet, image-guided neurosurgery faces an important issue for large skull openings where brain soft-tissues can exhibit large deformations in the course of surgery. As a consequence of this “brain-shift” the pre-operatively acquired images no longer correspond to reality and subsequent neuronavigation is therefore strongly compromised. In this chapter we present a neuronavigator which addresses this issue and offers passive help to the surgeon by displaying the position of the guided tools with respect to the corrected location of the anatomical features. This low-cost system relies on localized 2D Doppler ultrasound imaging of the brain which makes it possible to track the vascular tree deformation throughout the intervention. An elastic registration procedure is used to match the shifted tree with its pre-operative structure identified within Magnetic Resonance Angiography images. A patient specific Finite Element biomechanical model of the brain further extends the resulting sparse deformation field to the overall organ volume. Finally, the estimated global deformation is applied to all pre-operatively available volumetric images or data, such as tumor contours, and the corrected planning is displayed to the surgeon. The system, tested on a patient presenting a large meningioma, was able to compensate within seconds for the intraoperatively observed brain-shift, reducing the mean error on tumor margin localization from 3.5 mm (max = 7.6 mm, RMS = 3.7 mm) to 0.9 mm (max = 1.7 mm, RMS = 1.0 mm).

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Notes

  1. 1.

    ISIS, Saint Martin d’Héres, France.

  2. 2.

    http://www.slicer.org

  3. 3.

    Northern Digital Inc., Canada.

  4. 4.

    Praxim, La Tronche, France.

  5. 5.

    The Imaging Source Europe GmbH, Germany.

  6. 6.

    http://www.opengl.org

References

  1. Nimsky, C., Ganslandt, O., Cerny, S., Hastreiter, P., Greiner, G., Fahlbusch, R.: Quantification of visualization of and compensation for brain shift using intraoperative magnetic resonance imaging. Neurosurgery 47(5), 1070–1079 (2000)

    Article  Google Scholar 

  2. Nabavi, A., Black, P., Gering, D., Westin, C., Mehta, V., R.P, J.r., Ferrant, M., Warfield, S., Hata, N., Schwartz, R., Wells, W., Kikinis, R., Jolesz, F.: Serial intraoperative magnetic resonance imaging of brain shift. Neurosurgery 48(4), 787–797 (2001)

    Google Scholar 

  3. Reinges, M., Nguyen, H., Krings, T., Hutter, B., Rohde, V., Gilsbach, J.: Course of brain shift during microsurgical resection of supratentorial cerebral lesions: limits of conventional neuronavigation. Acta Neurochir. (Wien) 146(4), 369–377 (2004)

    Article  Google Scholar 

  4. Roberts, D., Hartov, A., Kennedy, F., Miga, M., Paulsen, K.: Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43(4), 749–758 (1998)

    Article  Google Scholar 

  5. Bucholz, R., Yeh, D., Trobaugh, J., McDurmont, L., Sturm, C., Baumann, C., Hen- derson, J., Levy, A., Kessman, P.: The correction of stereotactic inaccuracy caused by brain shift using an intraoperative ultrasound device. In: Troccaz, J., Grimson, E., Msges, R. (eds.) CVRMed-MRCAS’97: First Joint Conference on Computer Vision, Virtual Reality, and Robotics in Medicine and Medical Robotics and Computer Assisted Surgery, Grenoble, France, 19–22 March 1997, pp. 459–466. Springer, Berlin (1997)

    Google Scholar 

  6. Trantakis, C., Tittgemeyer, M., Schneider, J., Lindner, D., Winkler, D., Strauss, G., Meixensberger, J.: Investigation of time-dependency of intracranial brain shift and its relation to the extent of tumor removal using intra-operative MRI. Neurol. Res. 25(1), 9–12 (2003)

    Article  Google Scholar 

  7. Ferrant, M., Nabavi, A., Macq, B., Jolesz, F.A., Kikinis, R., Warfield, S.K.: Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model. IEEE Trans. Med. Imaging 20(12), 1384–1397 (2001)

    Article  Google Scholar 

  8. Ferrant, M., Nabavi, A., Macq, B., Black, P., Jolesz, F., Kikinis, R., Warfield, S.: Serial registration of intraoperative MR images of the brain. Med. Image Anal. 6(4), 337–359 (2002)

    Article  Google Scholar 

  9. Chu, R.M., Tummala, R.P., Hall, W.A.: Intraoperative magnetic resonance imaging-guided neurosurgery. Neurosurg. Q. 13(4), 234–250 (2003)

    Article  Google Scholar 

  10. Soza, G., Hastreiter, P., Vega, F., Rezk-Salama, C., Bauer, M., Nimsky, C., Greiner, G.: Non-linear intraoperative correction of brain shift with 1.5T data. In: Handels, H., Horsch, A., Lehmann, T., Meinzer, H.P. (eds.) Bildverarbeitung für die Medizin, pp. 21–25. Springer, Heidelberg (2003)

    Google Scholar 

  11. Clatz, O., Delingette, H., Talos, I., Golby, A., Kikinis, R., Jolesz, F., Ayache, N., Warfield, S.: Robust non-rigid registration to capture brain shift from intraoperative MRI. IEEE Trans. Med. Imaging 24(11), 1417–1427 (2005)

    Article  Google Scholar 

  12. Hu, J., Jin, X., Lee, J., Zhang, L., Chaudhary, V., Guthikonda, M., Yang, K., King, A.: Intraoperative brain shift prediction using a 3D inhomogeneous patient specific finite element model. J. Neurosurg. 106, 164–169 (2007)

    Article  Google Scholar 

  13. Wirtz, C., Tronnier, V., Bonsanto, M., Knauth, M., Staubert, A., Albert, F.: Image-guided neurosurgery with intraoperative MRI: update of frameless sterotaxy and radicality control. Stereotact. Funct. Neurosurg. 68, 39–43 (1997)

    Article  Google Scholar 

  14. Steinmeier, R., Fahlbusch, R., Ganslandt, O., Nimsky, C., Buchfelder, M., Kaus, M., Heigl, T., Lenz, G., Kuth, R., Huk, W.: Intraoperative magnetic resonance imaging with the magnetom open scanner: concepts, neurosurgical indications and procedures—a preliminary report. Neurosurgery 43, 739–748 (1998)

    Article  Google Scholar 

  15. Hata, N., Nabavi, A., Wells, W. III, Warfield, S., Kikinis, R., Black, P., Jolesz, F.: Three-dimensional optical ow method for measurement of volumetric brain deformation from intraoperative MR images. J. Comput. Assist. Tomogr. 24(4), 531–538 (2000)

    Article  Google Scholar 

  16. Hastreiter, P., Rezk-Salama, C., Nimsky, C., L”urig, C., Greiner, G., Ertl, T.: Registration techniques for the analysis of the brain shift in neurosurgery. Comput. Graph. 24(3), 385–389 (2000)

    Article  Google Scholar 

  17. Shattuck, D., Leahy, R.: BrainSuite: an automated cortical surface identification tool. Med. Image Anal. 6(2), 129–142 (2002)

    Article  Google Scholar 

  18. Kyriacou, S., Davatzikos, C., Zinreich, S., Bryan, R.: Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model. IEEE Trans. Med. Imaging 18(7), 580–592 (1999)

    Article  Google Scholar 

  19. Hagemann, A., Rohr, K., Stiel, H., Spetzger, U.: Biomechanical modeling of the human head for physically based, non-rigid image registration. IEEE Trans. Med. Imaging 18(10), 875–884 (1999)

    Article  Google Scholar 

  20. Dumpuri, P., Thompson, R., Dawant, B., Cao, A., Miga, M.: An atlas-based method to compensate for brain shift: preliminary results. Med. Image Anal. 11(2), 128–145 (2007)

    Article  Google Scholar 

  21. Audette, M., Siddiqi, K., Ferrie, F., Peters, T.: An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery. Comput. Vis. Image Understand. 89(2–3), 226–251 (2003)

    Article  Google Scholar 

  22. Miga, M., Sinha, T., Cash, D., Galloway, R., Weil, R.: Cortical surface registration for image-guided neurosurgery using laser-range scanning. IEEE Trans. Med. Imaging 22(8), 973–985 (2003)

    Article  Google Scholar 

  23. Comeau, R., Sadikot, A., Fenster, A., Peters, T.: Intraoperative ultrasound for guidance and tissue shift correction in image-guided neurosurgery. Med. Phys. 27, 787–800 (2000)

    Article  Google Scholar 

  24. Reinertsen, I., Lindseth, F., Unsqaard, G., Collins, D.: Clinical validation of vessel-based registration for correction of brain-shift. Med. Image Anal. 11(6), 673–684 (2007)

    Article  Google Scholar 

  25. Zagzag, D., Brem, S., Robert, F.: Neovascularization and tumor growth in the rabbit brain. Am. J. Pathol. 131(2), 361–372 (1988)

    Google Scholar 

  26. Hjelmeland, A., Lathia, J., Sathornsumetee, S., Rich, J.: Twisted tango: brain tumor neurovascular interactions. Nat. Neurosci. 14(11), 1375–1381 (2011)

    Article  Google Scholar 

  27. Clatz, O., Sermesant, M., Bondiau, P., Delingette, H., Warfield, S., Malandain, G., Ayache, N.: Realistic simulation of the 3-D growth of brain tumors in mr images coupling diffusion with biomechanical deformation. IEEE Trans. Med. Imaging 24(10), 1334–1346 (2005)

    Article  Google Scholar 

  28. Bucki, M., Lobos, C., Payan, Y.: A fast and robust patient specific finite element mesh registration technique: application to 60 clinical cases. Med. Image Anal. 14, 303–317 (2010). doi:10.1016/j.media.2010.02.003

    Google Scholar 

  29. Yano, T., Kodama, T., Suzuki, Y., Watanabe, K.: Gadolinium-enhanced 3D time-of-fight MR angiography: experimental and clinical evaluation. Acta Radiol. 38(1), 47–54 (1997)

    Google Scholar 

  30. Reinertsen, I., Descotaux, M., Drouin, S., Siddiqi, K., Collins, D.: Vessel driven correction of brain shift. In: Proceedings of Medical Image Computing and Computer Assisted Intervention—MICCAI 2004, pp. 208–216 (2004)

    Google Scholar 

  31. Belytschko, T., Liu, W.K., Moran, B.: Nonlinear Finite Elements for Continua and Structures. Wiley, Chichester (2006)

    Google Scholar 

  32. Metz, H., McElhaney, J., Ommaya, A.K: A comparison of the elasticity of live, dead, and fixed brain tissue. J. Biomech. 3(4), 453–458 (1970)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  35. Eckabert, O., Butz, T., Nabavi, A., Thirian, J.: Brain shift correction based on a boundary element biomechanical model with different material properties. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention—MICCAI 2003, pp. 41–49 (2003)

    Google Scholar 

  36. Soza, G., Grosso, R., Nimsky, C., Greiner, G., Hastreiter, P.: Estimating mechanical brain tissue properties with simulation and registration. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention—MICCAI 2004, vol. 3217, pp. 276–283 (2004)

    Google Scholar 

  37. Schiavone, P., Chassat, F., Boudou, T., Promayon, E., Valdivia, F., Payan, Y.: In vivo measurement of human brain elasticity using a light aspiration device. Med. Image Anal. 13, 673–678 (2009)

    Article  Google Scholar 

  38. Clatz, O., Bondiau, P., Delingette, H., Sermesant, M., Warfield, S., Malandain, G., Ayache, N.: Brain tumor growth simulation. Research Report, No. 5187, INRIA (2004)

    Google Scholar 

  39. Wittek, A., Miller, K., Kikinis, R., Warfield, S.K: Patient-specific model of brain deformation: application to medical image registration. J. Biomech. 40(4), 919–929 (2007)

    Article  Google Scholar 

  40. Cotin, S., Delingette, H., Ayache, N.: Real-time elastic deformations of soft tissues for surgery simulation. IEEE Trans. Visual. Comput. Graph. 5(1), 62–73 (1999)

    Article  Google Scholar 

  41. Arun, K.S., Huang, T.S., Blostein, S.D: Least-squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 698–700 (1987)

    Article  Google Scholar 

  42. Lee, B., Gobbi, D.G., Peters, T.M.: Vascular tree extraction from mra and power doppler us image volumes. In: Proceedings of the 22nd Annual EMBS International Conference (2000)

    Google Scholar 

  43. Reinertsen, I., Descoteaux, M., Siddiqi, K., Collins, D.: Validation of vessel-based registration for correction of brain shift. Med. Image Anal. 11(4), 374–388 (2007)

    Article  Google Scholar 

  44. Yasuda, K., Nakajima, S., Wakayama, A., Oshino, S., Kubo, S., Yoshimine, T.: Intraoperative three-dimensional reconstruction of power doppler vascular images. Minim. Invasive Neurosurg. 46(6), 323–326 (2003)

    Article  Google Scholar 

  45. Lango, T.: Ultrasound guided surgery: image processing and navigation. Thesis, Norwegian University of Science and Technology (2000)

    Google Scholar 

  46. Hughes, T.: The finite element method: Linear static and dynamic finite element analysis. Dover Publications, San Raphael (1987)

    MATH  Google Scholar 

  47. Kardestuncer, H.: Finite Element Handbook. McGraw-Hill, New York (1987). ISBN 0-07-033305-X

    Google Scholar 

  48. Bro-Nielsen, M.: Finite element modeling in surgery simulation. Proc. IEEE 86(3), 490–503 (1998)

    Article  Google Scholar 

  49. Hsu, W.M., Hughes, J.F., Kaufman, H.: Direct manipulation of free-form deformations. In: Proceedings of SIGGRAPH’92, pp.17–184. ACM, New York (1992)

    Google Scholar 

  50. Nesme, M., Faure, F., Payan, Y.: Accurate interactive animation of deformable models at arbitrary resolution. Int. J. Image Graph. 10, 175 (2010)

    Google Scholar 

  51. Shewchuk, J.: An introduction to the conjugate gradient method without the agonizing pain. Technical Report CMUCS-TR-94-125, Carnegie Mellon University (1994)

    Google Scholar 

  52. Vigneron, L.: FEM/XFEM-based modeling of brain shift, resection, and retraction for image-guided surgery. Ph.D. Thesis, Université de Liége (2009)

    Google Scholar 

  53. Jerabovka, L., Bousquet, G., Barbier, S., Faure, F., Allard, J.: Volumetric modeling and interactive cutting of deformable bodies. Prog. Biophys. Mol. Biol. 103, 217–224 (2010)

    Article  Google Scholar 

  54. 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, 159–168 (2010)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  56. Bucki, M.: Modélisation Biomécanique des Tissus Mous du Cerveau et Développement d’un Neuronavigateur Permettant la Prise en Compte Per-Opératoire du brain-shift. PhD Thesis, Université Joseph-Fourier—Grenoble I, France (2008)

    Google Scholar 

  57. Bucki, M., Montagne, A., Palombi, O.: Image-based soft-tissues resection tracking for intraoperative brain-shift compensation: general framework and preliminary study. In: Proceedings of Computer Assisted Radiology and Surgery, CARS’2010, Geneva, Switzerland, 23–26 June 2010

    Google Scholar 

  58. Lorensen, W., Cline, H.: Marching cubes: a high resolution 3-D surface construction algorithm. Proc. Comput. Graph. (SIGGRAPH’87) 21, 163–169 (1987)

    Google Scholar 

  59. Besl, P.J., McKay, N.D: A method for registration of 3D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–254 (1992)

    Article  Google Scholar 

  60. Wilms, G., Bosmans, H., Demaerel, P., Marchal, G.: Magnetic resonance angiography of the intracranial vessels. Eur. J. Radiol. 38(1), 10–18 (2001)

    Article  Google Scholar 

  61. Jung, H., Chang, K., Choi, D., Han, M., Han, M.: Contrast-enhanced MR angiography for the diagnosis of intracranial vascular disease: optimal dose of gadopentetate dimeglumine. Am. J. Roentgenol. 165(5), 1251–1255 (1995)

    Google Scholar 

  62. Özsarlak, H., Van Goethem, J., Parizel, P.: 3D time-of-flight MR angiography of the intracranial vessels: optimization of the technique with water excitation, parallel acquisition, eight-channel phased-array head coil and low-dose contrast administration. Eur. Radiol. 14(11), 2067–2071 (2004)

    Google Scholar 

  63. Descoteaux, M., Collins, L.D., Siddiqi, K.: A multi-scale geometric flow for segmenting vasculature in MRI: theory and validation. Med. Image Anal. 12(4), 497–513 (2008)

    Article  Google Scholar 

  64. Ji, S., Hartov, A., Fontaine, K., Borsic, A., Roberts, D., Paulsen, K.: Coregistered volumetric true 3D ultrasonography in image-guided neurosurgery. In: Proceedings of SPIE, vol. 6918. SPIE, Bellingham (2008)

    Google Scholar 

  65. Lorenzo, M.J.V., Agel, F.M., Belando, R.A: Tridimensional (3D) endoscopic ultrasonography. Rev. Esp. Enferm. Dig. 99(1), 39–45 (2007)

    Google Scholar 

  66. Bockermann, V.: Neuronavigation and endosonography for intracranial monitoring. J. Comput. Assist. Radiol. Surg. (Suppl. 1 on Comput. Assist. Neurosurg.) 3, 72–78 (2008)

    Google Scholar 

  67. Saftoiu, A., Gheonea, D.: Tridimensional (3D) endoscopic ultrasound a pictorial review. J. Gastrointestin. Liver Dis. 18(4), 501–505 (2009)

    Google Scholar 

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Acknowledgments

The authors wish to thank Dr. Pierre Lavagne and Dr. Gilles Francony from the surgical reanimation unit (URC) at the Hospital Michallon for their advice and help during this study.

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Authors and Affiliations

  1. TIMC-IMAG Laboratory, UMR CNRS 5525, University Joseph Fourier, 38706, La Tronche, France

    Marek Bucki, Mathieu Bailet & Yohan Payan

  2. Service de Neurochirurgie, Hôpital Albert Michallon, Centre Hospitalier et Universitaire de Grenoble, BP 217, 38043, Grenoble cedex, France

    Olivier Palombi

  3. PIMS-Europe, UMI CNRS 3069, University of British Columbia, 200-1933 West Mall, Vancouver, BC, V6T 1Z2, Canada

    Yohan Payan

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  1. Marek Bucki
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Correspondence to Marek Bucki .

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  1. , TIMC Laboratory, Joseph Fourier University, La Tronche cedex, 38706, France

    Yohan Payan

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Bucki, M., Palombi, O., Bailet, M., Payan, Y. (2012). Doppler Ultrasound Driven Biomechanical Model of the Brain for Intraoperative Brain-Shift Compensation: A Proof of Concept in Clinical Conditions. In: Payan, Y. (eds) Soft Tissue Biomechanical Modeling for Computer Assisted Surgery. Studies in Mechanobiology, Tissue Engineering and Biomaterials, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8415_2012_119

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  • DOI: https://doi.org/10.1007/8415_2012_119

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