Skip to main content

Capturing Brain Deformation

  • Conference paper
  • First Online:
Surgery Simulation and Soft Tissue Modeling (IS4TM 2003)

Abstract

A critical challenge for the neurosurgeon during surgery is to be able to preserve healthy tissue and minimize the disruption of critical anatomical structures while at the same time removing as much tumor tissue as possible. Over the past several years we have developed intraoperative image processing algorithms with the goal of augmenting the surgeon’s capacity to achieve maximal tumor resection while minimizing the disruption to normal tissue. The brain of the patient often changes shape in a nonrigid fashion over the course of a surgery, due to loss of cerebrospinal fluid, concomitant pressure changes, the impact of anaesthetics and the surgical resection itself. This further increases the challenge of visualizing and navigating critical brain structures. The primary concept of our approach is to exploit intraoperative image acquisition to directly visualize the morphology of brain as it changes over the course of the surgery, and to enhance the surgeon’s capacity to visualize critical structures by projecting extensive preoperative data into the intraoperative configuration of the patient’s brain.

Our approach to tracking brain changes during neurosurgery has been previously described. We identify key structures in volumetric preoperative and intraoperative scans, and use the constraints provided by the matching of these key surfaces to compute a biomechanical simulation of the volumetric brain deformation. The recovered volumetric deformation field can then be applied to preoperative data sets, such as functional MRI (fMRI) or diffusion tensor MRI (DT-MRI) in order to warp this data into the new configuration of the patient’s brain. In recent work we have constructed visualizations of preoperative fMRI and DT-MRI, and intraoperative MRI showing a close correspondence between the matched data. p ]A further challenge of intraoperative image processing is that augmented visualizations must be presented to the neurosurgeon at a rate compatible with surgical decision making. We have previously demonstrated our biomechanical simulation of brain deformation can be executed entirely during neurosurgery. We used a generic atlas to provide surrogate information regarding the expected location of critical anatomical structures, and were able to project this data to match the patient and to display the matched data to the neurosurgeon during the surgical procedure. The use of patient-specific DTI and fMRI preoperative data significantly improves the localization of critical structures. The augmented visualization of intraoperative data with relevant preoperative data can significantly enhance the information available to the neurosurgeon.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. F. Jolesz, “Image-guided Procedures and the Operating Room of the Future,” Radiology, vol. 204, pp. 601–612, May 1997.

    Google Scholar 

  2. A. Nabavi, P. M. Black, D. T. Gering, C. F. Westin, V. Mehta, R. S. Pergolizzi, M. Ferrant, S. K. Warfield, N. Hata, R. B. Schwartz, W. M. Wells III, R. Kikinis, and F. A. Jolesz, “Serial Intraoperative MR Imaging of Brain Shift,” Neurosurgery, vol. 48, pp. 787–798, Apr 2001.

    Article  Google Scholar 

  3. A. Hagemann, K. Rohr, H. S. Stiel, U. Spetzger, and J. M. Gilsbach, “Biomechanical modeling of the human head for physically based, non-rigid image registration,” IEEE Transactions On Medical Imaging, vol. 18, No. 10, pp. 875–884, 1999.

    Article  Google Scholar 

  4. O. Skrinjar and J. S. Duncan, “Real time 3D brain shift compensation,” in IPMI’99, pp. 641–649, 1999.

    Google Scholar 

  5. M. Miga, K. Paulsen, J. Lemery, A. Hartov, and D. Roberts, “In vivo quantification of a homogeneous brain deformation model for updating preoperative images during surgery,” IEEE Transactions On Medical Imaging, vol. 47, pp. 266–273, February 1999.

    Google Scholar 

  6. O. Skrinjar, C. Studholme, A. Nabavi, and J. Duncan, “Steps Toward a Stereo-Camera-Guided Biomechanical Model for Brain Shift Compensation,” in Proceedings of International Conference of Information Processing in Medical Imaging, pp. 183–189, 2001.

    Google Scholar 

  7. M. Ferrant, S. K. Warfield, A. Nabavi, B. Macq, and R. Kikinis, “Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model,” in MICCAI 2000: Third International Conference on Medical Robotics, Imaging And Computer Assisted Surgery; 2000 Oct 11–14; Pittsburgh, USA (A. M. DiGioia and S. Delp, eds.), (Heidelberg, Germany), pp. 19–28, Springer-Verlag, 2000.

    Google Scholar 

  8. D. Hill, C. Maurer, R. Maciunas, J. Barwise, J. Fitzpatrick, and M. Wang, “Measurement of intraoperative brain surface deformation under a craniotomy,” Neurosurgery, vol. 43, pp. 514–526, 1998.

    Article  Google Scholar 

  9. N. Hata, Rigid and deformable medical image registration for image-guided surgery. PhD thesis, University of Tokyo, 1998.

    Google Scholar 

  10. M. Ferrant, S. K. Warfield, C. R. G. Guttmann, R. V. Mulkern, F. A. Jolesz, and R. Kikinis, “3D Image Matching Using a Finite Element Based Elastic Deformation Model,” in MICCAI 99: Second International Conference on Medical Image Computing and Computer-Assisted Intervention; 1999 Sep 19–22; Cambridge, England (C. Taylor and A. Colchester, eds.), (Heidelberg, Germany), pp. 202–209, Springer-Verlag, 1999.

    Google Scholar 

  11. N. Hata, A. Nabavi, W. M. Wells, S. K. Warfield, R. Kikinis, P. M. Black, and F. A. Jolesz, “ Three-Dimensional Optical Flow Method for Measurement of Volumetric Brain Deformation from Intraoperative MR Images, ” J Comput Assist Tomogr, vol. 24, pp. 531–538, Jul 2000.

    Article  Google Scholar 

  12. K. Paulsen, M. Miga, F. Kennedy, P. Hoopes, A. Hartov, and D. Roberts, “A Computational Model for Tracking Subsurface Tissue Deformation During Stereotactic Neurosurgery,” IEEE Transactions On Medical Imaging, vol. 47, pp. 213–225, February 1999.

    Google Scholar 

  13. M. I. Miga, D. W. Roberts, F. E. Kennedy, L. A. Platenik, A. Hartov, K. E. Lunn, and K. D. Paulsen, “ Modeling of Retraction and Resection for Intraoperative Updating of Images,” Neurosurgery, vol. 49, pp. 75–85, July 2001.

    Article  Google Scholar 

  14. M. Ferrant, A. Nabavi, B. Macq, F. A. Jolesz, R. Kikinis, and S. K. Warfield, “Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model,” IEEE Trans Med Imag, vol. 20, pp. 1384–1397, Dec 2001.

    Article  Google Scholar 

  15. M. Ferrant, A. Nabavi, B. Macq, P. M. Black, F. A. Jolesz, R. Kikinis, and S. K. Warfield, “Serial Registration of Intraoperative MR Images of the Brain,” Med Image Anal, vol. 6, No. 4, pp. 337–359, 2002.

    Article  Google Scholar 

  16. S. K. Warfield, F. Talos, A. Tei, A. Bharatha, A. Nabavi, M. Ferrant, P. M. Black, F. A. Jolesz, and R. Kikinis, “Real-Time Registration of Volumetric Brain MRI by Biomechanical Simulation of Deformation during Image Guided Neurosurgery,” Comput Visual Sci, vol. 5, pp. 3–11, 2002.

    Article  MATH  Google Scholar 

  17. D. Gering, A. Nabavi, R. Kikinis, W. Grimson, N. Hata, P. Everett, F. Jolesz, and W. Wells, “An Integrated Visualization System for Surgical Planning and Guidance using Image Fusion and Interventional Imaging, ” in MICCAI 99: Proceedings of the Second International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 809–819, Springer Verlag, 1999.

    Google Scholar 

  18. R. Kikinis, M. E. Shenton, G. Gerig, J. Martin, M. Anderson, D. Metcalf, C. R. G. Guttmann, R. W. McCarley, W. E. Lorenson, H. Cline, and F. Jolesz, “Routine Quantitative Analysis of Brain and Cerebrospinal Fluid Spaces with MR Imaging,” Journal of Magnetic Resonance Imaging, vol. 2, pp. 619–629, 1992.

    Article  Google Scholar 

  19. A. Yezzi, A. Tsai, and A. Willsky, “Medical image segmentation via coupled curve evolution equations with global constraints,” in Mathematical Methods in Biomedical Image Analysis, (New York), pp. 12–19, IEEE, 2000.

    Google Scholar 

  20. S. K. Warfield, M. Kaus, F. A. Jolesz, and R. Kikinis, “Adaptive, Template Moderated, Spatially Varying Statistical Classification,” Med Image Anal, vol. 4, pp. 43–55, Mar 2000.

    Article  Google Scholar 

  21. M. R. Kaus, S. K. Warfield, A. Nabavi, E. Chatzidakis, P. M. Black, F. A. Jolesz, and R. Kikinis, “ Segmentation of MRI of meningiomas and low grade gliomas,” inMICCAI 99: Second International Conference on Medical Image Computing and Computer-Assisted Intervention; 1999 Sep 19–22; Cambridge, England (C. Taylor and A. Colchester, eds.), (Heidelberg, Germany), pp. 1–10, Springer-Verlag, 1999.

    Google Scholar 

  22. S. K. Warfield, R. V. Mulkern, C. S. Winalski, F. A. Jolesz, and R. Kikinis, “An Image Processing Strategy for the Quantification and Visualization of Exercise Induced Muscle MRI Signal Enhancement,” J Magn Reson Imaging, vol. 11, pp. 525–531, May 2000.

    Article  Google Scholar 

  23. C. F. Westin, S. E. Maier, H. Mamata, A. Nabavi, F. A. Jolesz, and R. Kikinis, “Processing and visualization for diffusion tensor MRI,” Med Image Anal, vol. 6, No. 2, pp. 93–108, 2002. 1361-8415 Journal Article.

    Article  Google Scholar 

  24. L. O’Donnell, S. Haker, and C.-F. Westin, “New Approaches to Estimation of White Matter Connectivity in Diffusion Tensor MRI: Elliptic PDEs and Geodesics in a Tensor-Warped Space,” in MICCAI 2002: Fifth International Conference on Medical Image Computing and Computer Assisted Intervention, (Heidelberg, Germany), pp. 459–466, Springer-Verlag, 2002.

    Google Scholar 

  25. A. Tsai, J. Fisher, C. Wible, W. M. Wells, J. Kim, and A. S. Willsky, “Analysis of functional mri data using mutual information,” in MICCAI 1999: Second International Conference on Medical Image Computing and Computer Assisted Intervention, (Heidelberg, Germany), pp. 473–480, Springer-Verlag, 1999.

    Google Scholar 

  26. J. Fisher, E. Cosman, C. Wible, and W. Wells, “Adaptive entropy rates for fmri time-series analysis, ” in MICCAI 2001: Fourth International Conference on Medical Image Computing and Computer Assisted Intervention, (Utrecht, the Netherlands), pp. 905–912, Springer-Verlag, 2001.

    Google Scholar 

  27. D. Gering, A. Nabavi, R. Kikinis, N. Hata, L. O’Donnell, W. Grimson, F. Jolesz, P. Black, and W. Wells III, “An integrated visualization system for surgical planning and guidance using image fusion and an open MR,” J Magn Reson Imaging, vol. 13, pp. 967–975, Jun 2001.

    Article  Google Scholar 

  28. S. K. Warfield, F. A. Jolesz, and R. Kikinis, “Real-Time Image Segmentation for Image-Guided Surgery, ” in SC 1998: High Performance Networking and Computing Conference; 1998 Nov 7–13; Orlando, USA, No. 1114, (New York), pp. 1–14, IEEE, 1998.

    Google Scholar 

  29. S. K. Warfield, K. H. Zou, and W. M. Wells, “Validation of Image Segmentation and Expert Quality with an Expectation-Maximization Algorithm,” inMICCAI 2002: Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention; 2002 Sep 25–28; Tokyo, Japan, (Heidelberg, Germany), pp. 298–306, Springer-Verlag, 2002.

    Google Scholar 

  30. W. Schroeder, K. Martin, and B. Lorensen, The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics. Prentice Hall PTR, New Jersey, 1996.

    Google Scholar 

  31. B. Geiger, “Three dimensional modeling of human organs and its application to diagnosis and surgical planning,” Tech. Rep. 2105, INRIA, 1993.

    Google Scholar 

  32. M. Ferrant, A. Nabavi, B. Macq, and S. K. Warfield, “Deformable Modeling for Characterizing Biomedical Shape Changes,” inDGCI2000: Discrete Geometry for Computer Imagery; 2000 Dec 13–15; Uppsala, Sweden (G. Borgefors, I. Nyström, and G. Sanniti di Baja, eds.), vol. 1953 of Lecture Notes in Computer Science, (Heidelberg, Germany), pp. 235–248, Springer, 2000.

    Chapter  Google Scholar 

  33. S. K. Warfield, F. Jolesz, and R. Kikinis, “A High Performance Computing Approach to the Registration of Medical Imaging Data,” Parallel Computing, vol. 24, pp. 1345–1368, Sep 1998.

    Article  Google Scholar 

  34. M. Ferrant, O. Cuisenaire, and B. Macq, “Multi-Object Segmentation of Brain Structures in 3D MRI Using a Computerized Atlas,” in SPIE Medical Imaging’ 99, vol. 3661–2, pp. 986–995, 1999.

    Google Scholar 

  35. O. C. Zienkiewicz and R. L. Taylor, The Finite Element Method: Basic Formulation and Linear Problems. McGraw Hill Book Co., New York, 4th ed., 1994.

    MATH  Google Scholar 

  36. S. Balay, W. D. Gropp, L. C. McInnes, and B. F. Smith, “Efficient management of parallelism in object oriented numerical software libraries,” in Modern Software Tools in Scientific Computing (E. Arge, A. M. Bruaset, and H. P. Langtangen, eds.), pp. 163–202, Birkhauser Press, 1997.

    Google Scholar 

  37. S. Balay, W. D. Gropp, L. C. McInnes, and B. F. Smith, “PETSc 2.0 users manual,” Tech. Rep. ANL-95/11-Revision 2.0.28, Argonne National Laboratory, 2000.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Warfield, S.K. et al. (2003). Capturing Brain Deformation. In: Ayache, N., Delingette, H. (eds) Surgery Simulation and Soft Tissue Modeling. IS4TM 2003. Lecture Notes in Computer Science, vol 2673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45015-7_20

Download citation

  • DOI: https://doi.org/10.1007/3-540-45015-7_20

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40439-2

  • Online ISBN: 978-3-540-45015-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics