Advertisement

An Evaluation of Adaptive Biomechanical Non-Rigid Registration for Brain Glioma Resection Using Image-Guided Neurosurgery

  • Fotis Drakopoulos
  • Chengjun Yao
  • Yixun Liu
  • Nikos ChrisochoidesEmail author
Conference paper
First Online:

Abstract

Interventional MRI (iMRI) has proven to be an effective tool for the visualization of brain deformation and for the optimization of the maximal safe volumetric tumor resection during Image-Guided Neurosurgery. Earlier we proposed two adaptive non-rigid registration methods between pre-operative and intra-operative MRI based on: (i) Nested Expectation Maximization (NEM) [8, 20] which implicitly compensates for tissue removal, and (ii) Geometric-based which explicitly adapts the mesh to compensate for the changes in the geometry of the brain [4, 21]. In this paper, we assess the accuracy of these methods and compare them with two widely used registration schemes: ITK’s rigid registration and ITK’s Physics-Based Non-Rigid Registration (PBNRR). The evaluation is based on registration error, for the brain deformation induced by cerebral glioma resection, and it utilizes three metrics for the error: (i) 100% Hausdorff Distance, (ii) error at specific anatomical points identified by a neurosurgeon, and (iii) visual inspection by a neurosurgeon. We conduct a retrospective study on ten patients with a malignant glioma. The evaluation shows that the geometric adaptive approach achieves the most accurate alignments compared to ITK’s PBNRR and the Nested Expectation Maximization. It significantly reduces the alignment error due to rigid registration commonly used by commercial neuronavigators, and completes a volumetric alignment, on average, in about 2.3 min on a 12-core Linux workstation, satisfying the time constraints imposed by neurosurgery.

Keywords

Hausdorff Distance Registration Accuracy Rigid Registration Soft Tissue Deformation Brain Deformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

Research reported in this publication was supported in part by the Modeling and Simulation Fellowship at Old Dominion University, CCF-1439079, John Simon Guggenheim Foundation, and by the Richard T. Cheng Endowment.

References

  1. 1.
    Louis David N, Hiroko O, Wiestler Otmar D, Cavenee Webster K, Burger Peter C, Anne J, Scheithauer Bernd W, Paul K (2007) The 2007 WHO classification of tumors of the central nervous system. J Acta Neuropathol 114(2):97–109CrossRefGoogle Scholar
  2. 2.
    Evren Keles G, Chang EF, Lamborn KR, Tihan T, Chang C-J, Chang SM, Berger MS (2006) Volumetric extent of resection and residual contrast enhancement on initial surgery as predictors of outcome in adult patients with hemispheric anaplastic astrocytoma. J Neurosurg 105(1):34–40CrossRefGoogle Scholar
  3. 3.
    Yixun L, Chengjun Y, Fotis D, Wu J, Liangfu Z, Nikos C (2014) A nonrigid registration method for correcting brain deformation induced by tumor resection. Med Phys 41(101710)Google Scholar
  4. 4.
    Drakopoulos F, Chrisochoides NP (2015) Accurate and fast deformable medical image registration for brain tumor resection using image-guided neurosurgery. Comput Methods Biomech Biomed Eng Imaging Visual 4(2):112–126Google Scholar
  5. 5.
    Archip N, Clatz O, Whalen S, Kacher D, Fedorov A, Kot A, Chrisochoides N, Jolesz F, Golby A, Black PM, Warfield SK (2007) Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery. NeuroImage 35(2):609–624CrossRefGoogle Scholar
  6. 6.
    Clatz O, Delingette H, Talos IF, Golby A, Kikinis R, Jolesz F, Ayache N, Warfield S (2005) Robust non-rigid registration to capture brain shift from intraoperative mri. IEEE Trans Med Imaging 24(11):1417–1427CrossRefGoogle Scholar
  7. 7.
    Michael MI (2015) Computational modeling for enhancing soft tissue image guided surgery: an application in neurosurgery. Ann Biomed Eng 44(1):1–11Google Scholar
  8. 8.
    Miga MI, Roberts DW, Kennedy FE, Platenik LA, Hartov A, Lunn KE et al (2001) Modeling of retraction and resection for intraoperative updating of images. Neurosurgery 49:75–84Google Scholar
  9. 9.
    Dorward NL, Olaf A, Velani B, Gerritsen FA, Harkness WFJ, Kitchen ND, Thomas DGT (1998) Postimaging brain distortion: magnitude, correlates, and impact on neuronavigation. J Neurosurg 88:656–662CrossRefGoogle Scholar
  10. 10.
    Mostayed A, Garlapati R, Joldes G, Wittek A, Roy A, Kikinis R, Warfield S, Miller K (2013) Biomechanical model as a registration tool for image-guided neurosurgery: evaluation against bspline registration. Ann Biomed Eng 41(11):2409–2425CrossRefGoogle Scholar
  11. 11.
    Wittek A, Miller K, Kikinis R, Warfield SK (2007) Patient-specific model of brain deformation: application to medical image registration. J Biomech 40(4):919–929CrossRefGoogle Scholar
  12. 12.
    Ferrant M, Nabavi A, Macq B, Black PM, Jolesz FA, Kikinis R, Warfield SK (2002) Serial registration of intraoperative MR images of the brain. Med Image Anal 6(4):337–359CrossRefGoogle Scholar
  13. 13.
    Ferrant M, Nabavi A, Macq B, Jolesz FA, Kikinis R, Warfield SK (2001) Registration of 3-d intraoperative MR images of the brain using a finite-element biomechanical model. IEEE Trans Med Imaging 20(12):1384–1397CrossRefGoogle Scholar
  14. 14.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155CrossRefGoogle Scholar
  15. 15.
    Antiga L, Piccinelli M, Botti L, Ene-Iordache B, Remuzzi A, Steinman D (2008) An image-based modeling framework for patient-specific computational hemodynamics. Med Biol Eng Comput 46(11):1097–1112CrossRefGoogle Scholar
  16. 16.
    Horton A, Wittek A, Joldes GR, Miller K (2010) A meshless Total Lagrangian explicit dynamics algorithm for surgical simulation. Int J Num Methods Biomed Eng 26(8):977–998CrossRefzbMATHGoogle Scholar
  17. 17.
    Miller K, Horton A, Joldes GR, Wittek A (2012) Beyond finite elements: a comprehensive, patient-specific neurosurgical simulation utilizing a meshless method. J Biomech 45(15):2698–2270CrossRefGoogle Scholar
  18. 18.
    Johnson H, Harris G, Williams K. 2007. Brainsfit: mutual information registrations of whole-brain 3d images, using the insight toolkit.Google Scholar
  19. 19.
    Liu Y, Kot A, Drakopoulos F, Yao C, Fedorov A, Enquobahrie A, Clatz O, Chrisochoides NP (2014) An itk implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery. Front Neuroinfo 8:33Google Scholar
  20. 20.
    Yixun L, Chrisochoides N (2013) Heterogeneous biomechanical model on correcting brain deformation induced by tumor resection. In: Computational biomechanics for medicine. Springer, New York, pp 115–126Google Scholar
  21. 21.
    Drakopoulos F, Liu Y, Foteinos P, Chrisochoides NP (2014) Towards a real time multi-tissue adaptive physics based non-rigid registration framework for brain tumor resection. Front Neuroinfo 8:11CrossRefGoogle Scholar
  22. 22.
    Foteinos P, Chrisochoides N (2014) High quality real-time image-to-mesh conversion for finite element simulations. J Parallel Distrib Comput 74(2):2123–2140CrossRefGoogle Scholar
  23. 23.
    Commandeur F, Velut J, Acosta O (2011) A VTK algorithm for the computation of the Hausdorff distance. VTK J 839Google Scholar
  24. 24.
    Hastreiter P, Rezk-Salama C, Soza G, Bauer M, Greiner G, Fahlbusch R, Ganslandt O, Nimsky C (2004) Strategies for brain shift evaluation. Med Image Anal 8(4):447–464CrossRefzbMATHGoogle Scholar
  25. 25.
    Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fotis Drakopoulos
    • 1
  • Chengjun Yao
    • 2
  • Yixun Liu
    • 1
  • Nikos Chrisochoides
    • 1
    Email author
  1. 1.The Department of Computer ScienceOld Dominion UniversityNorfolkUSA
  2. 2.The Department of NeurosurgeryHuashan HospitalShanghaiChina

Personalised recommendations

Cite paper

Buy options