Co-registration of intra-operative brain surface photographs and pre-operative MR images

  • Benjamin Berkels
  • Ivan Cabrilo
  • Sven Haller
  • Martin Rumpf
  • Karl Schaller
Original Article

Abstract

Purpose

   Brain shift, the change in configuration of the brain after opening the dura mater, is a significant problem for neuronavigation. Brain structures at intra-operative deformed positions must be matched with corresponding structures in the pre-operative 3D planning data. A method to co-register the cortical surface from intra-operative microscope images with pre-operative MRI-segmented data was developed and tested.

Methods

   Automated classification of sulci on MRI-extracted cortical surfaces was tested by comparison with user guided marking of prominent sulci on an intra-operative photography. A variational registration method with a fidelity energy for 3D deformations of the cortical surface in conjunction with a higher-order, linear elastic prior energy was used for the actual registration. The minimization of this energy was performed with a regularized gradient descent scheme using finite elements for spatial discretization. The sulcal classification method was tested on eight different clinical MRI data sets by comparison of the deformed MRI scans with intra-operative photographs of the brain surface.

Results

   User intervention was required for marking sulci on the photographs demonstrating the potential for incorporating an automatic classifier. The actual registration was validated first on an artificial testbed. The complete algorithm for the co-registration of actual clinical MRI data was successful for eight different patients.

Conclusions

   Pre-operative MRI scans can be registered to intra-operative brain surface photographs using a surface-to-surface registration method. This co-registration method has potential applications in neurosurgery, particularly during functional procedures.

Keywords

Elastic registration Brain segmentation Sulci  Variational methods Surface classification  Cortical surface tracking 

Notes

Acknowledgments

Benjamin Berkels and Martin Rumpf acknowledge the support by the Deutsche Forschungsgemeinschaft via the Grant Ru 567/12-1 and the Hausdorff Center for Mathematics, EXC 59. Furthermore, the authors acknowledge equipment support from Carl Zeiss (Germany). The research herein was originally started while Benjamin Berkels was at the Institute for Numerical Simulation, University of Bonn, Germany, and performed in part while he was holding a visiting position at the Institute of Mathematics and Image Computing, University of Lübeck, Germany.

Conflict of interest

Benjamin Berkels, Ivan Cabrilo, Sven Haller, Martin Rumpf and Karl Schaller declare that they have no conflict of interest. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from all patients for being included in the study.

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Copyright information

© CARS 2014

Authors and Affiliations

  • Benjamin Berkels
    • 1
  • Ivan Cabrilo
    • 2
    • 3
  • Sven Haller
    • 3
    • 4
  • Martin Rumpf
    • 5
  • Karl Schaller
    • 2
    • 3
  1. 1.Aachen Institute for Advanced Study in Computational Engineering Science (AICES)RWTH Aachen University AachenGermany
  2. 2.Department of NeurosurgeryUniversity Hospitals of Geneva GenevaSwitzerland
  3. 3.Faculty of MedicineUniversity of Geneva GenevaSwitzerland
  4. 4.Service neuro-diagnostique et neuro-interventionnel DISIMHôpitaux Universitaires de Genève Genève 14Switzerland
  5. 5.Institut für Numerische SimulationRheinische Friedrich-Wilhelms-Universität Bonn BonnGermany

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