Co-registration of intra-operative brain surface photographs and pre-operative MR images
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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.
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.
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.
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.
KeywordsElastic registration Brain segmentation Sulci Variational methods Surface classification Cortical surface tracking
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.
- 2.Bauer S, Berkels B, Ettl S, Arold O, Hornegger J, Rumpf M (2012) Marker-less reconstruction of dense 4-d surface motion fields using active laser triangulation from sparse measurements for respiratory motion management. In: Medical image computing and computer-assisted intervention (MICCAI 2012). Lecture notes in computer science, vol 7510, pp 414–421Google Scholar
- 3.Berkels B, Kotowski M, Rumpf M, Schaller C (2011) Sulci detection in photos of the human cortex based on learned discriminative dictionaries. In: Scale space and variational methods in computer vision. Lecture notes in computer science Google Scholar
- 4.Berkels B, Bauer S, Ettl S, Arold O, Hornegger J, Rumpf M (2013a) Joint surface reconstruction and 4-D deformation estimation from sparse data and prior knowledge for marker-less respiratory motion management. Med Phys (accepted)Google Scholar
- 5.Berkels B, Cabrilo I, Haller S, Rumpf M, Schaller K (2013b) Co-registration of intra-operative photographs and pre-operative MR images. In: Bildverarbeitung für die Medizin 2013, Springer, pp 122–127Google Scholar
- 9.Cyr CM, Kamal AF, Sebastian TB, Kimia BB (2000) 2D–3D registration based on shape matching. In: Proceedings of the IEEE workshop on mathematical methods in biomedical image analysis, pp 198–203Google Scholar
- 12.Heldmann S, Papenberg N (2009) A variational approach for volume-to-slice registration. Scale space and variational methods in computer vision. Lecture notes in computer science, vol 5567, pp 624–635Google Scholar
- 14.Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2008) Discriminative learned dictionaries for local image analysis. In: IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 1–8, doi: 10.1109/CVPR.2008.4587652
- 16.Modersitzki J (2004) Numerical methods for image registration. Oxford University Press, OxfordGoogle Scholar