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.
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References
Armijo L (1966) Minimization of functions having Lipschitz continuous first partial derivatives. Pac J Math 16(1):1–3
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 414–421
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
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)
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 122–127
Burschka D, Li M, Ishiia M, Taylor RH, Hager GD (2005) Scale-invariant registration of monocular endoscopic images to ct-scans for sinus surgery. Med Image Anal 9(5):413–426
Clarenz U, Rumpf M, Telea A (2004) Robust feature detection and local classification for surfaces based on moment analysis. IEEE Trans Vis Comput Graph 10(5):516–524
Clarkson MJ, Rueckert D, Hill DLG, Hawkes DJ (2001) Using photo-consistency to register 2D optical images of the human face to a 3D surface model. IEEE Trans Pattern Anal Mach Intell 23(11):1266–1280
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 198–203
Dalal SS, Edwards E, Kirsch HE, Barbaro NM, Knight RT, Nagarajan SS (2008) Localization of neurosurgically implanted electrodes via photograph-MRI-radiograph coregistration. J Neurosci Methods 174(1):106–115
Fischla B, Serenob MI, Dalea AM (1999) Cortical surface-based analysis: II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9(2):195–207
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 624–635
Kuhnt D, Bauer MH, Nimsky C (2012) Brain shift compensation and neurosurgical image fusion using intraoperative MRI: current status and future challenges. Crit Rev Biomed Eng 40:175–185
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
Markelja P, Tomaževiča D, Likara B, Pernuša F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16(3):642–661
Modersitzki J (2004) Numerical methods for image registration. Oxford University Press, Oxford
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Reinges MHT, Nguyen HH, Krings T, Hütter BO, Rohde V, Gilsbach JM (2004) Course of brain shift during microsurgical resection of supratentorial cerebral lesions: limits of conventional neuronavigation. Acta Neurochir (Wien) 146(4):369–377
Sethian JA (1996) A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci USA 93:1591–1595
Sundaramoorthi G, Yezzi A, Mennucci A (2007) Sobolev active contours. Int J Comput Vis 73(3):345–366
Tharin S, Golby A (2007) Functional brain mapping and its applications to neurosurgery. Neurosurgery 60(4 Suppl 2):185–202
Wang A, Mirsattari SM, Parrent AG, Peters TM (2011) Fusion and visualization of intraoperative cortical images with preoperative models for epilepsy surgical planning and guidance. Comput Aided Surg 16(4):149–160
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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Berkels, B., Cabrilo, I., Haller, S. et al. Co-registration of intra-operative brain surface photographs and pre-operative MR images. Int J CARS 9, 387–400 (2014). https://doi.org/10.1007/s11548-014-0979-y
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DOI: https://doi.org/10.1007/s11548-014-0979-y