Abstract
This paper presents a fully automatic procedure for optimization of depth electrode implantation planning in epilepsy. To record intracranial EEG in some patients with intractable epilepsy, depth electrodes are implanted through holes in the skull. The proposed fully automatic procedure maximizes recording coverage of the target volume by estimating the EEG recorded from each contact, while minimizing the risk of approaching vessels and other critical structures. All structures, including the hippocampus and amygdala were automatically segmented. We retrospectively validated the procedure for mesial temporal lobe implantations in 11 hemispheres. The automatic trajectories recorded from a larger volume of interest than the original manually selected trajectories while better avoiding the segmented structures. The procedure is integrated into a neuronavigation system enabling the surgeon to visualize the selected trajectories from an ordered list and, if necessary, enables re-planning a trajectory in near real time.
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References
Engel, J., Pedley, T.A.: Epilepsy: A Comprehensive Textbook. Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia (2008)
Bériault, S., Subaie, F.A., Lalys, F., Collins, D.L., Pike, G.B., Sadikot, A.F.: A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int. J. Comput. Assist. Radiol. Surg. 7, 1–18 (2012)
Essert, C., Haegelen, C., Lalys, F., Abadie, A., Jannin, P.: Automatic computation of electrode trajectories for deep brain stimulation: a hybrid symbolic and numerical approach. Int. J. Comput. Assist. Radiol. Surg. 7, 517–532 (2012)
Guo, T., Parrent, A.G., Peters, T.M.: Automatic target and trajectory identification for deep brain stimulation (DBS) procedures. In: Ayache, Nicholas, Ourselin, Sébastien, Maeder, Anthony (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 483–490. Springer, Heidelberg (2007)
Liu, Y., et al.: A surgeon specific automatic path planning algorithm for deep brain stimulation. In: Proceedings of the SPIE 8316 Medical Imaging 2011, p. 83161D (2011)
Seitel, A., et al.: Computer-assisted trajectory planning for percutaneous needle insertions. Med. Phys. 38, 3246–3259 (2011)
De Momi, E., Caborni, C., Cardinale, F., Castana, L., Casaceli, G., Cossu, M., Antiga, L., Ferrigno, G.: Automatic trajectory planner for StereoElectroEncephaloGraphy procedures: a retrospective study. IEEE Trans. Biomed. Eng. 4, 986–993 (2013)
Mercier, L., et al.: New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: system description and validation. Int. J. Comput. Assist. Radiol. Surg. 6, 507–522 (2011)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)
Nyul, L.G., Udupa, J.K., Saha, P.K.: Incorporating a measure of local scale in voxel-based 3-D image registration. IEEE Trans. Med. Imaging 22, 228–237 (2003)
Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., et al.: A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 1293–1322 (2001)
Eskildsen, S.F.: BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage 59, 2362–2373 (2012)
Collins, D.L., Pruessner, J.C.: Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. NeuroImage 52, 1355–1366 (2010)
Collins, D.L., Zijdenbos, A., Baaré, W., Evans, A.: ANIMAL+INSECT: improved cortical structure segmentation. In: Kuba, A., Šáamal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 210–223. Springer, Heidelberg (1999)
Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27, 425–441 (2008)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Danielsson, P.E.: Euclidean distance mapping. Comput. Graph. Image Process. 14, 227–248 (1980)
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This study was supported in part by CIHR MOP-97820 and by MNI CIBC postdoctoral fellowship in brain imaging.
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Zelmann, R. et al. (2014). Automatic Optimization of Depth Electrode Trajectory Planning. In: Erdt, M., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2013. Lecture Notes in Computer Science(), vol 8361. Springer, Cham. https://doi.org/10.1007/978-3-319-05666-1_13
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DOI: https://doi.org/10.1007/978-3-319-05666-1_13
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