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Electrode localization for planning surgical resection of the epileptogenic zone in pediatric epilepsy



   In planning for a potentially curative resection of the epileptogenic zone in patients with pediatric epilepsy, invasive monitoring with intracranial EEG is often used to localize the seizure onset zone and eloquent cortex. A precise understanding of the location of subdural strip and grid electrodes on the brain surface, and of depth electrodes in the brain in relationship to eloquent areas is expected to facilitate pre-surgical planning.


   We developed a novel algorithm for the alignment of intracranial electrodes, extracted from post-operative CT, with pre-operative MRI. Our goal was to develop a method of achieving highly accurate localization of subdural and depth electrodes, in order to facilitate surgical planning. Specifically, we created a patient-specific 3D geometric model of the cortical surface from automatic segmentation of a pre-operative MRI, automatically segmented electrodes from post-operative CT, and projected each set of electrodes onto the brain surface after alignment of the CT to the MRI. Also, we produced critical visualization of anatomical landmarks, e.g., vasculature, gyri, sulci, lesions, or eloquent cortical areas, which enables the epilepsy surgery team to accurately estimate the distance between the electrodes and the anatomical landmarks, which might help for better assessment of risks and benefits of surgical resection.


   Electrode localization accuracy was measured using knowledge of the position of placement from 2D intra-operative photographs in ten consecutive subjects who underwent intracranial EEG for pediatric epilepsy. Average spatial accuracy of localization was \(1.31\pm 0.69 \text{ mm }\) for all 385 visible electrodes in the photos.


   In comparison with previously reported approaches, our algorithm is able to achieve more accurate alignment of strip and grid electrodes with minimal user input. Unlike manual alignment procedures, our algorithm achieves excellent alignment without time-consuming and difficult judgements from an operator.

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  1. 1.

    This software will be available online (

  2. 2.

    The currently distributed software is available from


  1. 1.

    Hirtz D, Thurman D, Gwinn-Hardy K, Mohamed M, Chaudhuri A, Zalutsky R (2007) How common are the “common” neurologic disorders? Neurology 68(5):326–337

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Hauser WA, Annegers JF, Kurland LT (2007) Prevalence of epilepsy in Rochester, Minnesota: 1940–1980. Epilepsia 32(4):429–445

    Article  Google Scholar 

  3. 3.

    Kwan P, Brodie MJ (2001) Effectiveness of first antiepileptic drug. Epilepsia 42(10):1255–1260

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Schmidt D (2011) Efficacy of new antiepileptic drugs. Epilepsy Curr 11(1):9–11

    PubMed Central  PubMed  Article  Google Scholar 

  5. 5.

    Engel J Jr, McDermott MP, Wiebe S, Langfitt JT, Stern JM, Dewar S, Sperling MR, Gardiner I, Erba G, Fried I (2012) Early surgical therapy for drug-resistant temporal lobe epilepsy a randomized trial. JAMA J Am Med Assoc 307(9):922–930

    CAS  Article  Google Scholar 

  6. 6.

    Binnie CD, Polkey CE (2000) Commission on neurosurgery of the international league against epilepsy (ILAE) 1993–1997: recommended standards. Epilepsia 41(10):1346–1349

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Eksioglu Y, Riviello JJ Jr (2010) Intractable epilepsy in children and selection of surgical candidates. In: Cataltepe O, Jallo GI (eds) Pediatric epilepsy surgery: preoperative assessment and surgical treatment, Chap 2. Thieme Medical Publishers, New York, pp 7–13

  8. 8.

    Harvey AS, Cross JH, Shinnar S, Mathern GW (2008) Defining the spectrum of international practice in pediatric epilepsy surgery patients. Epilepsia 49(1):146–155

    PubMed  Article  Google Scholar 

  9. 9.

    Behrens E, Zentner J, Van Roost D, Hufnagel A, Elger C, Schramm J (1994) Subdural and depth electrodes in the presurgical evaluation of epilepsy. Acta Neurochirur 128(1):84–87

    CAS  Article  Google Scholar 

  10. 10.

    Engel AK, Moll CKE, Fried I, Ojemann GA (2005) Invasive recordings from the human brain: clinical insights and beyond. Nat Rev Neurosci 6(1):35–47

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Cataltepe O, Jallo GI (2010) Pediatric Epilepsy Surgery: Preoperative Assessment and Surgical Treatment. Thieme Medical Publishers, New York

  12. 12.

    Rosenow F, Lüders H (2001) Presurgical evaluation of epilepsy. Brain 124(9):1683–1700

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Wellmer J, Von Oertzen J, Schaller C, Urbach H, König R, Widman G, Van Roost D, Elger CE (2002) Digital photography and 3D MRI-based multimodal imaging for individualized planning of resective neocortical epilepsy surgery. Epilepsia 43(12):1543–1550

    PubMed  Article  Google Scholar 

  14. 14.

    Datta A, Loddenkemper T (2011) The epileptogenic zone In: Wyllie E, Cascino GD, Gidal BE, Goodkin H (eds) Wyllie’s treatment of epilepsy. Principles and practice, 5 edn. Lippincott, Williams & Wilkins, Philadelphia, pp 818–827

  15. 15.

    Dykstra AR, Chan AM, Quinn BT, Zepeda R, Keller CJ, Cormier J, Madsen JR, Eskandar EN, Cash SS (2011) Individualized localization and cortical surface-based registration of intracranial electrodes. NeuroImage 59:3563–3570

    PubMed Central  PubMed  Article  Google Scholar 

  16. 16.

    Hermes D, Miller KJ, Noordmans HJ, Vansteensel MJ, Ramsey NF (2010) Automated electrocorticographic electrode localization on individually rendered brain surfaces. J Neurosci Methods 185(2):293–298

    PubMed  Article  Google Scholar 

  17. 17.

    LaViolette PS, Rand SD, Ellingson BM, Raghavan M, Lew SM, Schmainda KM, Mueller W (2011) 3D visualization of subdural electrode shift as measured at craniotomy reopening. Epilepsy Res 94(1):102–109

    PubMed  Article  Google Scholar 

  18. 18.

    Tao JX, Hawes-Ebersole S, Baldwin M, Shah S, Erickson RK, Ebersole JS (2009) The accuracy and reliability of 3D CT/MRI co-registration in planning epilepsy surgery. Clin Neurophysiol 120(4):748–753

    PubMed  Article  Google Scholar 

  19. 19.

    Wang Y, Agarwal R, Nguyen D, Domocos V, Gotman J (2006) Intracranial electrode visualization in invasive pre-surgical evaluation for epilepsy. In: 27th annual international conference of the engineering in medicine and biology society. IEEE-EMBS 2005. pp 952–955

  20. 20.

    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

    PubMed Central  PubMed  Article  Google Scholar 

  21. 21.

    Andreas HJ, Huppertz HJ, Comeau RM, Honegger JB, Spreer JM, Zentner JK (2002) Visualization of subdural strip and grid electrodes using curvilinear reformatting of 3D MR imaging data sets. Am J Neuroradiol 23(3):400–403

    Google Scholar 

  22. 22.

    Kovalev D, Spreer J, Honegger J, Zentner J, Schulze-Bonhage A, Huppertz HJ (2005) Rapid and fully automated visualization of subdural electrodes in the presurgical evaluation of epilepsy patients. Am J Neuroradiol 26(5):1078–1083

    PubMed  Google Scholar 

  23. 23.

    Morris K, O’brien TJ, Cook MJ, Murphy M, Bowden SC (2004) A computer-generated stereotactic “Virtual Subdural Grid” to guide resective epilepsy surgery. Am J Neuroradiol 25(1):77– 83

    PubMed  Google Scholar 

  24. 24.

    Carmichael DW, Thornton JS, Rodionov R, Thornton R, McEvoy A, Allen PJ, Lemieux L (2008) Safety of localizing epilepsy monitoring intracranial electroencephalograph electrodes using MRI: radiofrequency-induced heating. J Magn Reson Imag 28(5):1233–1244

    Article  Google Scholar 

  25. 25.

    Davis LM, Spencer DD, Spencer SS, Bronen RA (1999) MR imaging of implanted depth and subdural electrodes: is it safe? Epilepsy Res 35(2):95–98

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Yang AI, Wang X, Doyle W, Halgren E, Carlson C, Belcher TL, Cash SS, Devinsky O, Thesen T (2012) Localization of dense intracranial electrode arrays using magnetic resonance imaging. Neuroimage 63:157–165

    PubMed  Article  Google Scholar 

  27. 27.

    Daga P, Modat M, Micallef C, Mancini L, White M, Cardoso MJ, Kitchen N, McEvoy A, Thornton J, Yousry T and others (2010) Near real time brain shift estimation for interventional MRI suite. High-Performance Computing (HP) workshop associated with Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

  28. 28.

    Nabavi A, Black PML, Gering DT, Westin CF, Mehta V, Pergolizzi RS Jr, Ferrant M, Warfield SK, Hata N, Schwartz RB (2001) Serial intraoperative magnetic resonance imaging of brain shift. Neurosurgery 48(4):787–798

    CAS  PubMed  Google Scholar 

  29. 29.

    Darcey TM, Roberts DW (2010) Technique for the localization of intracranially implanted electrodes. J Neurosurg 113(6):1182–1185

    PubMed  Article  Google Scholar 

  30. 30.

    Immonen A, Jutila L, Könönen M, Mervaala E, Partanen J, Puranen M, Rinne J, Ylinen A, Vapalahti M (2003) 3-D reconstructed magnetic resonance imaging in localization of subdural EEG electrodes: case illustration. Epilepsy Res 54(1):59–62

    PubMed  Article  Google Scholar 

  31. 31.

    Kamida T, Anan M, Shimotaka K, Abe T, Fujiki M, Kobayashi H (2010) Visualization of subdural electrodes with fusion CT scan/MRI during neuronavigation-guided epilepsy surgery. J Clin Neurosci 17(4):511–513

    PubMed  Article  Google Scholar 

  32. 32.

    Murphy MA, O’Brien TJ, Morris K, Cook MJ (2004) Multimodality image-guided surgery for the treatment of medically refractory epilepsy. J Neurosurg 100(3):452–462

    PubMed  Article  Google Scholar 

  33. 33.

    Archip N, Jolesz FA, Warfield SK (2007) A validation framework for brain tumor segmentation. Acad Radiol 14(10):1242–1251

    PubMed  Article  Google Scholar 

  34. 34.

    Clatz O, Delingette H, Talos IF, Golby AJ, Kikinis R, Jolesz FA, Ayache N, Warfield SK (2005) Robust nonrigid registration to capture brain shift from intraoperative MRI. IEEE Trans Med Imaging 24(11):1417–1427

    PubMed Central  PubMed  Article  Google Scholar 

  35. 35.

    Ferrant M, Nabavi A, Macq B, Black PML, Jolesz FA, Kikinis R, Warfield SK (2002) Serial registration of intraoperative MR images of the brain. Med Image Anal 6(4):337–359

    PubMed  Article  Google Scholar 

  36. 36.

    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):1384–1397

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Mahvash M, König R, Wellmer J, Urbach H, Meyer B, Schaller K (2007) Coregistration of digital photography of the human cortex and cranial magnetic resonance imaging for visualization of subdural electrodes in epilepsy surgery. Neurosurgery 61(5): 340–345

    Google Scholar 

  38. 38.

    Sebastiano F, Di Gennaro G, Esposito V, Picardi A, Morace R, Sparano A, Mascia A, Colonnese C, Cantore G, Quarato P (2006) A rapid and reliable procedure to localize subdural electrodes in presurgical evaluation of patients with drug-resistant focal epilepsy. Clin Neurophysiol 117(2):341–347

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Akselrod-Ballin A, Bock D, Reid RC, Warfield SK (2011) Accelerating image registration with the Johnson-Lindenstrauss Lemma: application to imaging 3-D neural ultrastructure with electron microscopy. IEEE Trans Med Imaging 30(7):1427–1438

    PubMed Central  PubMed  Article  Google Scholar 

  40. 40.

    Wells WM, Viola P, Atsumi H, Nakajima S, Kikinis R (1996) Multi-modal volume registration by maximization of mutual information. Med Image Anal 1(1):35–51

    PubMed  Article  Google Scholar 

  41. 41.

    Powell MJ (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7(2):155–162

    Article  Google Scholar 

  42. 42.

    Warfield SK, Jolesz FA, Kikinis R (1998) A high performance computing approach to the registration of medical imaging data. Parallel Comput 24(9):1345–1368

    Article  Google Scholar 

  43. 43.

    Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2):187–198

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Akhondi-Asl A, Warfield SK (2013) Simultaneous truth and performance level estimation through fusion of probabilistic segmentations. IEEE Trans Med Imaging. doi:10.1109/TMI.2013.2266258

    PubMed Central  PubMed  Google Scholar 

  45. 45.

    Commowick O, Akhondi-Asl A, Warfield SK (2012) Estimating a reference standard segmentation with spatially varying performance parameters: local MAP STAPLE. IEEE Trans Med Imaging 31(8):1593–1606

    PubMed Central  PubMed  Article  Google Scholar 

  46. 46.

    Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23(7):903–921

    PubMed Central  PubMed  Article  Google Scholar 

  47. 47.

    Atkeson CG, Moore AW, Schaal S (1997) Locally weighted learning. Artif Intell Rev 11(1):11–73

    Article  Google Scholar 

  48. 48.

    Hartley R, Zisserman A (2000) Multiple view geometry in computer vision, vol 2. Cambridge Univ Press, Cambridge

    Google Scholar 

  49. 49.

    Trucco E, Verri A (1998) Introductory techniques for 3-D computer vision, vol 93. Prentice Hall, Upper Saddle River

    Google Scholar 

  50. 50.

    Tomas-Fernandez X (2012) Warfield S Population intensity outliers or a new model for brain WM abnormalities. In: 9th IEEE international symposium on biomedical imaging (ISBI), 2012. pp 1543–1546

  51. 51.

    Beyer J, Hadwiger M, Wolfsberger S, Buhler K (2007) High-quality multimodal volume rendering for preoperative planning of neurosurgical interventions. IEEE Trans Vis Comput Graph 13(6):1696–1703

    Google Scholar 

  52. 52.

    Dimaio SP, Archip N, Hata N, Talos IF, Warfield SK, Majumdar A, McDannold N, Hynynen K, Morrison PR, Wells W (2006) Image-guided neurosurgery at Brigham and Women’s Hospital. IEEE Eng Med Biol Mag 25(5):67–73

    PubMed  Article  Google Scholar 

  53. 53.

    Schiffbauer H, Ferrari P, Rowley HA, Berger MS, Roberts TPL (2001) Functional activity within brain tumors: a magnetic source imaging study. Neurosurgery 49(6):1313–1321

    CAS  PubMed  Article  Google Scholar 

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This research was supported in part by NIH grants R01 RR021885, R01 EB008015, R03 EB008680, R01 LM010033, and R01 EB013248.

Conflict of Interest

Vahid Taimouri, Alireza Akhondi-Asl, Xavier Tomas-Fernandez, Jurriaan M. Peters, Sanjay P. Prabhu, Annapurna Poduri, Masanori Takeoka, Tobias Loddenkemper, Ann Marie R. Bergin, Chellamani Harini, Joseph R. Madsen, and Simon K. Warfield declare that they have no conflict of interest.

Ethical Standards Statement 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).

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Correspondence to Vahid Taimouri.

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Taimouri, V., Akhondi-Asl, A., Tomas-Fernandez, X. et al. Electrode localization for planning surgical resection of the epileptogenic zone in pediatric epilepsy. Int J CARS 9, 91–105 (2014).

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  • Pediatric epilepsy
  • Intracranial EEG
  • Electrode localization
  • Epilepsy surgery planning