title: Electrode localization for planning surgical resection of the epileptogenic zone in pediatric epilepsy.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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 ± 0.69 mm for all 385 visible electrodes in the photos.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Computational Radiology Laboratory, Boston, MA, USA, vahid.taimouri@childrens.harvard.edu.
authors.forename: Vahid
authors.initials: V
authors.lastname: Taimouri
authors.forename: Alireza
authors.initials: A
authors.lastname: Akhondi-Asl
authors.forename: Xavier
authors.initials: X
authors.lastname: Tomas-Fernandez
authors.forename: Jurriaan M
authors.initials: JM
authors.lastname: Peters
authors.forename: Sanjay P
authors.initials: SP
authors.lastname: Prabhu
authors.forename: Annapurna
authors.initials: A
authors.lastname: Poduri
authors.forename: Masanori
authors.initials: M
authors.lastname: Takeoka
authors.forename: Tobias
authors.initials: T
authors.lastname: Loddenkemper
authors.forename: Ann Marie R
authors.initials: AM
authors.lastname: Bergin
authors.forename: Chellamani
authors.initials: C
authors.lastname: Harini
authors.forename: Joseph R
authors.initials: JR
authors.lastname: Madsen
authors.forename: Simon K
authors.initials: SK
authors.lastname: Warfield
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery*; D002648:Child; D004567:Electrodes, Implanted; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis / Q000601:surgery*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D019635:Neurosurgical Procedures / Q000379:methods*; D025321:Surgery, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*