Abstract
Object: Subdural or deep intracerebral electrodes are essential in order to precisely localize epileptic zone in patients with medically intractable epilepsy. Precise localization of the implanted electrodes is critical to clinical diagnosing and treatment as well as for scientific studies. In this study, we sought to automatically and precisely extract intracranial electrodes using pre-operative MRI and post-operative CT images.Method: The subdural and depth intracranial electrodes were readily detected using clustering-based segmentation. Depth electrodes were tracked by fitting a quadratic curve to account for potential bending during the neurosurgery. The identified electrodes can be manipulated using a graphic interface and labeled to cortical areas in individual native space based on anatomical parcellation and displayed in the volume and surface space.Results: The electrodes' localizations were validated with high precision. The electrode coordinates were normalized to a standard space. Moreover, the probabilistic value being to a specific area or a functional network was provided.Conclusions: We developed an integrative toolbox to reconstruct and label the intracranial electrodes implanted in the patients with medically intractable epilepsy. This toolbox provided a convenient way to allow inter-subject comparisons and relation of intracranial EEG findings to the larger body of neuroimaging literature.
Highlights
Intracranial electroencephalograph techniques, including electrocorticography (ECoG) and stereotactic electroencephalography (SEEG), have been widely used in clinics to assess the localization of seizure onset zone for patients with medically intractable epilepsy
We proposed an integrative solution to localize both SEEG and ECoG electrodes based on pre-operative T1 and post-operative CT
Patients with either SEEG or ECoG electrodes implanted were included in this study
Summary
Intracranial electroencephalograph techniques, including electrocorticography (ECoG) and stereotactic electroencephalography (SEEG), have been widely used in clinics to assess the localization of seizure onset zone for patients with medically intractable epilepsy. One recent method proposed (Arnulfo et al, 2015) introduced an appropriate way to automatically segment the SEEG contacts with good reliability, but their method requires two high-resolution CT images (i.e., before and after electrode implantation), which might be unconventional for many hospitals. This constraint applied to the previous studies using two MRI images (Kovalev et al, 2005). These methods are accompanied by more or less deficiencies, such as relatively time consuming, unsatisfied accuracy, no precisely cortical information or only application to subdural electrodes
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