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Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates

Depth intracranial electrodes (IEs) placement is one of the most used procedures to identify the epileptogenic zone (EZ) in surgical treatment of drug resistant epilepsy patients, about 20–30% of this population. IEs localization is therefore a critical issue defining the EZ and its relation with el...

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Autores principales: Princich, Juan Pablo, Wassermann, Demian, Latini, Facundo, Oddo, Silvia, Blenkmann, Alejandro Omar, Seifer, Gustavo, Kochen, Silvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3876273/
https://www.ncbi.nlm.nih.gov/pubmed/24427112
http://dx.doi.org/10.3389/fnins.2013.00260
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author Princich, Juan Pablo
Wassermann, Demian
Latini, Facundo
Oddo, Silvia
Blenkmann, Alejandro Omar
Seifer, Gustavo
Kochen, Silvia
author_facet Princich, Juan Pablo
Wassermann, Demian
Latini, Facundo
Oddo, Silvia
Blenkmann, Alejandro Omar
Seifer, Gustavo
Kochen, Silvia
author_sort Princich, Juan Pablo
collection PubMed
description Depth intracranial electrodes (IEs) placement is one of the most used procedures to identify the epileptogenic zone (EZ) in surgical treatment of drug resistant epilepsy patients, about 20–30% of this population. IEs localization is therefore a critical issue defining the EZ and its relation with eloquent functional areas. That information is then used to target the resective surgery and has great potential to affect outcome. We designed a methodological procedure intended to avoid the need for highly specialized medical resources and reduce time to identify the anatomical location of IEs, during the first instances of intracranial EEG recordings. This workflow is based on established open source software; 3D Slicer and Freesurfer that uses MRI and Post-implant CT fusion for the localization of IEs and its relation with automatic labeled surrounding cortex. To test this hypothesis we assessed the time elapsed between the surgical implantation process and the final anatomical localization of IEs by means of our proposed method compared against traditional visual analysis of raw post-implant imaging in two groups of patients. All IEs were identified in the first 24 H (6–24 H) of implantation using our method in 4 patients of the first group. For the control group; all IEs were identified by experts with an overall time range of 36 h to 3 days using traditional visual analysis. It included (7 patients), 3 patients implanted with IEs and the same 4 patients from the first group. Time to localization was restrained in this group by the specialized personnel and the image quality available. To validate our method; we trained two inexperienced operators to assess the position of IEs contacts on four patients (5 IEs) using the proposed method. We quantified the discrepancies between operators and we also assessed the efficiency of our method to define the EZ comparing the findings against the results of traditional analysis.
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spelling pubmed-38762732014-01-14 Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates Princich, Juan Pablo Wassermann, Demian Latini, Facundo Oddo, Silvia Blenkmann, Alejandro Omar Seifer, Gustavo Kochen, Silvia Front Neurosci Neuroscience Depth intracranial electrodes (IEs) placement is one of the most used procedures to identify the epileptogenic zone (EZ) in surgical treatment of drug resistant epilepsy patients, about 20–30% of this population. IEs localization is therefore a critical issue defining the EZ and its relation with eloquent functional areas. That information is then used to target the resective surgery and has great potential to affect outcome. We designed a methodological procedure intended to avoid the need for highly specialized medical resources and reduce time to identify the anatomical location of IEs, during the first instances of intracranial EEG recordings. This workflow is based on established open source software; 3D Slicer and Freesurfer that uses MRI and Post-implant CT fusion for the localization of IEs and its relation with automatic labeled surrounding cortex. To test this hypothesis we assessed the time elapsed between the surgical implantation process and the final anatomical localization of IEs by means of our proposed method compared against traditional visual analysis of raw post-implant imaging in two groups of patients. All IEs were identified in the first 24 H (6–24 H) of implantation using our method in 4 patients of the first group. For the control group; all IEs were identified by experts with an overall time range of 36 h to 3 days using traditional visual analysis. It included (7 patients), 3 patients implanted with IEs and the same 4 patients from the first group. Time to localization was restrained in this group by the specialized personnel and the image quality available. To validate our method; we trained two inexperienced operators to assess the position of IEs contacts on four patients (5 IEs) using the proposed method. We quantified the discrepancies between operators and we also assessed the efficiency of our method to define the EZ comparing the findings against the results of traditional analysis. Frontiers Media S.A. 2013-12-31 /pmc/articles/PMC3876273/ /pubmed/24427112 http://dx.doi.org/10.3389/fnins.2013.00260 Text en Copyright © 2013 Princich, Wassermann, Latini, Oddo, Blenkmann, Seifer and Kochen. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Princich, Juan Pablo
Wassermann, Demian
Latini, Facundo
Oddo, Silvia
Blenkmann, Alejandro Omar
Seifer, Gustavo
Kochen, Silvia
Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates
title Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates
title_full Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates
title_fullStr Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates
title_full_unstemmed Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates
title_short Rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates
title_sort rapid and efficient localization of depth electrodes and cortical labeling using free and open source medical software in epilepsy surgery candidates
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3876273/
https://www.ncbi.nlm.nih.gov/pubmed/24427112
http://dx.doi.org/10.3389/fnins.2013.00260
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