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MRI and CT Fusion in Stereotactic Electroencephalography (SEEG)
Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locat...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670384/ https://www.ncbi.nlm.nih.gov/pubmed/37998556 http://dx.doi.org/10.3390/diagnostics13223420 |
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author | Pérez Hinestroza, Jaime Mazo, Claudia Trujillo, Maria Herrera, Alejandro |
author_facet | Pérez Hinestroza, Jaime Mazo, Claudia Trujillo, Maria Herrera, Alejandro |
author_sort | Pérez Hinestroza, Jaime |
collection | PubMed |
description | Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locating the epileptogenic tissue, a task achieved using diagnostic techniques like Stereotactic Electroencephalography (SEEG). SEEG utilizes multi-modal fusion to aid in electrode localization, using pre-surgical resonance and post-surgical computer tomography images as inputs. To ensure the absence of artifacts or misregistrations in the resultant images, a fusion method that accounts for electrode presence is required. We proposed an image fusion method in SEEG that incorporates electrode segmentation from computed tomography as a sampling mask during registration to address the fusion problem in SEEG. The method was validated using eight image pairs from the Retrospective Image Registration Evaluation Project (RIRE). After establishing a reference registration for the MRI and identifying eight points, we assessed the method’s efficacy by comparing the Euclidean distances between these reference points and those derived using registration with a sampling mask. The results showed that the proposed method yielded a similar average error to the registration without a sampling mask, but reduced the dispersion of the error, with a standard deviation of 0.86 when a mask was used and 5.25 when no mask was used. |
format | Online Article Text |
id | pubmed-10670384 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106703842023-11-09 MRI and CT Fusion in Stereotactic Electroencephalography (SEEG) Pérez Hinestroza, Jaime Mazo, Claudia Trujillo, Maria Herrera, Alejandro Diagnostics (Basel) Article Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locating the epileptogenic tissue, a task achieved using diagnostic techniques like Stereotactic Electroencephalography (SEEG). SEEG utilizes multi-modal fusion to aid in electrode localization, using pre-surgical resonance and post-surgical computer tomography images as inputs. To ensure the absence of artifacts or misregistrations in the resultant images, a fusion method that accounts for electrode presence is required. We proposed an image fusion method in SEEG that incorporates electrode segmentation from computed tomography as a sampling mask during registration to address the fusion problem in SEEG. The method was validated using eight image pairs from the Retrospective Image Registration Evaluation Project (RIRE). After establishing a reference registration for the MRI and identifying eight points, we assessed the method’s efficacy by comparing the Euclidean distances between these reference points and those derived using registration with a sampling mask. The results showed that the proposed method yielded a similar average error to the registration without a sampling mask, but reduced the dispersion of the error, with a standard deviation of 0.86 when a mask was used and 5.25 when no mask was used. MDPI 2023-11-09 /pmc/articles/PMC10670384/ /pubmed/37998556 http://dx.doi.org/10.3390/diagnostics13223420 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pérez Hinestroza, Jaime Mazo, Claudia Trujillo, Maria Herrera, Alejandro MRI and CT Fusion in Stereotactic Electroencephalography (SEEG) |
title | MRI and CT Fusion in Stereotactic Electroencephalography (SEEG) |
title_full | MRI and CT Fusion in Stereotactic Electroencephalography (SEEG) |
title_fullStr | MRI and CT Fusion in Stereotactic Electroencephalography (SEEG) |
title_full_unstemmed | MRI and CT Fusion in Stereotactic Electroencephalography (SEEG) |
title_short | MRI and CT Fusion in Stereotactic Electroencephalography (SEEG) |
title_sort | mri and ct fusion in stereotactic electroencephalography (seeg) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670384/ https://www.ncbi.nlm.nih.gov/pubmed/37998556 http://dx.doi.org/10.3390/diagnostics13223420 |
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