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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/22/3420 |
| _version_ | 1797459618261106688 |
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| author | Jaime Pérez Hinestroza Claudia Mazo Maria Trujillo Alejandro Herrera |
| author_facet | Jaime Pérez Hinestroza Claudia Mazo Maria Trujillo Alejandro Herrera |
| author_sort | Jaime Pérez Hinestroza |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T16:53:57Z |
| format | Article |
| id | doaj.art-91782157ce5d420f8bad48d22240b36b |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T16:53:57Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-91782157ce5d420f8bad48d22240b36b2023-11-24T14:37:32ZengMDPI AGDiagnostics2075-44182023-11-011322342010.3390/diagnostics13223420MRI and CT Fusion in Stereotactic Electroencephalography (SEEG)Jaime Pérez Hinestroza0Claudia Mazo1Maria Trujillo2Alejandro Herrera3Multimedia and Computer Vision Group, Universidad del Valle, Cali 760042, ColombiaMultimedia and Computer Vision Group, Universidad del Valle, Cali 760042, ColombiaMultimedia and Computer Vision Group, Universidad del Valle, Cali 760042, ColombiaMultimedia and Computer Vision Group, Universidad del Valle, Cali 760042, ColombiaEpilepsy 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.https://www.mdpi.com/2075-4418/13/22/3420image fusionstereotactic electroencephalographycomputer tomographymagnetic resonance imagingimage registration |
| spellingShingle | Jaime Pérez Hinestroza Claudia Mazo Maria Trujillo Alejandro Herrera MRI and CT Fusion in Stereotactic Electroencephalography (SEEG) Diagnostics image fusion stereotactic electroencephalography computer tomography magnetic resonance imaging image registration |
| 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 | image fusion stereotactic electroencephalography computer tomography magnetic resonance imaging image registration |
| url | https://www.mdpi.com/2075-4418/13/22/3420 |
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