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Image data and computational grids for computing brain shift and solving the electrocorticography forward problem

This article describes the dataset applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain” [1] that is available for download from the Zenodo data repository (https://zenodo.org/record/7687631) [2]. Preoperativ...

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Autores principales: Zwick, Benjamin F., Safdar, Saima, Bourantas, George C., Joldes, Grand R., Hyde, Damon E., Warfield, Simon K., Wittek, Adam, Miller, Karol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147975/
https://www.ncbi.nlm.nih.gov/pubmed/37128587
http://dx.doi.org/10.1016/j.dib.2023.109122
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author Zwick, Benjamin F.
Safdar, Saima
Bourantas, George C.
Joldes, Grand R.
Hyde, Damon E.
Warfield, Simon K.
Wittek, Adam
Miller, Karol
author_facet Zwick, Benjamin F.
Safdar, Saima
Bourantas, George C.
Joldes, Grand R.
Hyde, Damon E.
Warfield, Simon K.
Wittek, Adam
Miller, Karol
author_sort Zwick, Benjamin F.
collection PubMed
description This article describes the dataset applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain” [1] that is available for download from the Zenodo data repository (https://zenodo.org/record/7687631) [2]. Preoperative structural and diffusion-weighted magnetic resonance (MR) and postoperative computed tomography (CT) images of a 12-year-old female epilepsy patient under evaluation for surgical intervention were obtained retrospectively from Boston Children's Hospital. We used these images to conduct the analysis at The University of Western Australia's Intelligent Systems for Medicine Laboratory using SlicerCBM [3], our open-source software extension for the 3D Slicer medical imaging platform. As part of the analysis, we processed the images to extract the patient-specific brain geometry; created computational grids, including a tetrahedral grid for the meshless solution of the biomechanical model and a regular hexahedral grid for the finite element solution of the electrocorticography forward problem; predicted the postoperative MRI and DTI that correspond to the brain configuration deformed by the placement of subdural electrodes using biomechanics-based image warping; and solved the patient-specific electrocorticography forward problem to compute the electric potential distribution within the patient's head using the original preoperative and predicted postoperative image data. The well-established and open-source file formats used in this dataset, including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry, and Visualization Toolkit (VTK) files for computational grids, allow other research groups to easily reuse the data presented herein to solve the electrocorticography forward problem accounting for the brain shift caused by implantation of subdural grid electrodes.
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spelling pubmed-101479752023-04-30 Image data and computational grids for computing brain shift and solving the electrocorticography forward problem Zwick, Benjamin F. Safdar, Saima Bourantas, George C. Joldes, Grand R. Hyde, Damon E. Warfield, Simon K. Wittek, Adam Miller, Karol Data Brief Data Article This article describes the dataset applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain” [1] that is available for download from the Zenodo data repository (https://zenodo.org/record/7687631) [2]. Preoperative structural and diffusion-weighted magnetic resonance (MR) and postoperative computed tomography (CT) images of a 12-year-old female epilepsy patient under evaluation for surgical intervention were obtained retrospectively from Boston Children's Hospital. We used these images to conduct the analysis at The University of Western Australia's Intelligent Systems for Medicine Laboratory using SlicerCBM [3], our open-source software extension for the 3D Slicer medical imaging platform. As part of the analysis, we processed the images to extract the patient-specific brain geometry; created computational grids, including a tetrahedral grid for the meshless solution of the biomechanical model and a regular hexahedral grid for the finite element solution of the electrocorticography forward problem; predicted the postoperative MRI and DTI that correspond to the brain configuration deformed by the placement of subdural electrodes using biomechanics-based image warping; and solved the patient-specific electrocorticography forward problem to compute the electric potential distribution within the patient's head using the original preoperative and predicted postoperative image data. The well-established and open-source file formats used in this dataset, including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry, and Visualization Toolkit (VTK) files for computational grids, allow other research groups to easily reuse the data presented herein to solve the electrocorticography forward problem accounting for the brain shift caused by implantation of subdural grid electrodes. Elsevier 2023-04-07 /pmc/articles/PMC10147975/ /pubmed/37128587 http://dx.doi.org/10.1016/j.dib.2023.109122 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Zwick, Benjamin F.
Safdar, Saima
Bourantas, George C.
Joldes, Grand R.
Hyde, Damon E.
Warfield, Simon K.
Wittek, Adam
Miller, Karol
Image data and computational grids for computing brain shift and solving the electrocorticography forward problem
title Image data and computational grids for computing brain shift and solving the electrocorticography forward problem
title_full Image data and computational grids for computing brain shift and solving the electrocorticography forward problem
title_fullStr Image data and computational grids for computing brain shift and solving the electrocorticography forward problem
title_full_unstemmed Image data and computational grids for computing brain shift and solving the electrocorticography forward problem
title_short Image data and computational grids for computing brain shift and solving the electrocorticography forward problem
title_sort image data and computational grids for computing brain shift and solving the electrocorticography forward problem
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147975/
https://www.ncbi.nlm.nih.gov/pubmed/37128587
http://dx.doi.org/10.1016/j.dib.2023.109122
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