Data

Data for patient-specific solution of the electrocorticography forward problem in deforming brain

The University of Western Australia
Zwick, Benjamin F. ; Safdar, Saima ; Bourantas, George C. ; Joldes, Grand ; Hyde, Damon E. ; Warfield, Simon K. ; Wittek, Adam ; Miller, Karol
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.5281/zenodo.7687631&rft.title=Data for patient-specific solution of the electrocorticography forward problem in deforming brain&rft.identifier=10.5281/zenodo.7687631&rft.publisher=Zenodo&rft.description=This dataset contains magnetic resonance (MR) and computed tomography (CT) images of a patient undergoing intracranial electrical monitoring using electrocorticography grid electrodes, together with patient-specific geometry and computational grids created from these images applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain”. The images were acquired at Boston Children’s Hospital and provided to The University of Western Australia’s Intelligent Systems for Medicine Laboratory for analysis. The analysis was conducted using our open-source SlicerCBM software extension for the 3D Slicer medical imaging platform. The analysis steps include image processing to obtain the patient-specific brain geometry, construction of computational grids (tetrahedral grid for meshless solution of biomechanical model and regular hexahedral grid for finite element solution of the electrocorticography forward problem), biomechanics-based image warping to predict the postoperative images corresponding to the brain configuration deformed by placement of subdural electrodes, and patient-specific solution of the electrocorticography forward problem to compute the electric potential distribution within the patient’s head. We use well-established open-source data file formats including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry and Visualization Toolkit (VTK) files for computational grids. This facilitates the re-use of this dataset in a range of studies that rely on medical image analysis, and computational biomechanics and electrostatics to solve the electrocorticography forward problem for electrical source imaging.&rft.creator=Zwick, Benjamin F. &rft.creator=Safdar, Saima &rft.creator=Bourantas, George C. &rft.creator=Joldes, Grand &rft.creator=Hyde, Damon E. &rft.creator=Warfield, Simon K. &rft.creator=Wittek, Adam &rft.creator=Miller, Karol &rft.date=2022&rft_subject=epilepsy&rft_subject=electroencephalography (EEG)&rft_subject=electrocorticography (ECoG)&rft_subject=meshless methods&rft_subject=finite element method (FEM)&rft_subject=biomechanics&rft_subject=diffusion tensor imaging (DTI)&rft_subject=brain&rft_subject=neuroimaging&rft.type=dataset&rft.language=English Access the data

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This dataset contains magnetic resonance (MR) and computed tomography (CT) images of a patient undergoing intracranial electrical monitoring using electrocorticography grid electrodes, together with patient-specific geometry and computational grids created from these images applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain”. The images were acquired at Boston Children’s Hospital and provided to The University of Western Australia’s Intelligent Systems for Medicine Laboratory for analysis. The analysis was conducted using our open-source SlicerCBM software extension for the 3D Slicer medical imaging platform. The analysis steps include image processing to obtain the patient-specific brain geometry, construction of computational grids (tetrahedral grid for meshless solution of biomechanical model and regular hexahedral grid for finite element solution of the electrocorticography forward problem), biomechanics-based image warping to predict the postoperative images corresponding to the brain configuration deformed by placement of subdural electrodes, and patient-specific solution of the electrocorticography forward problem to compute the electric potential distribution within the patient’s head. We use well-established open-source data file formats including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry and Visualization Toolkit (VTK) files for computational grids. This facilitates the re-use of this dataset in a range of studies that rely on medical image analysis, and computational biomechanics and electrostatics to solve the electrocorticography forward problem for electrical source imaging.

Notes

External Organisations
The University of Western Australia; Harvard Medical School
Associated Persons
Saima Safdar (Creator)George C. Bourantas (Creator); Damon E. Hyde (Creator); Simon K. Warfield (Creator)

Issued: 2022

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