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

Benjamin F Zwick; Saima Safdar; George C Bourantas; Grand R Joldes; Damon E Hyde; Simon K Warfield; Adam Wittek; Karol Miller

doi:10.1016/j.dib.2023.109122

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

Data Brief. 2023 Apr 7:48:109122. doi: 10.1016/j.dib.2023.109122. eCollection 2023 Jun.

Authors

Benjamin F Zwick¹, Saima Safdar¹, George C Bourantas¹, Grand R Joldes¹, Damon E Hyde^{2

3}, Simon K Warfield^{2

3}, Adam Wittek¹, Karol Miller^{1

3}

Affiliations

¹ Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia.
² Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA.
³ Harvard Medical School, Boston, MA, USA.

Abstract

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.

Keywords: Biomechanics; Diffusion tensor imaging (DTI); Electrical source imaging (ESI); Electrocorticography (ECoG); Electroencephalography (EEG); Epilepsy; Finite element method (FEM); Meshless methods (MM).

Grants and funding

P50 HD105351/HD/NICHD NIH HHS/United States