Abstract:
Background: Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post- implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations. New methods: We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes’ CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints. Results: We tested GridFit on ~6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with <0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance. Comparison with existing methods: GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.
Authors:
Alejandro Omar Blenkmann
Sabine Liliana Leske
Anaïs Llorens
Jack J. Lin
Edward F. Chang
Peter Brunner
Gerwin Schalk
Jugoslav Ivanovic
Pål Gunnar Larsson
Robert Thomas Knight
Tor Endestad
Anne-Kristin Solbakk
Date: 2024
DOI: https://doi.org/10.1016/j.jneumeth.2024.110056