Article ; Online: Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods.
Journal of neuroscience methods
2024 Volume 404, Page(s) 110056
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/ ... ...
| 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. Conclusion: GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox. |
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| MeSH term(s) | Humans ; Electroencephalography/methods ; Electrodes, Implanted ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Cerebral Cortex/diagnostic imaging ; Electrodes |
| Language | English |
| Publishing date | 2024-01-14 |
| Publishing country | Netherlands |
| Document type | Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 282721-9 |
| ISSN | 1872-678X ; 0165-0270 |
| ISSN (online) | 1872-678X |
| ISSN | 0165-0270 |
| DOI | 10.1016/j.jneumeth.2024.110056 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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