03432nas a2200361 4500008004100000022001400041245014400055210006900199260001300268300001100281490000800292520223300300653001002533653002002543653001502563653002602578653002702604653001102631653003102642100003102673700002702704700002002731700001702751700002102768700001902789700001902808700002302827700002602850700002702876700001802903700002602921856012302947 2024 eng d a1872-678X00aAnatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods.0 aAnatomical registration of intracranial electrodes Robust modelb c2024 Apr a1100560 v4043 a

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.

10aBrain10aCerebral Cortex10aElectrodes10aElectrodes, Implanted10aElectroencephalography10aHumans10aMagnetic Resonance Imaging1 aBlenkmann, Alejandro, Omar1 aLeske, Sabine, Liliana1 aLlorens, Anaïs1 aLin, Jack, J1 aChang, Edward, F1 aBrunner, Peter1 aSchalk, Gerwin1 aIvanovic, Jugoslav1 aLarsson, Pål, Gunnar1 aKnight, Robert, Thomas1 aEndestad, Tor1 aSolbakk, Anne-Kristin uhttps://www.neurotechcenter.org/publications/2024/anatomical-registration-intracranial-electrodes-robust-model-based-0