Post-implantation 3-T MRI in patients with an epicranial stimulation device: image quality and deep learning-based electrode reconstruction

We evaluated structural and functional MRI at 3 T in five patients with an implanted EASEE focal cortex stimulation system and assessed MRI-only electrode reconstruction. Image quality was evaluated visually and by comparing implant-centered and control regions using structural GM/WM signal ratios, GE-EPI signal intensity, T2*, and relative off-resonance. A deep learning pipeline trained exclusively on synthetic data estimated electrode position from T1-weighted MPRAGE images. Structural and functional acquisitions were completed in all patients without scan-related adverse events. Structural images showed clear intracranial anatomy beneath the implant without visually apparent local signal loss or distortion. GE-EPI reference images and derived maps showed no consistent local disturbance corresponding to the electrode. GE-EPI signal was numerically lower in implant-centered regions, but no imaging metric differed significantly between regions (all p > 0.05). Electrode reconstructions were anatomically plausible in all patients, with patient-specific median central electrode-to-cortical gray-matter distances ranging from 9 to 20 mm. These findings demonstrate the feasibility of post-implantation structural and functional MRI and MRI-only estimation of electrode-cortex geometry, although localization accuracy remains unvalidated. This combination provides a basis for future studies integrating patient-specific electrode geometry, electric-field modeling, and functional connectivity analyses in focal cortex stimulation with the EASEE system.

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Publication Details

Journal
medRxiv
Published
2026-10-28
DOI
https://doi.org/10.64898/2026.10.27.26366576
Primary Topic
Advanced MRI Techniques and Applications
Type
preprint

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preprint

Post-implantation 3-T MRI in patients with an epicranial stimulation device: image quality and deep learning-based electrode reconstruction

Vera Dinkelacker, Antonia Barghoorn, Niels Schwaderlapp, Eva Martinez‐Lizana et al.
medRxiv
Advanced MRI Techniques and Applications
preprint

Post-implantation 3-T MRI in patients with an epicranial stimulation device: image quality and deep learning-based electrode reconstruction

Vera Dinkelacker, Antonia Barghoorn, Niels Schwaderlapp, Eva Martinez‐Lizana, Nicolas Zabler, Maxim Zaitsev, Matthias Duempelmann, Andreas Schulze‐Bonhage, Sotirios Kalousios, Bernhard Steinhoff
preprint en

Abstract

We evaluated structural and functional MRI at 3 T in five patients with an implanted EASEE focal cortex stimulation system and assessed MRI-only electrode reconstruction. Image quality was evaluated visually and by comparing implant-centered and control regions using structural GM/WM signal ratios, GE-EPI signal intensity, T2*, and relative off-resonance. A deep learning pipeline trained exclusively on synthetic data estimated electrode position from T1-weighted MPRAGE images. Structural and functional acquisitions were completed in all patients without scan-related adverse events. Structural images showed clear intracranial anatomy beneath the implant without visually apparent local signal loss or distortion. GE-EPI reference images and derived maps showed no consistent local disturbance corresponding to the electrode. GE-EPI signal was numerically lower in implant-centered regions, but no imaging metric differed significantly between regions (all p > 0.05). Electrode reconstructions were anatomically plausible in all patients, with patient-specific median central electrode-to-cortical gray-matter distances ranging from 9 to 20 mm. These findings demonstrate the feasibility of post-implantation structural and functional MRI and MRI-only estimation of electrode-cortex geometry, although localization accuracy remains unvalidated. This combination provides a basis for future studies integrating patient-specific electrode geometry, electric-field modeling, and functional connectivity analyses in focal cortex stimulation with the EASEE system.

medRxiv
University of Freiburg (DE), University Medical Center Freiburg (DE), Hôpitaux Universitaires de Strasbourg (FR)
European Commission, Interreg
Good health and well-being
Advanced MRI Techniques and Applications
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