背景:颅内电极通常来自植入后CT伪影。缺少定位低信噪比伪影和高密度电极阵列的自动算法。此外,网格/条的植入会导致大脑变形,导致融合植入后CT和植入前MR图像时的配准误差。脑移位补偿方法将电极坐标投影到皮层,但要么无法产生平滑的解决方案,要么无法解释大脑变形。
方法:我们首先介绍GridFit,一种基于模型的拟合方法,可同时将所有电极CT伪影定位在网格中,strips,或深度数组。第二,我们提出CEPA,结合基于正交的投影的脑移补偿算法,弹簧网格模型,和空间正则化约束。
结果:我们在6000个模拟场景上测试了GridFit。CT伪影的定位在困难的情况下表现出稳健的性能,比如噪音,重叠,和高密度植入物(<1mm误差)。来自20名具有挑战性的患者的数据的验证显示电极的99%准确定位(3160/3192)。我们用15名患者的数据测试了CEPA脑移位补偿。投影考虑或简单的机械变形原理,误差<0.4mm。电极间距离在相邻电极之间平滑地变化,而电极间距离的变化随投影距离线性增加。
方法:GridFit在困难的情况下成功地挑战了可用的方法,并通过保持电极间距离而优于视觉定位。CEPA的登记误差小于成熟替代品的登记误差。此外,5例患者静息状态高频活动建模进一步支持CEPA.
结论:GridFit和CEPA是记录颅内电极坐标的通用工具,即使在最具挑战性的植入场景中,也能提供高度准确的结果。这些方法在iElectrodes开源工具箱中实现。
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.
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.
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.
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.
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.