Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.
Department of Neurology, University of Michigan, Ann Arbor, MI, United States of America.
J Neural Eng. 2023 Feb 27;20(1). doi: 10.1088/1741-2552/acbb2b.
. Suboptimal electrode placement during subthalamic nucleus deep brain stimulation (STN DBS) surgery may arise from several sources, including frame-based targeting errors and intraoperative brain shift. We present a computer algorithm that can accurately localize intraoperative microelectrode recording (MER) tracks on preoperative magnetic resonance imaging (MRI) in real-time, thereby predicting deviation between the surgical plan and the MER trajectories.. Random forest (RF) modeling was used to derive a statistical relationship between electrophysiological features on intraoperative MER and voxel intensity on preoperative T2-weighted MR imaging. This model was integrated into a larger algorithm that can automatically localize intraoperative MER recording tracks on preoperative MRI in real-time. To verify accuracy, targeting error of both the planned intraoperative trajectory ('planned') and the algorithm-derived trajectory ('calculated') was estimated by measuring deviation from the final DBS lead location on postoperative high-resolution computed tomography ('actual').. MR imaging and MERs were obtained from 24 STN DBS implant trajectories. The cross-validated RF model could accurately distinguish between gray and white matter regions along MER trajectories (AUC 0.84). When applying this model within the localization algorithm, theMER trajectory estimate was found to be significantly closer to theDBS lead when compared to thetrajectory recorded during surgery (1.04 mm vs 1.52 mm deviation,< 0.002), with improvement shown in 19/24 cases (79%). When applying the algorithm to simulated DBS trajectory plans with randomized targeting error, up to 4 mm of error could be resolved to <2 mm on average (< 0.0001).. This work presents an automated system for intraoperative localization of electrodes during STN DBS surgery. This neuroengineering solution may enhance the accuracy of electrode position estimation, particularly in cases where high-resolution intraoperative imaging is not available.
. 在丘脑底核深部脑刺激 (STN DBS) 手术中,电极的位置不理想可能有几个原因,包括基于框架的靶向误差和术中脑移位。我们提出了一种计算机算法,可以实时准确地将术中微电极记录 (MER) 轨迹定位到术前磁共振成像 (MRI),从而预测手术计划和 MER 轨迹之间的偏差。使用随机森林 (RF) 建模来推导出术中 MER 上的电生理特征与术前 T2 加权 MRI 上的体素强度之间的统计关系。该模型被整合到一个更大的算法中,可以实时自动定位术中 MER 记录轨迹在术前 MRI 上。为了验证准确性,通过测量术后高分辨率计算机断层扫描 (CT) 上最终 DBS 导联位置的偏差来估计计划中的手术轨迹(“计划”)和算法衍生的轨迹(“计算”)的目标误差。获得了 24 条 STN DBS 植入轨迹的 MRI 和 MER。交叉验证的 RF 模型可以准确地区分 MER 轨迹上的灰质和白质区域(AUC 为 0.84)。当在定位算法中应用该模型时,与手术过程中记录的轨迹相比,MER 轨迹的估计值更接近 DBS 导联(偏差为 1.04 毫米对 1.52 毫米,<0.002),24 例中有 19 例得到改善(79%)。当将该算法应用于具有随机靶向误差的模拟 DBS 轨迹计划时,高达 4 毫米的误差可以平均减少到 <2 毫米(<0.0001)。这项工作提出了一种用于 STN DBS 手术中电极术中定位的自动化系统。这个神经工程解决方案可能会提高电极位置估计的准确性,特别是在无法获得高分辨率术中成像的情况下。