Robertson Faith C, Sha Raahil M, Amich Jose M, Essayed Walid Ibn, Lal Avinash, Lee Benjamin H, Calvachi Prieto Paola, Tokuda Junichi, Weaver James C, Kirollos Ramez W, Chen Min Wei, Gormley William B
1Department of Neurosurgery, Massachusetts General Hospital, Boston.
2Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Boston.
J Neurosurg. 2021 Oct 15;136(5):1475-1484. doi: 10.3171/2021.5.JNS211033. Print 2022 May 1.
A major obstacle to improving bedside neurosurgical procedure safety and accuracy with image guidance technologies is the lack of a rapidly deployable, real-time registration and tracking system for a moving patient. This deficiency explains the persistence of freehand placement of external ventricular drains, which has an inherent risk of inaccurate positioning, multiple passes, tract hemorrhage, and injury to adjacent brain parenchyma. Here, the authors introduce and validate a novel image registration and real-time tracking system for frameless stereotactic neuronavigation and catheter placement in the nonimmobilized patient.
Computer vision technology was used to develop an algorithm that performed near-continuous, automatic, and marker-less image registration. The program fuses a subject's preprocedure CT scans to live 3D camera images (Snap-Surface), and patient movement is incorporated by artificial intelligence-driven recalibration (Real-Track). The surface registration error (SRE) and target registration error (TRE) were calculated for 5 cadaveric heads that underwent serial movements (fast and slow velocity roll, pitch, and yaw motions) and several test conditions, such as surgical draping with limited anatomical exposure and differential subject lighting. Six catheters were placed in each cadaveric head (30 total placements) with a simulated sterile technique. Postprocedure CT scans allowed comparison of planned and actual catheter positions for user error calculation.
Registration was successful for all 5 cadaveric specimens, with an overall mean (± standard deviation) SRE of 0.429 ± 0.108 mm for the catheter placements. Accuracy of TRE was maintained under 1.2 mm throughout specimen movements of low and high velocities of roll, pitch, and yaw, with the slowest recalibration time of 0.23 seconds. There were no statistically significant differences in SRE when the specimens were draped or fully undraped (p = 0.336). Performing registration in a bright versus a dimly lit environment had no statistically significant effect on SRE (p = 0.742 and 0.859, respectively). For the catheter placements, mean TRE was 0.862 ± 0.322 mm and mean user error (difference between target and actual catheter tip) was 1.674 ± 1.195 mm.
This computer vision-based registration system provided real-time tracking of cadaveric heads with a recalibration time of less than one-quarter of a second with submillimetric accuracy and enabled catheter placements with millimetric accuracy. Using this approach to guide bedside ventriculostomy could reduce complications, improve safety, and be extrapolated to other frameless stereotactic applications in awake, nonimmobilized patients.
利用图像引导技术提高床边神经外科手术安全性和准确性的一个主要障碍是缺乏一种可快速部署的、用于移动患者的实时配准和跟踪系统。这一缺陷解释了为什么一直采用徒手放置外置脑室引流管的方法，该方法存在定位不准确、多次穿刺、通道出血以及损伤邻近脑实质的固有风险。在此，作者介绍并验证了一种用于无框架立体定向神经导航和非固定患者导管置入的新型图像配准和实时跟踪系统。
使用计算机视觉技术开发一种算法，该算法可进行近乎连续、自动且无标记的图像配准。该程序将受试者术前CT扫描图像与实时3D相机图像（Snap-Surface）融合，并通过人工智能驱动的重新校准（Real-Track）纳入患者的运动。对5个尸体头部进行了系列运动（快速和慢速的翻滚、俯仰和偏航运动）以及几种测试条件（如有限解剖暴露的手术铺巾和不同的受试者照明）下的表面配准误差（SRE）和目标配准误差（TRE）进行了计算。采用模拟无菌技术在每个尸体头部放置6根导管（共30次放置）。术后CT扫描允许比较计划的和实际的导管位置以计算使用者误差。
所有5个尸体标本的配准均成功，导管放置的总体平均（±标准差）SRE为0.429±0.108mm。在整个标本的低速和高速翻滚、俯仰和偏航运动过程中，TRE的准确性保持在1.2mm以下，最慢的重新校准时间为0.23秒。当标本覆盖或完全未覆盖时，SRE无统计学显著差异（p = 0.336）。在明亮与昏暗环境中进行配准对SRE无统计学显著影响（分别为p = 0.742和0.859）。对于导管放置，平均TRE为0.862±0.322mm，平均使用者误差（目标与实际导管尖端之间的差异）为1.674±1.195mm。
这种基于计算机视觉的配准系统能够以小于四分之一秒的重新校准时间对尸体头部进行实时跟踪，精度达到亚毫米级，并能够以毫米级精度进行导管放置。使用这种方法指导床边脑室造瘘术可减少并发症、提高安全性，并可推广到清醒、非固定患者的其他无框架立体定向应用中。
