Augmented Reality (AR) has been used to facilitate surgical guidance during External Ventricular Drain (EVD) surgery, reducing the risks of misplacement in manual operations. During this procedure, the pivotal challenge is the accurate estimation of spatial relationship between pre-operative images and actual patient anatomy in AR environment. In this research, we propose a novel framework utilizing Time of Flight (ToF) depth sensors integrated in commercially available AR Head Mounted Devices (HMD) for precise EVD surgical guidance. As previous studies have proven depth errors for ToF sensors, we first conducted a comprehensive assessment for the properties of this error on AR-HMDs. Subsequently, a depth error model and patient-specific model parameter identification method, is introduced for accurate surface information. After that, a tracking procedure combining retro-reflective markers and point clouds is proposed for accurate head tracking, where head surface is reconstructed using ToF sensor data for spatial registration, avoiding fixing tracking targets rigidly on the patient's cranium. Firstly, $7.580\pm 1.488 mm$ ToF sensor depth value error was revealed on human skin, indicating the significance of depth correction. Our results showed that the ToF sensor depth error was reduced by over $85\%$ using proposed depth correction method on head phantoms in different materials. Meanwhile, the head surface reconstructed with corrected depth data achieved sub-millimeter accuracy. Experiment on a sheep head revealed $0.79 mm$ reconstruction error. Furthermore, a user study was conducted for the performance of proposed framework in simulated EVD surgery, where 5 surgeons performed 9 k-wire injections on a head phantom with virtual guidance. Results of this study revealed $2.09 \pm 0.16 mm$ translational accuracy and $2.97\pm 0.91 ^\circ$ orientational accuracy.

翻译：摘要：增强现实（AR）已被用于辅助脑室外引流（EVD）手术的术中引导，从而降低手动操作中导管误置的风险。在此过程中，关键挑战在于在AR环境中精确估计术前图像与实际患者解剖结构之间的空间关系。本研究提出了一种新型框架，利用商用AR头戴式设备（HMD）中集成的飞行时间（ToF）深度传感器，实现精准的EVD手术引导。鉴于已有研究证明ToF传感器存在深度误差，我们首先对AR-HMD上该误差的特性进行了全面评估。随后，提出了一种深度误差模型及患者特定模型参数辨识方法，用于获取准确的表面信息。在此基础上，提出了一种结合反向反射标记与点云的跟踪流程，实现精确的头部跟踪，其中利用ToF传感器数据重建头部表面以进行空间配准，从而避免将跟踪目标刚性固定于患者颅骨。实验发现，人体皮肤上的ToF传感器深度值误差为$7.580\pm 1.488 mm$，表明深度校正的重要性。结果表明，采用所提深度校正方法后，不同材质头部模型的ToF传感器深度误差降低超过85%。同时，利用校正后的深度数据重建的头部表面达到了亚毫米精度。在羊头模型上的实验显示重建误差为$0.79 mm$。此外，在模拟EVD手术中开展了一项用户研究，以评估所提框架的性能，其中5名外科医生在虚拟引导下向头部模型注射了9根克氏针。研究结果显示，平移精度为$2.09 \pm 0.16 mm$，角度精度为$2.97\pm 0.91 ^\circ$。