无标记头部追踪用于精确且可及的神经导航
Markerless Head Tracking for Accurate and Accessible Neuronavigation
AI总结：
本文提出无标记方法，利用低成本摄像头和算法建模实现高精度神经导航，实验显示其在精度和成本效益上的优势。
摘要：
神经导航广泛应用于生物医学研究和干预，用于指导头周围仪器的精确放置以支持如经颅磁刺激等程序。然而，传统系统依赖于佩戴在受试者身上的标记，需要手动注册，且在程序中可能移位并引起不适。我们介绍了并评估了无标记方法，用低成本的可见和红外摄像头结合立体和深度感应，以及面部几何的算法建模来替代昂贵的硬件和物理标记。对50名人类受试者的验证显示，最佳无标记算法的跟踪偏差中位数仅为2.32毫米和2.01度，相较于传统标记系统，表明足够精确用于经颅磁刺激，并显著优于先前的无标记结果。该研究还表明，整合各种摄像头传感器的数据可以进一步提高整体精度。所提出的无标记神经导航方法可以降低设置成本和复杂性，提高患者舒适度，并在临床和研究环境中扩大神经导航的可及性。
英文摘要：
Neuronavigation is widely used in biomedical research and interventions to guide the precise placement of instruments around the head to support procedures such as transcranial magnetic stimulation. Traditional systems, however, rely on subject-mounted markers that require manual registration, may shift during procedures, and can cause discomfort. We introduce and evaluate markerless approaches that replace expensive hardware and physical markers with low-cost visible and infrared light cameras incorporating stereo and depth sensing, combined with algorithmic modeling of the facial geometry. Validation with 50 human subjects yielded a median tracking discrepancy of only 2.32 mm and 2.01$^\circ$ for the best markerless algorithm compared to a conventional marker-based system, which indicates sufficient accuracy for transcranial magnetic stimulation and a substantial improvement over prior markerless results. The study also suggests that integration of the data from the various camera sensors can improve the overall accuracy further. The proposed markerless neuronavigation methods can reduce setup cost and complexity, improve patient comfort, and expand access to neuronavigation in clinical and research settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
