Kögl Fryderyk Victor, Léger Étienne, Haouchine Nazim, Torio Erickson, Juvekar Parikshit, Navab Nassir, Kapur Tina, Pieper Steve, Golby Alexandra, Frisken Sarah
Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
Computer Aided Medical Procedures, Technische Universität München, Munich, Germany.
Comput Methods Biomech Biomed Eng Imaging Vis. 2023;11(4):1307-1315. doi: 10.1080/21681163.2022.2163428. Epub 2022 Dec 28.
This work presents a novel tool-free neuronavigation method that can be used with a single RGB commodity camera. Compared with freehand craniotomy placement methods, the proposed system is more intuitive and less error prone. The proposed method also has several advantages over standard neuronavigation platforms. First, it has a much lower cost, since it doesn't require the use of an optical tracking camera or electromagnetic field generator, which are typically the most expensive parts of a neuronavigation system, making it much more accessible. Second, it requires minimal setup, meaning that it can be performed at the bedside and in circumstances where using a standard neuronavigation system is impractical. Our system relies on machine-learning-based hand pose estimation that acts as a proxy for optical tool tracking, enabling a 3D-3D pre-operative to intra-operative registration. Qualitative assessment from clinical users showed that the concept is clinically relevant. Quantitative assessment showed that on average a target registration error (TRE) of 1.3cm can be achieved. Furthermore, the system is framework-agnostic, meaning that future improvements to hand-tracking frameworks would directly translate to a higher accuracy.
这项工作提出了一种新颖的无需工具的神经导航方法，该方法可与单个RGB商用相机配合使用。与徒手开颅手术放置方法相比，所提出的系统更直观且出错概率更低。所提出的方法相对于标准神经导航平台也具有几个优点。首先，其成本要低得多，因为它不需要使用光学跟踪相机或电磁场发生器，而这通常是神经导航系统中最昂贵的部件，从而使其更容易获得。其次，它所需的设置最少，这意味着它可以在床边以及使用标准神经导航系统不切实际的情况下进行。我们的系统依赖于基于机器学习的手部姿态估计，该估计可作为光学工具跟踪的代理，实现术前到术中的3D-3D配准。临床用户的定性评估表明该概念具有临床相关性。定量评估表明，平均可实现1.3厘米的目标配准误差（TRE）。此外，该系统与框架无关，这意味着未来对手部跟踪框架的改进将直接转化为更高的精度。
