Gu Wenhao, Opfermann Justin D, Knopf Jonathan, Krieger Axel, Unberath Mathias
Johns Hopkins University, Baltimore, MD, USA.
Arthrex Inc., 1 Arthrex Way, Naples, FL, USA.
Int J Comput Assist Radiol Surg. 2025 May 1. doi: 10.1007/s11548-025-03379-0.
Mixed reality for surgical navigation is an emerging tool for precision surgery. Achieving reliable surgical guidance hinges on robust tracking of the mixed reality device relative to patient anatomy. Contemporary approaches either introduce bulky fiducials that need to be invasively attached to the anatomy or make strong assumptions about the patient remaining stationary.
We present an approach to anatomy tracking that relies on biocompatible near-infrared fluorescent (NIRF) dots. Dots are quickly placed on the anatomy intra-operatively and the pose is tracked reliably via PnP-type methods. We demonstrate the potential of our NIRF dots approach to track patient movements after image registration on a 3D printed model, simulating an image-guided navigation process with a tablet-based mixed reality scenario.
The dot-based pose tracking demonstrated an average accuracy of 1.13 mm in translation and 0.69 degrees in rotation under static conditions, and 1.39 mm/1.10 degrees, respectively, under dynamic conditions.
Our results are promising and encourage further research in the viability of integrating NIRF dots in mixed reality surgical navigation. These biocompatible dots may allow for reliable tracking of patient motion post-registration, providing a convenient alternative to invasive marker arrays. While our initial tests used a tablet, adaptation to head-mounted displays is plausible with suitable sensors.
用于手术导航的混合现实是一种新兴的精准手术工具。要实现可靠的手术引导，关键在于混合现实设备相对于患者解剖结构的稳健跟踪。当代方法要么引入需要侵入性附着在解剖结构上的笨重基准标记，要么对患者保持静止做出很强的假设。
我们提出一种基于生物相容性近红外荧光（NIRF）点的解剖结构跟踪方法。在手术中快速将点放置在解剖结构上，并通过类似PnP的方法可靠地跟踪其位姿。我们在一个3D打印模型上展示了我们的NIRF点方法在图像配准后跟踪患者运动的潜力，模拟了基于平板电脑的混合现实场景下的图像引导导航过程。
基于点的位姿跟踪在静态条件下平移平均精度为1.13毫米，旋转平均精度为0.69度，在动态条件下分别为1.39毫米/1.10度。
我们的结果很有前景，并鼓励进一步研究将NIRF点集成到混合现实手术导航中的可行性。这些生物相容性点可以在配准后可靠地跟踪患者运动，为侵入性标记阵列提供了一种方便的替代方案。虽然我们最初的测试使用了平板电脑，但通过合适的传感器，适配头戴式显示器是可行的。
