Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Laboratory for Orthopaedic Biomechanics, ETH Zurich, Zurich, Switzerland.
Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Laboratory for Orthopaedic Biomechanics, ETH Zurich, Zurich, Switzerland.
Med Image Anal. 2022 Apr;77:102365. doi: 10.1016/j.media.2022.102365. Epub 2022 Jan 22.
The instrumentation of spinal fusion surgeries includes pedicle screw placement and rod implantation. While several surgical navigation approaches have been proposed for pedicle screw placement, less attention has been devoted towards the guidance of patient-specific adaptation of the rod implant. We propose a marker-free and intuitive Augmented Reality (AR) approach to navigate the bending process required for rod implantation. A stereo neural network is trained from the stereo video streams of the Microsoft HoloLens in an end-to-end fashion to determine the location of corresponding pedicle screw heads. From the digitized screw head positions, the optimal rod shape is calculated, translated into a set of bending parameters, and used for guiding the surgeon with a novel navigation approach. In the AR-based navigation, the surgeon is guided step-by-step in the use of the surgical tools to achieve an optimal result. We have evaluated the performance of our method on human cadavers against two benchmark methods, namely conventional freehand bending and marker-based bending navigation in terms of bending time and rebending maneuvers. We achieved an average bending time of 231s with 0.6 rebending maneuvers per rod compared to 476s (3.5 rebendings) and 348s (1.1 rebendings) obtained by our freehand and marker-based benchmarks, respectively.
脊柱融合手术的器械包括椎弓根螺钉放置和棒植入。虽然已经提出了几种用于椎弓根螺钉放置的手术导航方法,但对于棒植入的患者特定适应性引导的关注较少。我们提出了一种无标记且直观的增强现实 (AR) 方法来导航棒植入所需的弯曲过程。一个立体神经网络从 Microsoft HoloLens 的立体视频流中以端到端的方式进行训练,以确定相应椎弓根螺钉头的位置。从数字化的螺钉头位置计算出最佳的棒形,将其转换为一组弯曲参数,并用于通过一种新的导航方法指导外科医生。在基于 AR 的导航中,外科医生会逐步指导使用手术工具以达到最佳效果。我们针对两种基准方法(即常规徒手弯曲和基于标记的弯曲导航),在人类尸体上评估了我们的方法的性能,评估指标为弯曲时间和重新弯曲操作的次数。与我们的徒手和基于标记的基准相比,我们的平均弯曲时间为 231 秒,每根棒有 0.6 次重新弯曲操作,而分别为 476 秒(3.5 次重新弯曲)和 348 秒(1.1 次重新弯曲)。