发表机构
McGill University; Montreal Neurological Institute, McGill University（麦吉尔大学; 麦吉尔大学蒙特利尔神经研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
LARK利用多相机RGB硬件与卡尔曼滤波融合，实现低成本、抗遮挡的高精度手术器械跟踪，在五相机配置下达到亚毫米级配准误差。
AI 中文摘要
图像引导手术（IGS）依赖于对手术器械的精确跟踪，以提供相对于解剖结构的实时导航。商用立体红外跟踪器精度高，但容易受到遮挡的影响，且在许多场景下成本过高。本研究提出了LARK，一种使用商用RGB硬件和多视角冗余与融合的多相机光学跟踪系统。我们开发并评估了两种完整的跟踪方法：多视角单目位姿融合和多视角三角测量。两种方法都在不同遮挡水平下使用精密加工的网格和头部解剖模型进行评估，并与金标准立体红外系统进行比较。使用五个相机和自适应卡尔曼滤波，LARK在精密加工网格上实现了点定位（三角测量）的中位目标配准误差为0.64毫米，轨迹跟踪（位姿融合）的误差为0.73毫米。相机子集实验表明，随着可用视角减少，自适应位姿融合精度会优雅地退化。跟踪硬件成本低于1000美元，LARK为图像引导手术研究提供了一个低成本平台。硬件设计和软件可在https://this URL公开获取，数据集可在https://this URL获取。
英文摘要
Image-guided surgery (IGS) depends on accurate tracking of surgical instruments to provide real-time navigation relative to anatomical structures. Commercial stereo infrared trackers are accurate but prone to occlusion and cost-prohibitive for many settings. This work presents LARK, a multi-camera optical tracking system using commodity RGB hardware and multi-view redundancy and fusion. We develop and evaluate two complete tracking methods: multi-view monocular pose fusion and multi-view triangulation. Both methods are assessed under varying occlusion levels using a precision-machined grid and an anatomical head phantom, and compared against a gold-standard stereo infrared system. With five cameras and adaptive Kalman filtering, LARK achieves median target registration errors of 0.64 mm for point localization with triangulation and 0.73 mm for trajectory tracking with pose fusion on the machined grid. Camera-subset experiments show graceful degradation in adaptive pose-fusion accuracy as fewer views remain available. With tracking hardware costing under $1,000 USD, LARK provides a low-cost platform for image-guided surgery research. Hardware designs and software are publicly available at https://nist.mni.mcgill.ca/software/ , and datasets at https://nist.mni.mcgill.ca/data/ .
Comments24 pages, 15 figures, including appendices. Supplementary document included as an ancillary file. Supplementary video: https://youtu.be/ApZ8q9DjB-4
