ActiveReg：用于部分到完整骨骼配准的信息驱动型主动区域探测方法
ActiveReg: Information-Driven Active Regional Probing for Partial-to-Full Bone Registration
- School of Informatics, The University of Edinburgh（爱丁堡大学信息学院）
- Concord Repatriation General Hospital（康科德复员总医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ActiveReg是一种闭环主动区域探测框架，通过D最优规划器和在线评估减少骨骼配准所需采集点，在模拟和体模实验中，其精度与基线方法相当，采集点数量大幅减少。
AI中文摘要：
精准的骨骼配准对于骨科手术导航和机器人辅助至关重要。在常规流程中，外科医生需要在暴露的骨表面识别并探测多个指定位置，以获取配准所需的数据点，但在手术暴露有限的情况下，这一过程难度大且耗时。减少所需探测点数量并提供清晰的探测指引，可简化术中外科医生的数据采集任务。本文提出ActiveReg，这是一种闭环框架，可推荐探测区域而非单个点，允许在确切接触位置上具有灵活性。考虑已采集点和当前配准信息不确定性的D最优规划器，有助于用更少的点实现精准配准。在线评估结合了预更新创新一致性与预定义手术任务位置的不确定性，提升了完成决策的可靠性，无需真实值。在四种解剖场景下的模拟显示，ActiveReg与基线配准方法相比可达到相当的精度，同时使用的采集点数量显著更少。采用光学和电磁跟踪的真实体模实验表明，其平均目标配准误差与Gradient-SDF框架相当，仅使用24至28个采集点，而Gradient-SDF框架需使用771至1263个采集点。
英文摘要：
Accurate bone registration is essential for orthopedic surgical navigation and robotic assistance. In a common workflow, the surgeon needs to identify and probe a number of prescribed locations on the exposed bone surface to obtain data points for registration, which can be difficult and time-consuming under limited surgical exposure. Reducing the number of required points while providing clear probing guidance would ease the surgeon's acquisition task intra-operatively. We present ActiveReg, a closed-loop framework that recommends probing regions rather than individual points, allowing flexibility in the exact contact location. A D-optimal planner that takes into account the already acquired points and the uncertainty of current registration information helps achieve accurate registration with fewer points. An online assessment combines pre-update innovation consistency with uncertainty at predefined surgical task locations improves the reliability of completion decision, without requiring ground truth. Simulations across four anatomical scenarios demonstrated that ActiveReg can achieve competitive accuracy compared with baseline registration methods while using substantially fewer acquired points. Real phantom experiments with optical and electromagnetic tracking demonstrated comparable mean target registration error to the Gradient-SDF framework, using only 24-28 acquired points instead of 771-1263.
