基于工具驱动与残差动力学的观测参考组织变形预测
Observation-Referenced Tissue Deformation Forecasting with Tool-Driven and Residual Dynamics
AI总结：
本研究提出观测参考的组织变形预测方法，结合工具驱动与残差动力学，在12个手术片段上验证其误差比中位数为0.598，可作为手术相关模型的实用构建模块。
摘要：
从未来器械运动预测组织变形可为预测规划及日益自主的手术系统提供支持。本研究针对该问题，采用适配于观测到的器械-组织交互窗口的场景特定可微曲面模型。与预测绝对模拟位置不同，我们提出一种“观测参考”公式，将每个组织点锚定到其最后一次有效观测，仅添加由已知预规划的未来器械轨迹诱导的变形，这降低了对重建、初始化及持续模拟偏移的敏感性，同时保留了预测的变形响应。该物理模型采用无网格、基于位置的可微动力学，结合场景特定的刚度、附着性、区域尺度及抓取耦合特性。我们进一步引入可选的“观测参考残差模型”，将图谱空间模式与周期谐动力学相结合，用于处理器械输入未解释的结构化运动。我们在来自三个数据集的12个手术操作片段上对该框架进行回顾性评估。观测参考带来的增益最大，相对于对应的绝对模拟预测，其片段内误差比的中位数为0.598。当剩余运动呈现周期结构时，可选的周期残差进一步提升了预测效果。这些结果表明，观测参考变形预测可作为未来手术世界模型、数字孪生及生成式模拟器的实用构建模块，这类模型需预测组织在器械作用下的演化情况。
英文摘要：
Forecasting tissue deformation from future instrument motion could support predictive planning and increasingly autonomous surgical systems. We study this problem using a scene-specific differentiable surface model fitted to an observed tool-tissue interaction window. Rather than forecasting absolute simulated positions, we introduce an \textit{observation-referenced} formulation that anchors each tissue point to its last valid observation and adds only the deformation induced by the known, preplanned future tool trajectory. This reduces sensitivity to reconstruction, initialization, and persistent simulation offsets while preserving the predicted deformation response. The physical model uses differentiable, mesh-free position-based dynamics with scene-specific rigidity, attachment, region scale, and grasp coupling. We further introduce an optional \textit{observation-referenced residual model} combining graph-spectral spatial modes with periodic harmonic dynamics for structured motion not explained by the tool input. We evaluate the framework retrospectively on 12 surgical manipulation clips from three datasets. Observation referencing provides the largest gain, with a median within-clip error ratio of $0.598$ relative to the corresponding absolute simulation forecast. The optional periodic residual further improves forecasting when the remaining motion exhibits periodic structure. These results support observation-referenced deformation prediction as a practical building block for future surgical world models, digital twins, and generative simulators that must predict how tissue evolves under instrument actions.
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