轴构型自适应的导管尖端位置估计：基于电机历史条件残差学习

中文摘要 AI 辅助

肌腱驱动的连续体操纵器广泛应用于医疗领域，其中准确的尖端位置估计对于精确导航和器械定位至关重要。然而，患者解剖结构和手术设置引入了依赖于任务的未知轴构型，而摩擦、松弛和柔顺性则导致迟滞现象，使得尖端估计具有挑战性。本文提出了一种基于电机历史条件的门控循环单元（GRU）残差估计器，用于三维导管尖端估计，无需直接感知轴构型。首先，采用初始多方向扫描策略来校准几何导管模型主干，并将电机角度和驱动扭矩响应编码为轴构型上下文向量。在后续运动过程中，该上下文条件化一个GRU，该GRU预测任务空间残差以校正该主干，仅依赖电机测量。上下文在当前轴构型下保持固定，而循环状态捕获不断演化的驱动历史。在四根一次性心内超声心动图导管和16种弯曲轴构型中，该方法实现了3.3毫米的开环尖端均方根误差（RMSE），相对于常曲率基线降低了59%。

英文摘要

Tendon-driven continuum manipulators are widely used in medical applications, where accurate tip-position estimation is essential for precise navigation and instrument positioning. However, patient anatomy and procedural setup impose task-dependent unknown shaft configurations, while friction, slack, and compliance introduce hysteresis, making tip estimation challenging. This paper presents a motor-history-conditioned gated recurrent unit (GRU) residual estimator for three-dimensional catheter tip estimation without direct shaft-configuration sensing. First, an initial multidirectional sweep strategy is applied to calibrate a geometric catheter model backbone, and encode the motor-angle and drive-torque response into a shaft-configuration context vector. During subsequent motion, the context conditions a GRU that predicts a task-space residual correcting this backbone, relying on motor measurements alone. The context remains fixed for the current shaft configuration, while the recurrent state captures the evolving actuation history. Across four disposable intra-cardiac echocardiography catheters and 16 bent shaft configurations, the method achieves 3.3mm open-loop tip RMSE, a 59% reduction relative to the constant-curvature baseline.

发表机构

• Siemens Healthineers（西门子医疗）
• University of California San Diego（加利福尼亚大学圣迭戈分校）

机构由 AI 辅助整理，请以论文原文为准。