基于CT影像的机器人精准耳蜗植入，实现接触力自适应控制。
CT-Enabled Patient-Specific Simulation and Contact-Aware Robotic Planning for Cochlear Implantation
- 构建可微分的弹性电极模型，支持摩擦接触与滑移连续过渡
- 通过CT重建患者耳蜗结构，实现高精度力反馈与插入深度控制
- 适合做精准医疗机器人、耳科手术辅助系统的研究者参考
机器人耳蜗植入需精确预测和调控接触力，以减少内耳损伤并防止卡滞和屈曲等失败模式。本文提出统一的从CT到仿真的流程，实现接触感知的插入规划与验证。我们构建了一种低维可微分的柯塞拉杆电极模型，结合摩擦接触与伪动力学正则化，确保滑移-粘附过程连续。利用CT影像重建患者特异性耳蜗解剖结构，并通过鼓室腔管路的解析参数化表示，实现高效的可微分最近点投影接触查询。基于可微分的平衡约束公式，推导出满足远程中心运动（RCM）约束的在线方向更新律，有效抑制横向插入力同时保持轴向推进。仿真与台架实验验证了变形与力趋势，显著降低卡滞/屈曲风险，提升插入深度。研究证明，基于CT的成像能增强建模、规划与手术安全性。
原文摘要 · Abstract (English)
Robotic cochlear-implant (CI) insertion requires precise prediction and regulation of contact forces to minimize intracochlear trauma and prevent failure modes such as locking and buckling. Aligned with the integration of advanced medical imaging and robotics for autonomous, precision interventions, this paper presents a unified CT-to-simulation pipeline for contact-aware insertion planning and validation. We develop a low-dimensional, differentiable Cosserat-rod model of the electrode array coupled with frictional contact and pseudo-dynamics regularization to ensure continuous stick-slip transitions. Patient-specific cochlear anatomy is reconstructed from CT imaging and encoded via an analytic parametrization of the scala-tympani lumen, enabling efficient and differentiable contact queries through closest-point projection. Based on a differentiated equilibrium-constraint formulation, we derive an online direction-update law under an RCM-like constraint that suppresses lateral insertion forces while maintaining axial advancement. Simulations and benchtop experiments validate deformation and force trends, demonstrating reduced locking/buckling risk and improved insertion depth. The study highlights how CT-based imaging enhances modeling, planning, and safety capabilities in robot-assisted inner-ear procedures.
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