用扩散策略实现可泛化的颈动脉超声自动扫描，兼顾安全与精度。
UltraDP: Generalizable Carotid Ultrasound Scanning with Force-Aware Diffusion Policy
- 基于多模态输入的扩散策略，动态生成扫描动作。
- 在未见过的受试者上达到95%横切面定位成功率。
- 适合需高安全性和泛化能力的医疗机器人研发人员。
超声扫描是实时、无创诊断的关键技术。然而，患者解剖结构差异及人机交互复杂性给自主机器人扫描带来挑战。现有超声机器人普遍泛化能力弱且数据利用效率低。为此，我们提出UltraDP，一种基于扩散策略的方法，接收多模态输入（超声图像、腕部相机图像、接触力矩、探头位姿），生成符合多模态动作分布的自主颈动脉扫描动作。我们设计专用引导模块，使策略输出的动作能将动脉居中于超声图像中。为确保机器人与人体间稳定接触和安全互动，采用混合力-阻抗控制器驱动机器人跟踪轨迹。同时，构建了大规模训练数据集，包含21名不同性别志愿者的210次扫描，共46万对样本。通过探索引导模块与扩散策略的强泛化能力，UltraDP在未见受试者上实现了95%的横切面扫描成功率，验证了其有效性。
原文摘要 · Abstract (English)
Ultrasound scanning is a critical imaging technique for real-time, non-invasive diagnostics. However, variations in patient anatomy and complex human-in-the-loop interactions pose significant challenges for autonomous robotic scanning. Existing ultrasound scanning robots are commonly limited to relatively low generalization and inefficient data utilization. To overcome these limitations, we present UltraDP, a Diffusion-Policy-based method that receives multi-sensory inputs (ultrasound images, wrist camera images, contact wrench, and probe pose) and generates actions that are fit for multi-modal action distributions in autonomous ultrasound scanning of carotid artery. We propose a specialized guidance module to enable the policy to output actions that center the artery in ultrasound images. To ensure stable contact and safe interaction between the robot and the human subject, a hybrid force-impedance controller is utilized to drive the robot to track such trajectories. Also, we have built a large-scale training dataset for carotid scanning comprising 210 scans with 460k sample pairs from 21 volunteers of both genders. By exploring our guidance module and DP's strong generalization ability, UltraDP achieves a 95% success rate in transverse scanning on previously unseen subjects, demonstrating its effectiveness.
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