Deep Vein Thrombosis (DVT) is a common yet potentially fatal condition, often leading to critical complications like pulmonary embolism. DVT is commonly diagnosed using Ultrasound (US) imaging, which can be inconsistent due to its high dependence on the operator's skill. Robotic US Systems (RUSs) aim to improve diagnostic test consistency but face challenges with the complex scanning pattern needed for DVT assessment, where precise control over US probe pressure is crucial for indirectly detecting occlusions. This work introduces an imitation learning method, based on Kernelized Movement Primitives (KMP), to standardize DVT US exams by training an autonomous robotic controller using sonographer demonstrations. A new recording device design enhances demonstration ergonomics, integrating with US probes and enabling seamless force and position data recording. KMPs are used to capture scanning skills, linking scan trajectory and force, enabling generalization beyond the demonstrations. Our approach, evaluated on synthetic models and volunteers, shows that the KMP-based RUS can replicate an expert's force control and image quality in DVT US examination. It outperforms previous methods using manually defined force profiles, improving exam standardization and reducing reliance on specialized sonographers.

翻译：深静脉血栓（DVT）是一种常见但可能致命的疾病，常导致肺栓塞等严重并发症。DVT通常通过超声（US）成像进行诊断，但由于其高度依赖操作者的技能，诊断结果可能存在不一致性。机器人超声系统（RUSs）旨在提高诊断测试的一致性，但在应对DVT评估所需的复杂扫描模式时面临挑战，其中对超声探头压力的精确控制对于间接检测血管闭塞至关重要。本研究提出了一种基于核化运动基元（KMP）的模仿学习方法，通过使用超声医师的示范数据训练自主机器人控制器，以实现DVT超声检查的标准化。一种新型记录设备设计提升了示范过程的人体工程学性能，该设备与超声探头集成，能够无缝记录力和位置数据。KMP用于捕捉扫描技能，将扫描轨迹与力联系起来，从而实现超越示范数据的泛化能力。我们在合成模型和志愿者身上评估了该方法，结果表明基于KMP的RUS能够在DVT超声检查中复现专家的力控制与图像质量。该方法优于先前使用手动定义力剖面的方法，提升了检查的标准化程度并降低了对专业超声医师的依赖。