  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.
