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.

INTRODUCTION Minimally Invasive Surgery (MIS) is a modern surgical approach that utilizes advanced techniques and specialized instruments to perform procedures with minimal damage to surrounding tissues. One commonly used tool in MIS is the laparoscopic instrument, which is inserted through small incisions in the body for tissue manipulation or dissection. Conventional laparoscopic forceps use the handle opening angle to control the jaw opening angle. A common limitation of laparoscopic instruments is the ambiguous haptic feedback, which prevents the user from feeling the actual texture or resistance of the tissue being grasped. Surgeons can only guess the amount of applied force through visual cues and proprioception.