Image-Driven Imitation Learning: Acquiring Expert Scanning Skills in Robotics Ultrasound

A promising ultrasound (US) image acquisition requires experienced sonographers holding the probe with proper force and pose to ensure an excellent acoustic coupling. To enable a robotic ultrasound system (RUSS) to acquire the sonographers’ skills from ultrasound image demonstrations, this paper proposes a cutting-edge framework that integrates an expert technique discrimination network and a robotic strategy generation network to learn expert scanning skills. In this framework, the expert technique discrimination network focuses on learning expert scanning techniques from the pre- and post-frame ultrasound images. Furthermore, to acquire expert scanning skills and obtain a standard image view, we design a knowledge-based algorithm grounded on inverse reinforcement learning (IRL) to generate a series of scanning policies concerning the expert technique discrimination network. Both simulations and experiments are conducted to validate the effectiveness of the proposed framework by comparing it with MI-GPSR and PTR. The scanning success rate and trajectory tracking error of the algorithm in the simulation environment are 68% and 12.0782, respectively, while in the phantom environment are 94% and 11.8367. The results demonstrate good performance in the task of imitating expert techniques for autonomous scanning. Note to Practitioners—The motivation for this work originates from the need for ultrasound scanning tasks, such as carotid plaque and thyroid scans, to follow specific procedures. In clinical practice, sonographers require extensive learning and training to acquire these scanning skills. Traditional autonomous robotic ultrasound research often focuses on achieving the final standard view, neglecting the logical flow of the scanning process. Moreover, current studies on imitation learning for robotic ultrasound typically require not only ultrasound images from expert demonstrations but also additional data like probe position, adding complexity and potential errors to the data collection process. The proposed method addresses this by designing a framework that learns and comprehends expert techniques solely from image demonstrations. This endows RUSS with the ability to perform a human-like ultrasound scanning task. This work can be applied in the field of autonomous ultrasound robotics to assist sonographers in achieving more precise scans.