Quasi-static Elastography-driven Automated Robotic Ultrasound Screening and Localization

Robotic ultrasound imaging systems primarily focus on enhancing automation in grayscale image acquisition but lack essential functional information, which restricts their clinical effectiveness and efficiency. In this regard, we propose a novel robotic ultrasound system capable of automatically screening and anomaly localization using quasi-static elastography (QE). For continuous screening, a compliant force control strategy is devised to manage the complex probe operations required for elasticity data acquisition. This involves the integration of adaptive out-of-plane posture control and in-plane palpation motion control. For anomaly localization, tissue strains are analyzed using multi-source motion data. We introduce an unsupervised tissue displacement estimation method, complemented by a strain estimator with multi-frame fusion for robust strain estimation. A 3D strain map is reconstructed to enable closed-loop control for automated robotic localization. The system has been validated through extensive experiments on two realistic phantoms and tested on human subjects. Results demonstrate that our system can perform robotic QE-based screening across subjects with varying surface conditions and lesion depths, showing improved efficiency and adaptability compared to existing systems. It achieves satisfactory accuracy in strain-based anomaly localization, with a detection rate of 0.77 and an average localization error of 1.01±0.47 mm for the abdominal phantom, and 0.73 and 3.44±0.84 mm for the more challenging thyroid phantom. By identifying and localizing suspicious anomalies in 3D space, the proposed system shows promise in providing preliminary dia.