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

Area of Science:

• Medical Robotics
• Biomedical Imaging
• Ultrasound Technology

Background:

• Current robotic ultrasound systems primarily automate grayscale image acquisition, neglecting crucial functional information.
• This limitation restricts their clinical effectiveness and efficiency in diagnosing abnormalities.

Purpose of the Study:

• To develop a novel robotic ultrasound system for automated screening and anomaly localization using quasi-static elastography (QE).
• To improve the clinical utility of robotic ultrasound by incorporating functional elasticity information.

Main Methods:

• Implemented a compliant force control strategy for continuous QE data acquisition, integrating adaptive out-of-plane posture and in-plane palpation control.
• Developed an unsupervised tissue displacement estimation method and a multi-frame fusion strain estimator for robust analysis.
• Reconstructed 3D strain maps to enable closed-loop control for automated robotic anomaly localization.

Main Results:

• The system demonstrated efficient and adaptable robotic QE-based screening across diverse subject conditions and lesion depths.
• Achieved high accuracy in strain-based anomaly localization, with a 0.77 detection rate and 1.01±0.47 mm error on an abdominal phantom.
• Showcased promising results on a thyroid phantom (0.73 detection rate, 3.44±0.84 mm error), indicating robustness in challenging scenarios.

Conclusions:

• The proposed robotic ultrasound system effectively performs automated screening and anomaly localization using QE.
• It offers improved efficiency, adaptability, and accuracy compared to existing systems, paving the way for enhanced preliminary diagnostics.