Guiding the Last Centimeter: Novel Anatomy-Aware Probe Servoing for Standardized Imaging Plane Navigation in Robotic Lung Ultrasound

Navigating the ultrasound (US) probe to the standardized imaging plane (SIP) for image acquisition is a critical but operator-dependent task in conventional freehand diagnostic US. Robotic US systems (RUSS) offer the potential to enhance imaging consistency by leveraging real-time US image feedback to optimize the probe pose, thereby reducing reliance on operator expertise. However, determining the proper approach to extracting generalizable features from the US images for probe pose adjustment remains challenging. In this work, we propose a SIP navigation framework for RUSS, exemplified in the context of robotic lung ultrasound (LUS). This framework facilitates automatic probe adjustment when in proximity to the SIP. This is achieved by explicitly extracting multiple anatomical features presented in real-time LUS images and performing non-patient-specific template matching to generate probe motion towards the SIP using image-based visual servoing (IBVS). The framework is further integrated with the active-sensing end-effector (A-SEE), a customized robot end-effector that leverages patient external body geometry to maintain optimal probe alignment with the contact surface, thus preserving US signal quality throughout the navigation. The proposed approach ensures procedural interpretability and inter-patient adaptability. Validation is conducted through anatomy-mimicking phantom and in-vivo evaluations involving five human subjects. The results show the framework’s high navigating precision with the probe correctly located at the SIP for all cases, exhibiting positioning error of under 2 mm in translation and under 2 degrees in rotation. These results demonstrate the navigation process’s capability to accommodate anatomical variations among patients.
Note to Practitioners
—Compared with traditional freehand ultrasound (US) imaging, robotic ultrasound systems (RUSS) have the potential to largely standardize the US diagnosis outcome caused by varying operator expertise if an inter-patient consistent, automatic standardized imaging plane (SIP) navigation process is available. This paper presents a SIP navigation framework for lung US (LUS) examination, which recognizes anatomical landmarks from the US images and fine-tunes the pose of the US probe so that the landmarks are positioned in accordance with a non-patient-specific template image. The special end-effector, active-sensing end-effector (A-SEE), maintains the probe at an optimal orientation with respect to the body, allowing consistent-quality US images to be acquired throughout the navigation. Unlike previous works, our approach can navigate to complicated SIP containing multiple anatomies with interpretable robot arm motion. We verified our framework’s ability to navigate the probe to the SIP with millimeter-level accuracy under phantom and human experiment settings. While preliminary results demonstrate the framework’s efficacy in guiding the robotic LUS procedure, the performance of the system on other examinations (e.g., liver and thyroid US) involving soft tissues requires further validation. In the future, the framework can be applied in various US examinations by implementing specific anatomical feature detection modules.