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 remain 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). This 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 navigation precision with the probe correctly located at the SIP for all cases, exhibiting positioning error of under 2 mm in translation and under 2 degree in rotation. These results demonstrate the navigation process's capability to accomondate anatomical variations among patients.

翻译：在传统自由手持式诊断超声中，将超声探头导航至标准化成像平面以获取图像是一项关键但高度依赖操作者的任务。机器人超声系统具备通过利用实时超声图像反馈来优化探头位姿的潜力，从而提高成像一致性，减少对操作者专业技能的依赖。然而，如何确定从超声图像中提取可泛化特征以调整探头位姿的恰当方法仍然具有挑战性。在本工作中，我们提出了一种用于机器人超声系统的标准化成像平面导航框架，并以机器人肺部超声为应用背景进行示例。该框架能够在探头接近标准化成像平面时实现自动调整。这是通过显式提取实时肺部超声图像中呈现的多种解剖特征，并执行非患者特异性模板匹配，从而利用基于图像的视觉伺服技术生成朝向标准化成像平面的探头运动来实现的。该框架进一步与主动传感末端执行器集成，后者是一种定制的机器人末端执行器，利用患者外部身体几何形状来保持探头与接触表面的最佳对准，从而在整个导航过程中维持超声信号质量。所提出的方法确保了流程的可解释性与患者间的适应性。验证通过解剖模拟体模和涉及五名人类受试者的体内评估进行。结果表明，该框架具有高导航精度，在所有案例中探头均能准确定位于标准化成像平面，其平移定位误差小于2毫米，旋转定位误差小于2度。这些结果证明了该导航过程能够适应患者间的解剖结构差异。