Ma Xihan, Zhang Ziming, Zhang Haichong K
Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609 USA.
Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609 USA.
Rep U S. 2021 Sep-Oct;2021:9467-9474. doi: 10.1109/iros51168.2021.9635902. Epub 2021 Dec 16.
Under the ceaseless global COVID-19 pandemic, lung ultrasound (LUS) is the emerging way for effective diagnosis and severeness evaluation of respiratory diseases. However, close physical contact is unavoidable in conventional clinical ultrasound, increasing the infection risk for health-care workers. Hence, a scanning approach involving minimal physical contact between an operator and a patient is vital to maximize the safety of clinical ultrasound procedures. A robotic ultrasound platform can satisfy this need by remotely manipulating the ultrasound probe with a robotic arm. This paper proposes a robotic LUS system that incorporates the automatic identification and execution of the ultrasound probe placement pose without manual input. An RGB-D camera is utilized to recognize the scanning targets on the patient through a learning-based human pose estimation algorithm and solve for the landing pose to attach the probe vertically to the tissue surface; A position/force controller is designed to handle intraoperative probe pose adjustment for maintaining the contact force. We evaluated the scanning area localization accuracy, motion execution accuracy, and ultrasound image acquisition capability using an upper torso mannequin and a realistic lung ultrasound phantom with healthy and COVID-19-infected lung anatomy. Results demonstrated the overall scanning target localization accuracy of 19.67 ± 4.92 mm and the probe landing pose estimation accuracy of 6.92 ± 2.75 mm in translation, 10.35 ± 2.97 deg in rotation. The contact force-controlled robotic scanning allowed the successful ultrasound image collection, capturing pathological landmarks.
在全球新冠疫情持续不断的情况下，肺部超声（LUS）是呼吸系统疾病有效诊断和严重程度评估的新兴方法。然而，传统临床超声检查中不可避免地会有密切的身体接触，增加了医护人员的感染风险。因此，一种使操作者与患者之间身体接触最少的扫描方法对于最大限度提高临床超声检查程序的安全性至关重要。机器人超声平台可以通过用机械臂远程操纵超声探头来满足这一需求。本文提出了一种机器人肺部超声系统，该系统无需人工输入即可自动识别并执行超声探头的放置姿势。利用RGB-D相机通过基于学习的人体姿势估计算法识别患者身上的扫描目标，并求解将探头垂直附着到组织表面的着陆姿势；设计了一个位置/力控制器来处理术中探头姿势调整以保持接触力。我们使用上半身人体模型以及具有健康和新冠感染肺部解剖结构的逼真肺部超声模型，评估了扫描区域定位精度、运动执行精度和超声图像采集能力。结果表明，整体扫描目标定位精度为19.67±4.92毫米，探头着陆姿势估计精度在平移方面为6.92±2.75毫米，在旋转方面为10.35±2.97度。接触力控制的机器人扫描成功采集到了超声图像，捕捉到了病理标志。
