Akbari Mojtaba, Carriere Jay, Meyer Tyler, Sloboda Ron, Husain Siraj, Usmani Nawaid, Tavakoli Mahdi
Telerobotic and Biorobotic System Group, Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
Division of Radiation Oncology, Tom Baker Cancer Centre, Calgary, AB, Canada.
Front Robot AI. 2021 Mar 22;8:645424. doi: 10.3389/frobt.2021.645424. eCollection 2021.
During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.
在超声(US)扫描过程中,超声检查技师与患者密切接触,这使他们面临感染新冠病毒的风险。在本文中,我们提出了一种机器人辅助系统,该系统可自动扫描组织,增加超声检查技师与患者之间的距离并缩短他们之间的接触时间。此方法是作为对新冠疫情的快速响应而开发的。它考虑了超声检查技师在超声扫描操作方式方面的偏好,并且可以针对不同应用快速进行训练。我们提出的系统使用一个握持超声探头的灵巧机器人手臂自动扫描组织。该系统实时评估采集到的超声图像的质量。这种超声图像反馈将用于根据图像帧的质量自动调整超声探头的接触力。质量评估算法基于三个超声图像特征:相关性、压缩和噪声特性。这些超声图像特征被输入到支持向量机分类器中,机器人手臂将根据支持向量机的输出调整超声扫描力。所提出的系统使超声检查技师能够与患者保持距离,因为超声检查技师无需长时间握持探头并按压患者身体。支持向量机使用牛和猪的生物组织进行训练,然后该系统在增塑溶胶仿体组织上进行了实验测试。实验结果向我们表明,我们提出的质量评估算法成功地保持了超声图像质量,并且速度足够快,可用于机器人控制回路。