Tang Xiuhong, Wang Hongbo, Luo Jingjing, Jiang Jinlei, Nian Fan, Qi Lizhe, Sang Lingfeng, Gan Zhongxue
Academy for Engineering and Technology, Fudan University, Shanghai, China.
Intelligent Robot Engineering Research Center of Ministry of Education, Shanghai, China.
Front Robot AI. 2024 May 7;11:1383732. doi: 10.3389/frobt.2024.1383732. eCollection 2024.
In traditional cardiac ultrasound diagnostics, the process of planning scanning paths and adjusting the ultrasound window relies solely on the experience and intuition of the physician, a method that not only affects the efficiency and quality of cardiac imaging but also increases the workload for physicians. To overcome these challenges, this study introduces a robotic system designed for autonomous cardiac ultrasound scanning, with the goal of advancing both the degree of automation and the quality of imaging in cardiac ultrasound examinations. The system achieves autonomous functionality through two key stages: initially, in the autonomous path planning stage, it utilizes a camera posture adjustment method based on the human body's central region and its planar normal vectors to achieve automatic adjustment of the camera's positioning angle; precise segmentation of the human body point cloud is accomplished through efficient point cloud processing techniques, and precise localization of the region of interest (ROI) based on keypoints of the human body. Furthermore, by applying isometric path slicing and B-spline curve fitting techniques, it independently plans the scanning path and the initial position of the probe. Subsequently, in the autonomous scanning stage, an innovative servo control strategy based on cardiac image edge correction is introduced to optimize the quality of the cardiac ultrasound window, integrating position compensation through admittance control to enhance the stability of autonomous cardiac ultrasound imaging, thereby obtaining a detailed view of the heart's structure and function. A series of experimental validations on human and cardiac models have assessed the system's effectiveness and precision in the correction of camera pose, planning of scanning paths, and control of cardiac ultrasound imaging quality, demonstrating its significant potential for clinical ultrasound scanning applications.
在传统的心脏超声诊断中，规划扫描路径和调整超声窗口的过程完全依赖于医生的经验和直觉，这种方法不仅影响心脏成像的效率和质量，还增加了医生的工作量。为了克服这些挑战，本研究引入了一种用于自主心脏超声扫描的机器人系统，旨在提高心脏超声检查的自动化程度和成像质量。该系统通过两个关键阶段实现自主功能：首先，在自主路径规划阶段，它利用基于人体中心区域及其平面法向量的相机姿态调整方法，实现相机定位角度的自动调整；通过高效的点云处理技术完成人体点云的精确分割，并基于人体关键点对感兴趣区域（ROI）进行精确定位。此外，通过应用等距路径切片和B样条曲线拟合技术，它独立规划扫描路径和探头的初始位置。随后，在自主扫描阶段，引入了一种基于心脏图像边缘校正的创新伺服控制策略，以优化心脏超声窗口的质量，通过导纳控制进行位置补偿，以增强自主心脏超声成像的稳定性，从而获得心脏结构和功能的详细视图。在人体和心脏模型上进行的一系列实验验证评估了该系统在相机姿态校正、扫描路径规划和心脏超声成像质量控制方面的有效性和精度，证明了其在临床超声扫描应用中的巨大潜力。
