使用强化学习进行肋间间机器人超声波成像的自主路径规划
Yuan Bi1, Cheng Qian1, Zhicheng Zhang2
1Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich, Munich, Germany.
Scientific reports
|February 11, 2026
概括
本研究引入了一种新的强化学习 (RL) 方法,用于机器人超声波 (US) 扫描路径规划. 它使用3D解剖数据在像肋间间区域这样的复杂区域创建非阴影的美国轨迹.
科学领域:
- 医疗成像中的机器人和人工智能
背景情况:
- 超声波 (US) 影像对临床诊断至关重要,但由于操作员的变化而受到影响.
- 机器人系统为美国的考试提供了更高的精度和可重复性.
- 目前的美国自主导航往往缺乏3D解剖背景,阻碍了复杂地区的路径规划.
研究的目的:
- 开发一种新的强化学习 (RL) 方法,用于自主横间US扫描路径规划.
- 将来自计算机断层扫描 (CT) 模板的3D解剖学知识整合到RL决策过程中.
- 为了实现高效和非阴影的美国轨迹规划在解剖学上具有挑战性的地区.
主要方法:
- 使用CT模板与模拟瘤进行训练,创建了一个虚拟环境.
- 采用了强化学习框架,利用3D状态表示.
- 特定任务的状态表示和奖励函数旨在优化扫描并减轻声衰减和阴影等工件.
主要成果:
- 拟议的RL框架成功地规划了非阴影的美国扫描轨迹.
- 该方法在未见患者模型上为单个或多个扫描目标规划路径方面表现出了效率.
- 该方法有效地导航了声音访问有限的区域.
结论:
- 该研究提出了基于RL的有效解决方案,用于自主规划美国路径,特别是在复杂的解剖区域.
- 整合3D解剖信息显著提高了美国扫描路径规划的准确性和可靠性.
- 这项工作为未来机器人超声波执行和控制的发展奠定了基础.
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