Autonomous ultrasound (US) acquisition is an important yet challenging task, as it involves interpretation of the highly complex and variable images and their spatial relationships. In this work, we propose a deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans. Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process. We validate our method in a simulation environment built with real-world data collected in the US imaging of the spine. Experimental results demonstrate that our method can perform reproducible US probe navigation towards the standard scan plane with an accuracy of $4.91mm/4.65^\circ$ in the intra-patient setting, and accomplish the task in the intra- and inter-patient settings with a success rate of $92\%$ and $46\%$, respectively. The results also show that the introduction of image quality optimization in our method can effectively improve the navigation performance.
翻译:自主超声波(US)获取是一项重要而富有挑战性的任务,因为它涉及对高度复杂和可变图像及其空间关系的解读。 在这项工作中,我们提议了一个深度强化学习框架,以自主控制基于实时图像反馈的6D虚拟美国探测器的6-D配置,以在现实世界美国扫描的限制下向标准扫描机飞行。此外,我们提议了一种基于信任的方法,以编码学习过程中图像质量的最佳化。我们验证了在模拟环境中使用美国脊椎成像中收集的真实世界数据构建的方法。实验结果表明,我们的方法可以对标准扫描机进行可复制的美国探测器导航,准确度为4.91毫米/4.65 ⁇ circ$,在病人内部和病人间环境中完成任务,成功率分别为92美元和46美元。结果还表明,在方法中引入图像质量优化可以有效改进导航性能。