Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning

Abstract

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.

Accepted at ICRA 2021

References in corpus (2)

• Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study
• Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

Cited by in corpus (5)

• Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations
• Closed-Loop Magnetic Manipulation for Robotic Transesophageal Echocardiography
• Learning Ultrasound Scanning Skills from Human Demonstrations
• Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain
• Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework