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

TL;DR

This paper presents a deep reinforcement learning framework for autonomous ultrasound probe navigation to standard scan planes, achieving high accuracy and success rates in simulated spine imaging scenarios.

Contribution

It introduces a novel deep reinforcement learning approach with confidence-based image quality optimization for autonomous ultrasound probe control.

Findings

Achieves 4.91mm/4.65° accuracy in intra-patient scans.

Reaches 92% success rate intra-patient, 46% inter-patient.

Image quality optimization improves navigation performance.

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.