Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework

TL;DR

This paper presents a dual-agent reinforcement learning and deep learning framework for autonomous ultrasound probe navigation in spinal sonography, utilizing shadow information to improve view acquisition accuracy in a simulated environment.

Contribution

It introduces a novel shadow-aware dual-agent framework that mimics expert decision-making for autonomous spinal ultrasound imaging, integrating shadow information for improved navigation.

Findings

Achieved average navigation accuracy of 5.18mm/5.25° intra-subject.

Achieved average navigation accuracy of 12.87mm/17.49° inter-subject.

Validated effectiveness in simulation with data from 17 volunteers.

Abstract

Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers. In this work, we propose a novel dual-agent framework that integrates a reinforcement learning (RL) agent and a deep learning (DL) agent to jointly determine the movement of the US probe based on the real-time US images, in order to mimic the decision-making process of an expert sonographer to achieve autonomous standard view acquisitions in spinal sonography. Moreover, inspired by the nature of US propagation and the characteristics of the spinal anatomy, we introduce a view-specific acoustic shadow reward to utilize the shadow information to implicitly guide the navigation of the probe toward different standard views of the spine. Our method is validated in both quantitative and qualitative experiments in a simulation environment built with US data acquired from 17 volunteers. The average navigation accuracy toward different standard views achieves 5.18mm/5.25deg and 12.87mm/17.49deg in the intra- and inter-subject settings, respectively. The results demonstrate that our method can effectively interpret the US images and navigate the probe to acquire multiple standard views of the spine.