A Decision-Making Algorithm for Robotic Breast Ultrasound High-Quality Imaging via Broad Reinforcement Learning From Demonstration

Abstract

Robotic breast ultrasound (RBUS) aims to standardize breast ultrasonography, reduce the workload of sonographers, and provide high-quality ultrasound (US) images for subsequent diagnosis. In the process of RBUS screening, adjusting the US probe correctly and efficiently to acquire high-quality US images is fundamental and significant. In this letter, a learning-based US probe adjustment framework is proposed. Firstly, a lightweight multi-task combination approach is utilized for jointly assessing US imaging quality with multiple indicators. Then, an experience-guided learning algorithm, called broad reinforcement learning from demonstration (BRLfD), is proposed to efficiently learn the optimal US probe adjustment policy. The effectiveness of the learned policy is verified in five testing lesion locations. The results show that the US probe can reach the goal state in less than 5 steps on average, and the proposed method can automatically adjust the US probe efficiently to obtain high-quality breast US images for clinical diagnosis.