Breaking the Latency Barrier: Synergistic Perception and Control for High-Frequency 3D Ultrasound Servoing

TL;DR

This paper introduces a novel perception-control framework for robotic ultrasound systems that achieves high-frequency, accurate 3D target tracking and re-acquisition, enabling more responsive and robust clinical procedures.

Contribution

It presents a synergistic co-design of perception and control with a dual-stream perception network and a flow policy, significantly reducing latency and improving tracking accuracy in RUSS.

Findings

Achieves closed-loop control over 60Hz frequency.

Tracks 3D trajectories with mean error below 6.5mm.

Successfully re-acquires targets from large displacements and tracks at high speeds.

Abstract

Real-time tracking of dynamic targets amidst large-scale, high-frequency disturbances remains a critical unsolved challenge in Robotic Ultrasound Systems (RUSS), primarily due to the end-to-end latency of existing systems. This paper argues that breaking this latency barrier requires a fundamental shift towards the synergistic co-design of perception and control. We realize it in a novel framework with two tightly-coupled contributions: (1) a Decoupled Dual-Stream Perception Network that robustly estimates 3D translational state from 2D images at high frequency, and (2) a Single-Step Flow Policy that generates entire action sequences in one inference pass, bypassing the iterative bottleneck of conventional policies. This synergy enables a closed-loop control frequency exceeding 60Hz. On a dynamic phantom, our system not only tracks complex 3D trajectories with a mean error below 6.5mm but also demonstrates robust re-acquisition from over 170mm displacement. Furthermore, it can track targets at speeds of 102mm/s, achieving a terminal error below 1.7mm. Moreover, in-vivo experiments on a human volunteer validate the framework's effectiveness and robustness in a realistic clinical setting. Our work presents a RUSS holistically architected to unify high-bandwidth tracking with large-scale repositioning, a critical step towards robust autonomy in dynamic clinical environments.