Contact-Adaptive Robotic Ultrasound Probe Control for Tissue Exploration and Continuous Task-Relevant Visualization Using Robot-Free Image-Motion Demonstration

2026-09-27

AI Summary

This study addresses the challenge of continuously maintaining a surgeon-selected view for monitoring tissue changes during surgery. We propose a non-learning-based Dynamic Inertial Motion Controller (DIMC). This method utilizes a six-axis inertial sensor to record robot-free demonstration trajectories, integrating image delay compensation, static-dynamic segmentation, and beam-axis force control to construct an interpretable controller incorporating force regulation and safety supervision mechanisms. In experiments using a dynamic bladder model, DIMC successfully achieved stable view maintenance in all seven trials (7/7), compared to only one success in the control group (1/7). Furthermore, it effectively suppressed contact force deviations. These results validate the reliability and safety of the proposed approach for contact-adaptive intraoperative monitoring.

Abstract

Robotic ultrasound commonly targets standardized views, predefined scanning protocols, or expert-scan reproduction. We target a different role: while a clinician performs the primary procedure, a robotic assistant maintains a clinician-selected view so that changing tissue remains observable. We present contact-adaptive robotic ultrasound monitoring derived from robot-free demonstrations. Sonologger clamps a six-axis inertial sensor to the clinician-operated probe and records synchronized ultrasound and probe-frame relative rotations without a robot, camera, or tracker. Rather than imitating trajectories, the recordings measure which probe axis and sign the clinician uses to correct an image offset. A measured image latency is removed before still-move-still segmentation yields relative-rotation labels; translation is excluded from control because of inertial drift. These measurements parameterize the Demonstration-Informed Image-Motion Controller (DIMC), an interpretable, non-learned controller combined with beam-axis force regulation and an independent safety supervisor with 5 N forced retreat. On a dynamic bladder phantom, DIMC acquired and held a task-relevant view in 7 of 7 blind, pose-paired trials versus 1 of 7 for a static hold (McNemar p = 0.031), and retained the view during syringe-driven hydro-distension while the supervisor bounded the force excursion. The framework offers an operational representation of ultrasound context for contact-adaptive intraoperative monitoring; clinical validation remains future work.

Problem

• Robotic ultrasound
• Contact-adaptive control
• Intraoperative monitoring
• Task-relevant visualization
• Tissue exploration

Innovation

• Contact-adaptive control
• Demonstration-informed controller
• Robotic ultrasound
• Inertial sensing
• Safety supervision