# Motion-Compensated Autonomous Scanning for Tumour Localisation using Intraoperative Ultrasound

## TL;DR

This paper presents a motion-compensated autonomous ultrasound scanning system that adapts to tissue motion during tumour localisation, improving accuracy and reducing surgeon workload in minimally invasive procedures.

## Contribution

It introduces a novel motion-aware scanning trajectory generation and a vision-based tissue motion learning approach integrated into a visual servoing framework.

## Key findings

- Validated with phantom experiments showing improved accuracy.
- Demonstrated robustness in ex vivo tissue motion scenarios.
- Effective motion compensation enhances tumour localisation precision.

## Abstract

Intraoperative ultrasound facilitates localisation of tumour boundaries during minimally invasive procedures. Autonomous ultrasound scanning systems have been recently proposed to improve scanning accuracy and reduce surgeons' cognitive load. However, current methods mainly consider static scanning environments typically with the probe pressing against the tissue surface. In this work, a motion-compensated autonomous ultrasound scanning system using the da Vinci Research Kit (dVRK) is proposed. An optimal scanning trajectory is generated considering both the tissue surface shape and the ultrasound transducer dimensions. A robust vision-based approach is proposed to learn the underlying tissue motion characteristics. The learned motion model is then incorporated into the visual servoing framework. The proposed system has been validated with both phantom and ex vivo experiments using the ground truth motion data for comparison.