Autonomous Robotic Ultrasound System for Liver Follow-up Diagnosis: Pilot Phantom Study

TL;DR

This paper presents an autonomous robotic ultrasound system that uses CT imaging, deep learning, and precise navigation to perform liver follow-up scans, aiming to improve efficiency and accessibility in outpatient settings.

Contribution

The study introduces a novel autonomous ultrasound system integrating 3D US-CT registration and deep learning for precise liver imaging and target localization.

Findings

Accurate 3D hepatic vein imaging achieved

Reliable target localization in phantom trials

Potential to reduce healthcare costs and time

Abstract

The paper introduces a novel autonomous robot ultrasound (US) system targeting liver follow-up scans for outpatients in local communities. Given a computed tomography (CT) image with specific target regions of interest, the proposed system carries out the autonomous follow-up scan in three steps: (i) initial robot contact to surface, (ii) coordinate mapping between CT image and robot, and (iii) target US scan. Utilizing 3D US-CT registration and deep learning-based segmentation networks, we can achieve precise imaging of 3D hepatic veins, facilitating accurate coordinate mapping between CT and the robot. This enables the automatic localization of follow-up targets within the CT image, allowing the robot to navigate precisely to the target's surface. Evaluation of the ultrasound phantom confirms the quality of the US-CT registration and shows the robot reliably locates the targets in repeated trials. The proposed framework holds the potential to significantly reduce time and costs for healthcare providers, clinicians, and follow-up patients, thereby addressing the increasing healthcare burden associated with chronic disease in local communities.