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.
@article{arxiv.2405.05787,
title = {Autonomous Robotic Ultrasound System for Liver Follow-up Diagnosis: Pilot Phantom Study},
author = {Tianpeng Zhang and Sekeun Kim and Jerome Charton and Haitong Ma and Kyungsang Kim and Na Li and Quanzheng Li},
journal= {arXiv preprint arXiv:2405.05787},
year = {2024}
}