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

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.

APP-RUSS: Automated Path Planning for Robotic Ultrasound System

Autonomous robotic ultrasound System (RUSS) has been extensively studied. However, fully automated ultrasound image acquisition is still challenging, partly due to the lack of study in combining two phases of path planning: guiding the ultrasound probe to the scan target and covering the scan surface or volume. This paper presents a system of Automated Path Planning for RUSS (APP-RUSS). Our focus is on the first phase of automation, which emphasizes directing the ultrasound probe's path toward the target over extended distances. Specifically, our APP-RUSS system consists of a RealSense D405 RGB-D camera that is employed for visual guidance of the UR5e robotic arm and a cubic Bezier curve path planning model that is customized for delivering the probe to the recognized target. APP-RUSS can contribute to understanding the integration of the two phases of path planning in robotic ultrasound imaging, paving the way for its clinical adoption.