Robotized Ultrasound Imaging of the Peripheral Arteries -- a Phantom Study

TL;DR

This study demonstrates a robotized ultrasound system capable of autonomously imaging peripheral arteries with high accuracy in phantom models, potentially enabling fully automated, radiation-free vascular diagnostics.

Contribution

It introduces a hierarchical CNN-based control system for robotic ultrasound imaging, advancing towards fully autonomous peripheral artery scans.

Findings

Achieved 100% vessel lumen visibility in phantom scans.

Mean vessel center deviation was 2.47 mm in easy scenarios.

System shows promise for automated, radiation-free artery imaging.

Abstract

The first choice in diagnostic imaging for patients suffering from peripheral arterial disease is 2D ultrasound (US). However, for a proper imaging process, a skilled and experienced sonographer is required. Additionally, it is a highly user-dependent operation. A robotized US system that autonomously scans the peripheral arteries has the potential to overcome these limitations. In this work, we extend a previously proposed system by a hierarchical image analysis pipeline based on convolutional neural networks in order to control the robot. The system was evaluated by checking its feasibility to keep the vessel lumen of a leg phantom within the US image while scanning along the artery. In 100 % of the images acquired during the scan process the whole vessel lumen was visible. While defining an insensitivity margin of 2.74 mm, the mean absolute distance between vessel center and the horizontal image center line was 2.47 mm and 3.90 mm for an easy and complex scenario, respectively. In conclusion, this system presents the basis for fully automatized peripheral artery imaging in humans using a radiation-free approach.