Robotized Ultrasound Imaging of the Peripheral Arteries -- a Phantom Study
This work addresses the need for skilled sonographers in diagnosing peripheral arterial disease by proposing a robotized system, though it is incremental as it builds on a previously proposed system.
The authors tackled the challenge of automating ultrasound imaging for peripheral arteries by extending a robotic system with a hierarchical CNN-based image analysis pipeline, achieving 100% visibility of the vessel lumen in scans and mean absolute distances of 2.47 mm and 3.90 mm from the image center in easy and complex scenarios, respectively.
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.