Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation

Ultrasound (US) imaging is widely used for diagnosing and monitoring arterial diseases, mainly due to the advantages of being non-invasive, radiation-free, and real-time. In order to provide additional information to assist clinicians in diagnosis, the tubular structures are often segmented from US images. To improve the artery segmentation accuracy and stability during scans, this work presents a novel pulsation-assisted segmentation neural network (PAS-NN) by explicitly taking advantage of the cardiac-induced motions. Motion magnification techniques are employed to amplify the subtle motion within the frequency band of interest to extract the pulsation signals from sequential US images. The extracted real-time pulsation information can help to locate the arteries on cross-section US images; therefore, we explicitly integrated the pulsation into the proposed PAS-NN as attention guidance. Notably, a robotic arm is necessary to provide stable movement during US imaging since magnifying the target motions from the US images captured along a scan path is not manually feasible due to the hand tremor. To validate the proposed robotic US system for imaging arteries, experiments are carried out on volunteers' carotid and radial arteries. The results demonstrated that the PAS-NN could achieve comparable results as state-of-the-art on carotid and can effectively improve the segmentation performance for small vessels (radial artery). The code¹¹Code: https://qithub.com/dianveHuanq/RobPMEPASNN and demonstration video²²Video: https://youtu.belc9AM042_lUQ can be publicly accessed.

I. Introduction

Peripheral artery disease (PAD) is among the most impactful cardiovascular conditions [1]. It is a chronic vascular disease where the arteries become blocked or narrowed, reducing the blood flow. Regular ultrasound (US) examination can effectively diagnose PAD, allowing for the early treatment. Considering the limited medical workforce and highly operator-dependent nature of US scans, developing a robotic US system (RUSS) for the daily US examination or US-guided navigation [2] is becoming a research hot spot. Extensive studies have been conducted on the tasks of autonomous arteries screening, such as carotid [3] and radial arteries [4], [5]. In order to have a deep understanding of the field of RUSS, readers can refer to a comprehensive survey article, which systematically discussed the basic enabling technology, advanced application-orientated technology, and emerging learning-based robotic acquisition [6].