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Ultrasound (US) imaging is widely used for diagnosing and monitoring arterialdiseases, mainly due to the advantages of being non-invasive, radiation-free,and real-time. In order to provide additional information to assist cliniciansin diagnosis, the tubular structures are often segmented from US images. Toimprove the artery segmentation accuracy and stability during scans, this workpresents a novel pulsation-assisted segmentation neural network (PAS-NN) byexplicitly taking advantage of the cardiac-induced motions. Motionmagnification techniques are employed to amplify the subtle motion within thefrequency band of interest to extract the pulsation signals from sequential USimages. The extracted real-time pulsation information can help to locate thearteries on cross-section US images; therefore, we explicitly integrated thepulsation into the proposed PAS-NN as attention guidance. Notably, a roboticarm is necessary to provide stable movement during US imaging since magnifyingthe target motions from the US images captured along a scan path is notmanually feasible due to the hand tremor. To validate the proposed robotic USsystem for imaging arteries, experiments are carried out on volunteers' carotidand radial arteries. The results demonstrated that the PAS-NN could achievecomparable results as state-of-the-art on carotid and can effectively improvethe segmentation performance for small vessels (radial artery).

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