  Robotic ultrasound (US) imaging aims at overcoming some of the limitations of
free-hand US examinations, e.g. difficulty in guaranteeing intra- and
inter-operator repeatability. However, due to anatomical and physiological
variations between patients and relative movement of anatomical substructures,
it is challenging to robustly generate optimal trajectories to examine the
anatomies of interest, in particular, when they comprise articulated joints. To
address this challenge, this paper proposes a vision-based approach allowing
autonomous robotic US limb scanning. To this end, an atlas MRI template of a
human arm with annotated vascular structures is used to generate trajectories
and register and project them onto patients' skin surfaces for robotic US
acquisition. To effectively segment and accurately reconstruct the targeted 3D
vessel, we make use of spatial continuity in consecutive US frames by
incorporating channel attention modules into a U-Net-type neural network. The
automatic trajectory generation method is evaluated on six volunteers with
various articulated joint angles. In all cases, the system can successfully
acquire the planned vascular structure on volunteers' limbs. For one volunteer
the MRI scan was also available, which allows the evaluation of the average
radius of the scanned artery from US images, resulting in a radius estimation
($1.2\pm0.05~mm$) comparable to the MRI ground truth ($1.2\pm0.04~mm$).
