  Synthetic Aperture Vector Flow Imaging (SA-VFI) can visualize complex cardiac
and vascular blood flow patterns at high temporal resolution with a large field
of view. Convolutional neural networks (CNNs) are commonly used in image and
video recognition and classification. However, most recently presented CNNs
also allow for making per-pixel predictions as needed in optical flow
velocimetry. To our knowledge we demonstrate here for the first time a CNN
architecture to produce 2D full flow field predictions from high frame rate SA
ultrasound images using supervised learning. The CNN was initially trained
using CFD-generated and augmented noiseless SA ultrasound data of a realistic
vessel geometry. Subsequently, a mix of noisy simulated and real \textit{in
vivo} acquisitions were added to increase the network's robustness. The
resulting flow field of the CNN resembled the ground truth accurately with an
endpoint-error percentage between 6.5\% to 14.5\%. Furthermore, when confronted
with an unknown geometry of an arterial bifurcation, the CNN was able to
predict an accurate flow field indicating its ability for generalization.
Remarkably, the CNN also performed well for rotational flows, which usually
requires advanced, computationally intensive VFI methods. We have demonstrated
that convolutional neural networks can be used to estimate complex
multidirectional flow.
