  4D-flow magnetic resonance imaging (MRI) is an emerging imaging technique
where spatiotemporal 3D blood velocity can be captured with full volumetric
coverage in a single non-invasive examination. This enables qualitative and
quantitative analysis of hemodynamic flow parameters of the heart and great
vessels. An increase in the image resolution would provide more accuracy and
allow better assessment of the blood flow, especially for patients with
abnormal flows. However, this must be balanced with increasing imaging time.
The recent success of deep learning in generating super resolution images shows
promise for implementation in medical images. We utilized computational fluid
dynamics simulations to generate fluid flow simulations and represent them as
synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D
flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet
network was trained on this synthetic 4D flow data and was capable in producing
noise-free super resolution 4D flow phase images with upsample factor of 2. We
also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal
volunteer data, and demonstrated comparable results with the actual flow rate
measurements giving an absolute relative error of 0.6 to 5.8% and 1.1 to 3.8%
in the phantom data and normal volunteer data, respectively.
