  Phase-contrast magnetic resonance imaging (MRI) provides time-resolved
quantification of blood flow dynamics that can aid clinical diagnosis. Long in
vivo scan times due to repeated three-dimensional (3D) volume sampling over
cardiac phases and breathing cycles necessitate accelerated imaging techniques
that leverage data correlations. Standard compressed sensing reconstruction
methods require tuning of hyperparameters and are computationally expensive,
which diminishes the potential reduction of examination times. We propose an
efficient model-based deep neural reconstruction network and evaluate its
performance on clinical aortic flow data. The network is shown to reconstruct
undersampled 4D flow MRI data in under a minute on standard consumer hardware.
Remarkably, the relatively low amounts of tunable parameters allowed the
network to be trained on images from 11 reference scans while generalizing well
to retrospective and prospective undersampled data for various acceleration
factors and anatomies.
