The quantification of hemodynamics using 4D-flow magnetic resonance imaging (MRI) data requires an adequate spatio-temporal vector field resolution at a low noise level. Here, the Menze group provides a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. A detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution shows that the new method not only improves the peak-velocity to noise ratio of the flow field by 10% and 30% for in vivo cardiovascular and cerebrovascular data, respectively, for 4 × super-resolution, but also offers 10x faster inference over the state-of-the-art.
See Shit et al., Front. Artif. Intell.