Luuk Jacobs

A team at the University of Zurich and ETH Zurich built CURRENT, a recurrent neural network that performs spatial, temporal and spatiotemporal super-resolution of aortic 4D flow MRI within one architecture. On synthetic data its spatial super-resolution directional error was 0.072±0.037 versus 0.097±0.053 for 4DFlowNet, and its temporal directional error was 0.062±0.031 versus 0.064±0.031 for T4DFlowNet. Joint spatiotemporal super-resolution gave relative pressure estimates closer to the high-resolution reference than either direction alone, and the model, trained only on synthetic data, showed promising generalization to in vivo acquisitions.

A team at the University of Zurich and ETH Zurich built CURRENT, a recurrent neural network that performs spatial, temporal and spatiotemporal super-resolution of aortic 4D flow MRI within one architecture. On synthetic data its spatial super-resolution directional error was 0.072±0.037 versus 0.097±0.053 for 4DFlowNet, and its temporal directional error was 0.062±0.031 versus 0.064±0.031 for T4DFlowNet. Joint spatiotemporal super-resolution gave relative pressure estimates closer to the high-resolution reference than either direction alone, and the model, trained only on synthetic data, showed promising generalization to in vivo acquisitions.