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Phase-contrast magnetic resonance imaging (MRI) provides time-resolvedquantification of blood flow dynamics that can aid clinical diagnosis. Long invivo scan times due to repeated three-dimensional (3D) volume sampling overcardiac phases and breathing cycles necessitate accelerated imaging techniquesthat leverage data correlations. Standard compressed sensing reconstructionmethods require tuning of hyperparameters and are computationally expensive,which diminishes the potential reduction of examination times. We propose anefficient model-based deep neural reconstruction network and evaluate itsperformance on clinical aortic flow data. The network is shown to reconstructundersampled 4D flow MRI data in under a minute on standard consumer hardware.Remarkably, the relatively low amounts of tunable parameters allowed thenetwork to be trained on images from 11 reference scans while generalizing wellto retrospective and prospective undersampled data for various accelerationfactors and anatomies.

Deep variational network for rapid 4D flow MRI reconstruction