We present a pipeline to synthesize patient-specific pulsatile turbulent 4D flow MRI datasets of the aorta. Aortic motion and inflow are extracted from in-vivo 2D cine and time-resolved 2D phase-contrast data. Computational fluid dynamics is used to obtain 4D velocity and turbulence fields to simulate MR signals using multipoint 4D flow tensor MRI protocols, which are reconstructed into velocity and turbulence maps with a Bayesian approach. As a result, realistic paired data of ground truth and their projection into MR images enable assessing accuracy and precision of encoding and inference, training of inference machines and, ultimately, deriving optimal experimental designs.
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