4D-Flow MRI is a valuable technique for quantifying cardiovascular hemodynamics in the aorta; however, it suffers from manual off-line post processing. To address this, we integrated our custom deep learning tools for automatic 4D-Flow processing within the on-scanner reconstruction environment through Siemen’s Framework for Image Reconstruction (FIRE) interface. We retrospectively reconstructed raw data from 10 patients with aortic dilation, valve repair and/or aneurysm as well as one, prospectively recruited, control on scanner. Our deep learning tools ran successfully, and an aortic velocity maximum intensity projection cine was generated and sent to the scanner’s console alongside the reconstructed 4D-flow.
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