Real-time spiral phase contrast MR is practical for free-breathing assessment of flow. However, for sufficient spatial and temporal resolutions it requires high acceleration rates leading to long reconstruction times. Here we propose to train a 3D U-Net with complex convolutions to accurately reconstruct phase data and flow curves from highly undersampled data. Prospectively acquired in-vivo data were reconstructed with similar image quality but ~4.6x faster than compressed sensing reconstructions which could improve workflow.
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