While compressed sensing is a proven method for highly accelerating cardiovascular MRI, its lengthy reconstruction time hinders clinical translation. Deep learning is a promising method to accelerate reconstruction processing. We propose a generative adversarial network (GAN) with optimal loss terms for rapid reconstruction of 28.8-fold accelerated real-time phase-contrast MRI. Our results show that GAN reconstructs images 613 times faster than compressed sensing without significant loss in peak and mean velocity measurements and image sharpness.
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