An MR fingerprinting (MRF) dictionary can be difficult to generate, especially when the dictionary calculation involves complicated physics. We present a new method, named MRF-GAN, based on the generative adversarial network (GAN) to create MR fingerprints. We demonstrate that MRF-GAN can generate accurate MRF fingerprints and the associated in vivo MRF maps comparing to the conventional MRF dictionary. Moreover, it can significantly reduce the dictionary generation time which opens the door to rapid calculation and optimization of MRF dictionaries with more complex physics.
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