  Purpose: This work proposes a novel approach to efficiently generate MR
fingerprints for MR fingerprinting (MRF) problems based on the unsupervised
deep learning model generative adversarial networks (GAN). Methods: The GAN
model is adopted and modified for better convergence and performance, resulting
in an MRF specific model named GAN-MRF. The GAN-MRF model is trained,
validated, and tested using different MRF fingerprints simulated from the Bloch
equations with certain MRF sequence. The performance and robustness of the
model are further tested by using in vivo data collected on a 3 Tesla scanner
from a healthy volunteer together with MRF dictionaries with different sizes.
T1, T2 maps are generated and compared quantitatively. Results: The validation
and testing curves for the GAN-MRF model show no evidence of high bias or high
variance problems. The sample MRF fingerprints generated from the trained
GAN-MRF model agree well with the benchmark fingerprints simulated from the
Bloch equations. The in vivo T1, T2 maps generated from the GAN-MRF
fingerprints are in good agreement with those generated from the Bloch
simulated fingerprints, showing good performance and robustness of the proposed
GAN-MRF model. Moreover, the MRF dictionary generation time is reduced from
hours to sub-second for the testing dictionary. Conclusion: The GAN-MRF model
enables a fast and accurate generation of the MRF fingerprints. It
significantly reduces the MRF dictionary generation process and opens the door
for real-time applications and sequence optimization problems.
