Keywords: MR Fingerprinting/Synthetic MR, Machine Learning/Artificial Intelligence, B0 and B1 correctionMagnetization transfer contrast MR fingerprinting (MTC-MRF) enables fast reconstruction of free bulk water and semisolid macromolecules parameters. However, B0 and B1 inhomogeneities that originate from system imperfection can corrupt MR fingerprints, thereby impairing the tissue quantification. We proposed a fast, deep-learning MTC-MRF technique that simultaneously estimates multiple tissue parameters and corrects the effect of B0 and B1 variations. An only-train-once recurrent neural network was designed to perform the fast tissue parameter quantification regardless of MRF acquisition schedule. This allows a dynamic scan-wise linear calibration of the scan parameters using the measured B0 and B1 maps.
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