Keywords: MR Fingerprinting/Synthetic MR, MR FingerprintingTraditional MR fingerprinting involves matching the acquired signal evolutions against a dictionary of expected tissue fingerprints to obtain the corresponding tissue parameters. Since this dictionary is essentially a discrete representation of a physical model and the matching process amounts to brute-force search in a discretized parameter space, there arises a tradeoff between discretization error and parameter estimation time. In this work, we investigate this tradeoff and show via numerical simulation how a neural net-based approach solves it. We additionally conduct a phantom study using 1.5T and 3T data to demonstrate the consistency of neural net-based estimation with dictionary matching.
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