The purpose of this work was to develop a method that simultaneously reduces and estimates the uncertainty in the T1 maps obtained with the VFA method while also avoiding the need for any manual tuning of regularization parameters. A Markov Chain Monte Carlo-based algorithm was implemented and evaluated on real and synthetic data. The results show that the method can be used to reduce both noise and noise-induced bias and simultaneously give information about the uncertainty in the estimates.
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