We propose a Bayesian approach with built-in parameter estimation to perform T2* mapping from undersampled k-space measurements. Compared to conventional regularization-based approaches that require manual parameter tuning, the proposed approach treats the parameter as random variables and jointly recovers them with T2* map. Additionally, the estimated parameters are adaptive to each dataset, this allows us to achieve better performances than regularization-based approaches where the parameters are fixed after the tuning process. Experiments show that our approach outperforms the state-of-the-art l1-norm minimization approach, especially in the low-sampling-rate regime.
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