To address limitations with classical SENSE algorithms, we propose Bayesian Sensitivity Encoding (Bayes-SENSE), which obviates the need to tune regularization penalties, provides variance maps that can quantify algorithmic uncertainty, and is easily extendable to multi-contrast reconstruction. Bayes-SENSE is a synergistic combination of SENSE and Bayesian CS (BCS). We adapt recent work accelerating BCS to develop an efficient and highly parallelizable inference algorithm for Bayes-SENSE based on the conjugate gradient (CG) method. We evaluate Bayes-SENSE in several undersampling settings with parallel imaging, and demonstrate that it outperforms L2-/L1-SENSE in terms of reconstruction error while also providing the aforementioned benefits.
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