We propose Bayesian accelerated Compressed Sensing (BaCS) to improve the computational speed of Bayesian CS by two orders of magnitude. We achieve this by circumventing a costly matrix inversion problem using conjugate gradients and Monte Carlo sampling, which lend themselves well to parallel processing using GPUs. Exploiting parallelism renders BaCS even faster than sparseMRI, while having the ability to exploit similarities between multi-contrast images to improve reconstruction performance. Further, we extend BaCS to multi-channel reconstruction by synergistically combining it with SENSE to enable yet higher acceleration rates.
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