PurposeThe adoption of multi-channel compressed sensing (CS) for clinical MRI hinges on the ability to accurately reconstruct images from an under-sampled dataset in a reasonable time frame. When CS is combined with SENSE parallel imaging, reconstruction can be computationally intensive. As an alternative to iterative methods that repetitively evaluate a forward CS+SENSE model, we introduce a technique for the fast computation of a compact inverse model solution.
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