An imaging analytic is proposed that efficiently reconstruct high-resolution 4D MR images using GPU computing. Modeling k-space data low dimensionality with low PARAFAC rank of tensors, the correlation across different dimensions are captured via tensor subspaces, sequentially learned from the subsampled data, to impute the missing k-space entries. The novel analytics gain considerable computational saving relative to the state-of-the-art compressive sampling schemes, while achieving failry similar image quality.
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