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Abstract #3862

Fast 4D MRI Reconstruction Analytics using Low-Rank Tensor Imputation

Morteza Mardani1, Joseph Cheng1,2, John Pauly1, and Lei Xing1

1Stanford University, Stanford, CA, United States, 2Stanford University, United States

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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