DWI acquired with b-values greater than 1000 s/mm2 and higher-order diffusion analyses based on such DWI series have the potential to improve tumor differentiation, while the extended sampling of b-values makes the acquisition time inconveniently long. We propose an acceleration scheme that sparsely samples k-space and reconstructs images using a new low-rank tensor model which exploits both global and local low-rank structure. Under an acceleration factor of 8, parameter mapping results on one simulated and 7 patient datasets show improved accuracy over another low-rank tensor model that exploits global correlation only, and comparable accuracy to clinically used four-fold GRAPPA reconstruction.
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