Problem summary: Brain MR elastography is associated with extended acquisition times, which is alleviated by reduced coverage or resolution. The aim of this project is to utilize deep learning to accelerate MRE. Methods: We employ an MRE for fully sampled acquisition. Undersampled data is simulated by masking phase-encoding steps. A cascaded reconstruction network is used to reconstruct the phase image from undersampled k-space. Results: There are subtle differences between the reconstructed and fully sampled phase images. We observe a non-significant difference for stiffness values in our preliminary results. Conclusions: The method shows promise for accelerating MR elastography data.
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