While magnetic resonance elastography (MRE) provides a non-invasive method of estimating tissue stiffness, which is indicative of a variety of diseases, MRE scans typically require lengthy breath-holds which are prohibitive for many patients. We have extended a recently proposed Bayesian imaging method, called ReVEAL, for MRE. This method is capable of reconstructing images from highly undersampled data by leveraging both sparsity and the near equal magnitude across multiple offsets, inherent to MRE acquisition, as reconstruction constraints. This reconstruction method is validated against SENSE using a retrospectively downsampled phantom dataset and three retrospectively downsampled in vivo liver datasets.
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