Arvind Balachandrasekaran

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We introduce a fast structured low-rank matrix completion algorithm with low memory & computational demand to recover the dynamic MRI data from undersampled measurements. The 3-D dataset is modeled as a piecewise smooth signal, whose discontinuities are localized to the zero sets of a bandlimited function. We show that a structured matrix corresponding to(More)
We introduce a self-expressiveness prior to exploit the redundancies between voxel profiles in dynamic MRI. Specifically, we express the temporal profile of each voxel in the dataset as a sparse linear combination of temporal profiles of other voxels. This scheme can be thought of as a direct approach to exploit the inter-voxel redundancies as opposed to(More)
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