Arvind Balachandrasekaran

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We propose a structured low rank matrix completion algorithm to recover a time series of images consisting of linear combination of exponential parameters at every pixel, from undersampled Fourier measurements. The spatial smoothness of these parameters is exploited along with the exponential structure of the time series at every pixel, to derive an(More)
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(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)
We introduce a structured low rank matrix completion algorithm to recover a series of images from their undersampled measurements, where the signal along the parameter dimension at every pixel is described by a linear combination of exponentials. We exploit the exponential behavior of the signal at every pixel, along with the spatial smoothness of the(More)
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