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)
ACKNOWLEDGEMENTS First of all I would like to express my sincere gratitude to my supervisor Dr. Mathews Jacob for his contribution and support throughout my Master's study. He has always given me constructive and effective guidance in my research and inspired me with brilliant ideas. He is always there to listen to my ideas and to share his advice. I(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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