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Abstract #1199

Low-rank plus sparse tensor reconstruction for high-dimensional cardiac MRI

Rebecca Ramb1, Michael Zenge2, Li Feng1, Matthew Muckley1, Christoph Forman3, Leon Axel1, Dan Sodickson1, and Ricardo Otazo1

1Department of Radiology, New York University School of Medicine, New York, NY, United States, 2Siemens Medical Solutions USA, Inc., Malvern, PA, United States, 3Siemens Healthcare GmbH, Erlangen, Germany

The recently proposed general low rank tensor framework enabled a paradigm change, where data acquisition and image reconstruction are represented in a higher-dimensional space. The overall data space is sampled only as different states randomly coincide, which leads to data gaps. These gaps can introduce challenges in spatiotemporal fidelity for only low-rank- or only sparsity-based reconstructions. Here, a $$$\mathcal{L}+\mathcal{S}$$$ tensor decomposition is investigated, which offers a more robust solution as the sparse component captures updates on top of the overall dynamics represented in the low-rank component. A free-breathing, T1-sensitive cardiac MRI with real-time Cartesian data acquisition over multiple cardiac and inversion recovery phases is employed to investigate potentials for comprehensive cardiac MRI, including for instance late gadolinium scar cine imaging.

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