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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