Unrolled neural networks (UNNs) have enabled state-of-the-art reconstruction of dynamic MRI data, however, they remain limited by GPU memory hindering applications to high-resolution, high-dimensional imaging. Previously, we proposed a deep subspace learning reconstruction (DSLR) method to reconstruct low-rank representations of dynamic imaging data. In this work, we present DSLR+, which improves upon DSLR by leveraging a locally low-rank model and a more accurate data consistency module. We demonstrate improvements over state-of-the-art UNNs with respect to 2D cardiac cine image quality and reconstruction memory footprint, which is greatly reduced by reconstructing compressed representations of the data instead of the data itself.
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