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Abstract #0277

DSLR+: Enhancing deep subspace learning reconstruction for high-dimensional MRI 

Christopher Michael Sandino1, Frank Ong2, Ke Wang3, Michael Lustig3, and Shreyas Vasanawala2
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA, United States

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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