The Locally Low-Rank (LLR) constraint has been increasingly used for MR acceleration. Here we compare two strategies for LLR-constrained reconstruction, namely the Non-overlapped LLR (NLLR) and the Densely-overlapped LLR (DLLR) to show their differences. The NLLR strategy has been used by a number of LLR algorithms, including the most well-known POCS algorithm. On the other hand, the DLLR strategy has not been well-recognized as a different strategy, and algorithms able to employ the strategy have only been developed recently. In this work, we show that DLLR is different and superior to NLLR by yielding faster convergence and reduced undersampling artifacts.
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