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Abstract #4683

Densely-overlapped locally low-rank algorithms outperform conventional locally low-rank algorithms for accelerating parametric mapping

Chenxi Hu1, Fan Yang1, Xin Tang1, Zhiyong Zhang1, and Dana Peters2
1Shanghai Jiao Tong University, Xuhui, China, 2Yale University, New Haven, CT, United States


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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