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Abstract #2736

Multi-Slice GRE-MOLLI at 3T using Denoising with Low-Rank and Sparsity Constraints

Paul Kyu Han1,2, Chao Ma1,2, Nicolas Guehl1,2, Nathaniel Alpert1,2, Marc Normandin1,2, and Georges El Fakhri1,2

1Radiology, Massachusetts General Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States

Modified Look-Locker inversion recovery (MOLLI) uses bSSFP readout due to its high SNR, however, bSSFP is sensitive to off-resonance effects which result in banding artifacts. Recently, GRE has been proposed as an alternative readout for MOLLI, however, the low SNR efficiency of GRE-MOLLI is still a major problem. In this work, we propose to use a denoising reconstruction framework with low-rank and sparsity constraints to improve the low SNR of GRE-MOLLI. The proposed denoising method improved the low SNR of GRE-MOLLI, and multi-slice GRE-MOLLI is feasible for artifact-free T1 mapping with wider spatial coverage at high magnetic fields ($$$\geq 3T$$$).

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