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Abstract #3442

Denoising MR Fingerprinting by matching against General Noise Model at 0.55 T

Ruogu Matthew Zhu1, Nicole Seiberlich2, and Yun Jiang2
1Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, United States, 2Department of Radiology, University of Michigan, Ann Arbor, MI, United States


Low SNR is a challenge for magnetic resonance fingerprinting (MRF) at low-field (0.55 T). In this work, we apply a locally low rank denoising method based on elimination of noise-only principal components according to the Marchenko-Pastur distribution to MRF data. We show that this method is effective at denoising both phantom and in vivo MRF images.

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