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

Synopsis

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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