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Abstract #4057

Two-Step Semi-Supervised Denoising for Low-field Diffusion MRI

Jo Schlemper1, Neel Dey2, Seyed Sadegh Mohseni Salehi1, Kevin Sheth3, W. Taylor Kimberly4, and Michal Sofka1
1Hyperfine, Guilford, CT, United States, 2New York University, New York City, NY, United States, 3Yale University, New Haven, CT, United States, 4Massachusetts General Hospital, Boston, MA, United States


In clinical low-field MRI, prolonged data acquisition is impractical, limiting the achievable SNR during imaging. In the absence of ground truth, unsupervised denoising is desirable, but many of them underperform on correlated noise structure of reconstructed MR images. In this work, we present an effective two step training framework for removing correlated MR noise without ground truth. We demonstrate that the proposed approach outperforms the existing denoising methods when applied to the low-field (64mT) diffusion-weighted images and demonstrate that significant noise reduction is possible. 82.5% of processed images were expertly rated clearly/far better overall.

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