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Abstract #2190

Unsupervised Denoising for Low-field Diffusion MRI

Jo Schlemper1, Neel Dey2, Seyed Sadegh Mohseni Salehi1, Carole Lazarus1, Rafael O'Halloran1, Prantik Kundu1,3, and Michal Sofka1
1Hyperfine Research Inc., Guilford, CT, United States, 2New York University, New York, NY, United States, 3Icahn School of Medicine at Mount Sinai, New York, NY, United States

An unsupervised deep learning framework is proposed for denoising low-field 64 mT diffusion-weighted MRI images (DWI). The denoised DWI (b-value = 890 s/mm2) and apparent diffusion coefficient (ADC) maps were evaluated in a user study by four expert graders in terms of sharpness, noise reduction, and overall utility. Our framework was found to enable low-field DWI restoration with strong noise while maintaining relevant image features. 62.50% and 64.28% of processed images were rated clearly/far better overall for DWI and ADC, respectively, with only 0.05% of processed DWI and 0% of processed ADC rated clearly/far worse.

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