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Abstract #0108

Patch2Self denoising reveals a new theoretical understanding of Diffusion MRI

Shreyas Fadnavis1, Joshua Batson2, and Eleftherios Garyfallidis3
1Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States, 2Chan Zuckerberg Biohub, San Francisco, CA, United States, 3Indiana University Bloomington, Bloomington, IN, United States

Diffusion MRI (dMRI) is a promising tool for evaluating the spinal cord in health and disease, however low SNR can impede accurate, repeatable, quantitative measurements. Here, we apply a recently proposed denoiser, Patch2Self, that strictly suppresses statistically independent random fluctuations in the signal originating from various sources of noise. Typical spinal cord dMRI scans have a smaller number of gradient directions (10-20) making PCA based 4D denoisers (require at least 30) inapplicable. Using self-supervised learning, Patch2Self addresses these issues which we quantitatively show with an improvement in repeatability and conspicuity of pathology in the spinal cord.

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