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Abstract #3464

Patch2Self denoising of diffusion MRI in the cervical spinal cord improves repeatability and feature conspicuity

Kurt G. Schilling1,2, Shreyas Fadnavis3, Mereze Visagie2, Eleftherios Garyfallidis3, Bennett A. Landman2,4, Seth A. Smith1,2, and Kristin P. O'Grady1,2
1Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, 2Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States, 3Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States, 4Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 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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