Abstract #3464

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

Kurt G. Schilling^1,2, Shreyas Fadnavis³, Mereze Visagie², Eleftherios Garyfallidis³, Bennett A. Landman^2,4, Seth A. Smith^1,2, and Kristin P. O'Grady^1,2

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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