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Abstract #1047

Denoising  of diffusion MRI data using Random Matrix Theory

Jelle Veraart1,2, Dmitry S. Novikov2, Jan Sijbers1, and Els Fieremans2

1iMinds Vision Lab, University of Antwerp, Antwerp, Belgium, 2Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States

We here adopt the idea of noise removal by means of transforming redundant data into the Principal Component Analysis (PCA) domain and preserving only the components that contribute to the signal to denoise diffusion MRI (dMRI) data. We objectify the threshold on the PCA eigenvalues for denoising by exploiting the fact that the noise-only eigenvalues are expected to obey the universal Marchenko-Pastur (MP) distribution. By doing so, we design a selective denoising technique that reduces signal fluctuations solely rooting in thermal noise, not in fine anatomical details.

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