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Abstract #4344

NoiseFactors: Blind Denoising of dMRI via Randomized Factor Models

Shreyas Fadnavis1, Hu Cheng2, and Eleftherios Garyfallidis1
1Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States, 2Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States

NoiseFactors is a probabilistic graphical model to suppress and remove additive noise in a single DWI image. It mitigates the issues caused by noise by preserving correlations in the signal components and suppressing the uncorrelated noise within local neighbourhoods. We solve the low-rank approximation problem by learning a best m-component approximation of a factor model. To do so we also introduce a novel flipped bi-crossvalidation to estimate the factor model. It outperforms the state-of-the-art PCA based methods such as Marchenko-Pastur PCA and Local PCA. The proposed method for denoising will be made available with an open-source implementation in DIPY.

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