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Abstract #2905

Improved MP-PCA denoising through tensor generalization

Jonas Lynge Olesen1,2, Andrada Ianus3, Noam Shemesh3, and Sune Nørhøj Jespersen1,2
1Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Aarhus University, Aarhus, Denmark, 2Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark, 3Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal


A popular SNR-boosting method in MRI is denoising based on principal component analysis with automated rank estimation by exploiting the Marchenko-Pastur distribution of noise singular values (MP-PCA). MP-PCA operates by reshaping data-patches into matrices to discriminate signal from noise using random matrix theory. Here, we generalize MP-PCA to exploit tensor-structured data arising in, e.g., multi-contrast or multicoil acquisitions, without introducing new assumptions. As proof of concept, we demonstrate a substantial increase in denoising performance in a multi-TE DKI dataset, in particular for small sliding windows. This is beneficial especially in cases of rapidly varying contrast or spatially varying noise.

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