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Abstract #0984

Structure preserving noise removal in Hilbert space from ultra-high resolution diffusion MRI data

Gabriel Ramos-Llordén1, Gonzalo Vegas-Sanchez-Ferrero1, Congyu Liao2, Carl-Fredrik Westin1, Kawin Setsompop2, and Yogesh Rathi1
1Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, 2Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States

In-vivo submillimeter resolution diffusion MRI suffers from limited signal-to-noise ratio (SNR) due to the small voxel size. Denoising techniques can improve the SNR and facilitate further dMRI analysis. Among them, perhaps PCA-based (e.g, Marchenko-Pastur PCA) have shown the best performance. In this work, we introduce kernel PCA, a powerful nonlinear generalization of linear PCA to Hilbert spaces that is shown to suppress a substantial amount of noise (which MP-PCA is incapable of) and still reliably preserve dMRI signal. We showcase K-PCA noise removal with 660 micrometer gSlider data, where we compared it qualitatively and qualitatively with MP-PCA.

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