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Abstract #5342

A diffusion-matched principal component analysis (DM-PCA) based denoising procedure for high-resolution diffusion-weighted MRI

Nan-kuei Chen1, Hing-Chiu Chang2, Ali Bilgin3, Adam Bernstein3, and Theodore P Trouard3

1Biomedical Engineering, University of Arizona, Tucson, AZ, United States, 2University of Hong Kong, Hong Kong, Hong Kong, 3University of Arizona, Tucson, AZ, United States

A concern with high-resolution DWI and DTI is the limited SNR. Here we report a new denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of voxels with very similar signal variation patterns along the diffusion dimension, 2) performing PCA along the diffusion dimension for those voxels, and 3) suppressing noisy PCA components. The DM-PCA method performs reliably for input data with a range of SNR and different numbers of diffusion encoding scans, without compromising anatomic resolvability, and should prove highly valuable for imaging studies in research and clinical uses.

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