Keywords: Data Processing, Diffusion/other diffusion imaging techniques, Harmonization Denoising
To address the ongoing reproducibility crisis in quantitative diffusion MRI (dMRI), efforts are underway to harmonize and improve precision of diffusion parameter estimation. Using inter- and intra-scanner test-retest higher order dMRI, we compare the reproducibility of two popular harmonization methods, ComBat and linear-RISH, to that of denoising using MPPCA on complex-valued dMRI. We find that denoising combined with harmonization improves voxel-wise test-retest ICC by up to 60% compared to harmonization alone. Using dMRI at different voxel sizes, we find that denoising reduces the bias due to varying noise floors more accurately than harmonization. Denoising appears essential to harmonize dMRI datasets.
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