  The clinical translation of diffusion MRI (dMRI)-derived quantitative
contrasts hinges on robust reproducibility, minimizing both same-scanner and
cross-scanner variability. This study evaluates the reproducibility of
higher-order diffusion metrics (beyond conventional diffusion tensor imaging),
at the voxel and region-of-interest levels on magnitude and complex-valued dMRI
data, using denoising with and without harmonization. We compared same-scanner,
cross-scanner, and cross-protocol variability for a multi-shell dMRI protocol
in 20 subjects. We first evaluated the effectiveness of denoising strategies
for both magnitude and complex data to mitigate noise-induced bias and
variance, to improve dMRI parametric maps and reproducibility. We examined the
impact of denoising under different analysis approaches, comparing voxel-wise
and region of interest (ROI)-based methods. We also evaluated the role of
denoising when harmonizing dMRI across scanners and protocols. DTI and DKI maps
visually improve after MPPCA denoising, with noticeably fewer outliers in
kurtosis maps. Denoising enhances voxel-wise reproducibility, with test-retest
variability of kurtosis indices reduced from 15-20% to 5-10% after denoising.
Complex dMRI denoising reduces the noise floor by up to 60%. In ROI-analyses,
denoising also increased statistical power, with reduction in sample size
requirements by up to 40% for detecting differences across populations.
Combining denoising with linear-RISH harmonization, in voxel-wise assessments,
improved intra-scanner intraclass correlation coefficients for FA from moderate
to excellent repeatability over harmonization alone. The enhancement in data
quality and precision due to denoising facilitates the broader application and
acceptance of these advanced imaging techniques in both clinical practice and
large-scale neuroimaging studies.
