The apparent diffusion coefficient is a powerful imaging biomarker, sensitive to microstructure properties, and possessing excellent repeatability. Exclusion of perfusion influence (b<150 s.mm-2) reflects true diffusivity, although fewer data points reduces precision, and thus repeatability. We investigate repeatability of experimental breast diffusion data, and show an unexpected increased repeatability with low b-value data inclusion, in contrast to simulated data. This indicates that experimentally-acquired low b-values contains additional noise, perhaps modulated by the non-Gaussianity of the underlying diffusion processes, that decreases diffusion modelling repeatability independent of the true diffusion curve, and should be considered as part of the analysis strategy.
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