Diffusion MRI measurements are affected by noise which propagates into the model estimation. Residual bootstrapping has been proposed to assess the uncertainty of parameter estimates, but do not consider the confounding effect of measurement outliers (e.g. due to subject motion), limiting their usage on clinical data. We present a robust bootstrapping algorithm and demonstrate its performance on the estimation and uncertainty quantification of rotationally invariant spherical harmonic (RISH) features with simulations and clinical data. Our algorithm can improve the reliability of cross-scanner harmonization relying on RISH and probabilistic tractography.
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