Moment-matching is one proposed approach for estimating Standard Model parameters which partly overcomes the issues of the model’s notoriously shallow fitting landscape. The method achieves robustness by matching the model’s moments to the cumulants of the data determined by diffusion kurtosis imaging which is stable and clinically feasible. However, estimates of cumulants generally suffer from bias due to the use of finite b-values. Here, it is demonstrated that this bias propagates to the model-parameter estimates resulting in substantial inaccuracy even for small b-values.
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