To which extent the Cramér-Rao bound (CRB) is a reliable benchmark in quantitative MRS?

Following MRS data fitting, absolute or relative Cramér-Rao bounds (CRB and rCRB, respectively) are often computed as indices of parameter uncertainties. However, the unknown true values of parameters are required to compute CRB. Here, we studied the effect of substituting the true values by the noisy estimates on the bounds (noted CRB* and rCRB*). We showed by simulations that both CRB* and rCRB* are particularly sensitive to noise. Also, the mode of rCRB* distribution is left-shifted, which leads to a significant false positive risk when rCRB* is thresholded. A threshold not exceeding 20% is recommended to limit this risk.