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Purpose  Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, it has been shown recently that, in the general Standard Model, parameter estimation from dMRI data is ill‐conditioned even when very high b‐values are applied. We analyze this issue for the Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from single diffusion encoding (SDE) to double diffusion encoding (DDE) resolves the ill‐posedness for intermediate diffusion weightings, producing an increase in accuracy and precision of the parameter estimation.    Methods  We analyze theoretically the cumulant expansion up to fourth order in b of SDE and DDE signals. Additionally, we perform  in silico  experiments to compare SDE and DDE capabilities under similar noise conditions.    Results  We prove analytically that DDE provides invariant information non‐accessible from SDE, which makes the NODDIDA parameter estimation injective. The  in silico  experiments show that DDE reduces the bias and mean square error of the estimation along the whole feasible region of 5D model parameter space.    Conclusions  DDE adds additional information for estimating the model parameters, unexplored by SDE. We show, as an example, that this is sufficient to solve the previously reported degeneracies in the NODDIDA model parameter estimation.

Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding