Here we give a short summary of the competing models. Additionally, Table 2 provides a summary of their key characteristics. More details are included in the Appendix. Ramirez‐Manzanares: a dictionary‐based technique that accounts for multiple fibre bundles and models the distribution of tissue properties (axon radius, parallel diffusivity) and the orientation dispersion of fibres. Nilsson: a multi‐compartment model that models isotropic, hindered and restricted diffusion and accounts for varying (T 1, T 2) relaxation times for each compartment.58 Scherrer a multi‐compartment model in which each compartment is modelled by a statistical distribution of 3‐D tensors.16 Ferizi1 and Ferizi2: two three‐compartment models that account for varying T 2 relaxation times for each compartment. As regards the intracellular compartment, Ferizi1 models the orientation dispersion by using dispersed sticks as one compartment; Ferizi2 uses a single radius cylinder instead.42 Poot: a three‐compartment model comprising an isotropic diffusion compartment, a tensor compartment and a model‐free compartment in which an Apparent Diffusion Coefficient (ADC) is estimated for each direction independently. T 2 relaxation times are also estimated for each compartment.59 Rokem: a combination of the sparse fascicle model43 with restriction spectrum imaging60 that describes the signal arising from a multi‐compartment model in a densely sampled spherical grid, using L1 regularization to enforce sparsity. Eufracio: an extension of the Diffusion Basis Function (DBF) model that accounts for multiple b‐value shells. Loya‐Olivas1 and Loya‐Olivas2: two models based on the Linear Acceleration of Sparse and Adaptive Diffusion Dictionary (LASADD) technique. Loya‐Olivas1 uses the DBF signal model, while Loya‐Olivas2 uses a three‐compartment tissue model. The optimization uses linearized signal models to speed up computation and sparseness constraints to regularize. Alipoor: a model of fourth‐order tensors, corrected for T 2‐relaxation across different shells. A robust LS fitting was applied to mitigate influence of outliers. Sakaie: a two‐compartment model of restricted and hindered diffusion with angular variation. A simple exclusion scheme based on the b=0 signal intensity was applied to remove outliers. Fick: a spatio‐temporal signal model to represent 3‐D diffusion signal simultaneously over varying diffusion time. Laplacian regularization was applied during the fitting.61 Rivera: a regularized linear regression model of diffusion encoding variables. This is intentionally built as a simplistic model to be used as a baseline for model comparison.