Table 2.

Summary of the various diffusion models evaluated. Tissue models are models that include an explicit description of the underlying tissue microstructure with a multi‐compartment approach. In contrast, signal models focus on describing the DW signal attenuation without explicitly describing the underlying tissue and instead correspond to a ‘signal processing’ approach

	Type of model	Nb of free param. (genu/fornix)	Models effect of δ and Δ	Noise assumption	Optimization algorithm	Outliers strategy	Special signal prediction strategy
R–Manzanares	Tissue	N/A	Yes	Gaussian	weighted‐LS	Yes	CV
					bootstrapping
Nilsson	Tissue	< 12/12	Yes	Gaussian	LM	Yes	CV
Scherrer	Tissue	10/16	No	Gaussian	Bobyqa	Yes	No
Ferizi_1	Tissue	< 12/12	Yes	approx.‐Rician	LM	No	No
Ferizi_2	Tissue	< 10/10	Yes	approx.‐Rician	LM	No	No
Alipoor	Signal	17/17	No	Gaussian	weighted‐LS	Yes	No
Sakaie	Signal	N/A	No	Gaussian	nonlinear‐LS	Yes	No
Rokem	Tissue	∼20	No	Gaussian	Elastic net	No	CV
				+ Noise floor
Eufracio	Tissue	7/7	No	Gaussian	bounded‐LS	No	No
					Lasso, Ridge
Loya‐Olivas_1	Tissue	11	No	Gaussian	bounded‐LS	No	No
					& Lasso
Loya‐Olivas_2	Tissue	11	No	Gaussian	bounded‐LS	No	No
Poot	Signal	103	No	Rician	LM‐like	No	No
Fick	Signal	475	Yes	Gaussian	Laplacian‐ reg‐LS	No	partial‐CV
Rivera	Signal	23	Yes	Gaussian	Weighted Lasso	Yes	CV

Abbreviations: LS=least‐squares, LM=Levenberg–Marquardt, CV=cross‐validation, reg=regularized