Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls

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q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (2015, methodology): “In the case of denoising and q-DL-R, model parameters are estimated from the reconstructed DWIs by standard model fitting [3] for comparison.”
Methodological considerations on tract-based spatial statistics (TBSS) (2014, methodology): “The tensors were estimated with the weighted linear least squares approach (Veraart et al., 2013).”
Denoising of diffusion MRI using random matrix theory (2016, methodology): “Both the FA and MD were calculated from the eigenvalues of the diffusion tensors, which were estimated from dMRI data with b ≤ 1ms/μ m 2 with Diffusion Tensor Imaging (DTI) (Basser et al., 1994) using the weighed linear least squares estimator (Veraart et al., 2013b).”
Decomposing the role of alpha oscillations during brain maturation (2020, methodology): “, 2015), tensor fitting, and extraction of diffusivity measures using weighted linear least squares estimation was applied (Fieremans et al., 2011; Veraart et al., 2011; Veraart et al., 2013).”