“… ( Li et al, 2018 ) | SMT | gradients | The recommended number of uniformly distributed diffusion-sensitizing gradients defined in terms of CV < 0.05 to a reference is given by 10 × b / b 1 ( b 1 = 1000 s/mm 2 ) for a typical SNR ∼ 20 measured at the baseline. Decreasing the number of gradients increases at an exponential rate the CV of the signal |
( Pieciak et al, 2019 ) | MAP-MRI, MAPL | Gradients | Decreasing the number of diffusion-sensitizing gradients increases the mean relative error (up to 20% for 10% measurements out of 270) and the standard deviation of the RTOP both for the MAP-MRI and MAPL. The mean relative error of the RTOP increases approximately linearly in the function of the subsampled gradients |
( Aja-Fernández et al, 2020 ) | MAP-MRI, MAPL, RBFs | Shells, gradients, b -value | Increasing the maximal b -value parameter (i.e., number of shells and gradients) leads to the amplification of microstructural-related measures (RTOP, RTAP, RTPP) independently of the technique used to estimate the EAP function (MAP-MRI, MAPL, RBFs) |
Modalities: QBI – Q-ball imaging, SD – spherical deconvolution, NODDI – neurite orientation dispersion and density imaging, DKI – diffusion kurtosis imaging, 3CM – three-compartment model, SMT – spherical mean technique, MAP-MRI – mean apparent propagator MRI, MAPL – Laplacian MAP, RBFs – radial basis functions, 3D-SHORE – three-dimensional simple harmonic oscillator based reconstruction and estimation, AMICO – accelerated microstructure imaging via convex optimization.…”