Numerous applications in diffusion MRI involve computing the orientationally-averaged diffusion-weighted signal. Most approaches assume that the gradient vectors are uniformly distributed on a sphere, computing the orientationally-averaged signal through arithmetic averaging. One challenge is that not all acquisition schemes have gradient vectors distributed over perfect spheres. Alternative averaging methods include: weighted signal averaging; spherical harmonic; and Mean Apparent Propagator MRI (MAP-MRI). Here, these methods are compared under different signal-to-noise (SNR) realizations. With dense and isotropically-distributed sampling, all methods give comparable results. As the SNR and number of data points are reduced, MAP-MRI-based approaches give pronounced improvements over the other methods.
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