It depends. If one relies on a biophysical model, the associated constraints may limit the benefit of combining the complementary pieces of diffusion information yielded by multidimensional diffusion MRI acquisitions. However, it seems that these acquisitions can
alleviate degeneracy in certain models
make fiber dispersion estimates more robust to model inaccuracies
improve accuracy on parameter estimation
in certain cases.
When considering cumulant expansions of the signal, it has been demonstrated that at least two b-tensor shapes are required for unique estimation of the covariance tensor in q-space trajectory imaging and that non-axisymmetric b-tensors are required for unique estimation of the skewness tensor in diffusion skewness tensor imaging. Also, diffusional variance decomposition relies on the use of two b-shapes to disentangle the two sources of diffusion kurtosis appearing in the deviations from mono-exponential behavior of the powder-averaged tensor-valued diffusion encoded signal.
Finally, for less constrained approaches such as diffusion tensor distribution imaging, multidimensional diffusion MRI acquisitions provide more robust inversions, as indicated in previous work (see Figure 1 here for instance). This specific technique retrieves nonparametric distributions of axisymmetric diffusion tensors. Since such tensors are described by four numbers (isotropic diffusivity, normalized anisotropy, two angles for orientation), it makes sense to use an acquisition scheme comprising four tunable parameters to estimate these numbers. B-tensors do feature four tunable parameters, i.e. the b-value, the b-shape and two angles for orientation, matching the number of "diffusion unknowns".