Nonparametric distributions of cell sizes or diffusion tensors have recently been applied to analyze clinically relevant data acquired with advanced diffusion encoding schemes building on oscillating gradients, targeting the frequency-dependence and cell size, or more general q-vector trajectories focusing on the tensorial aspects. We introduce nonparametric D(ω)-distributions as a joint analysis framework taking both frequency-dependence and tensorial properties into account, and demonstrate the approach with ex vivo rat brain data acquired with gradient waveforms exploring the relevant dimensions of the tensor-valued encoding spectrum b(ω).
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