Parameter degeneracies and numerical fitting issues are the bane of diffusion MRI modelling. In this work, we present a framework using convex optimization to fit diffusion MRI models efficiently similarly to magnetic resonance fingerprinting. We also show how the singular value decomposition of these models can help to visualize how well the parameter space of a given model is sampled by a given acquisition protocol. Results on b-tensor encoded datasets and datasets leveraging multiple echo times literally show how these additional measurement dimensions disentangle model parameters better compared to traditional sequences.
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