Generalized sensitivity functions provide insights about which temporal observations are most informative for the estimation of biological model parameters. We formulate the same concept in the dMRI field to investigate how biophysical models/data representations react to HARDI acquisitions of different b-values. This approach handily shows how different parameters feature enhanced estimation precision at different b-values and exposes potential correlations between them, shedding light on possible a posteriori identifiability issues. Requiring only byproducts of standard optimization routines, generalized sensitivity functions can easily be integrated in standard analyses when proposing either a new model or a modification of existing ones.
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