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Abstract #4397

Generalized sensitivity functions for optimal b-value selection in diffusion-based MRI modeling

Umberto Villani1,2, Simona Schiavi3, and Alessandra Bertoldo1,2
1Padova Neuroscience Center, University of Padova, Padova, Italy, 2Department of Information Engineering, University of Padova, Padova, Italy, 3Department of Computer Science, University of Verona, Verona, Italy

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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