Fitting the diffusion kurtosis imaging free water elimination model (DKI-FWE) to diffusion MRI data represents an ill-conditioned problem. Fortunately, the conditioning of the model fitting can be improved by explicitly modeling the T2 relaxation dependency of the signal. As a benefit, diffusion and kurtosis metrics robust to partial volume effects can be estimated with conventional techniques. In this work, we use Cramér-Rao lower bound (CRLB) theory to identify optimal acquisition settings that maximize the precision of the model parameter estimates.
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