A generalized signal model for diffusional kurtosis imaging (DKI) is proposed containing an adjustable parameter that can be optimized to reduced systematic errors in kurtosis estimates. This is illustrated by applying an established tissue model for diffusion in brain to fix the parameter, and numerical simulations are employed to demonstrate the improvement in accuracy relative to kurtosis estimates obtained with the standard DKI signal model. Finally, in vivo brain data is used to compare mean kurtosis estimates obtained with the standard and optimized DKI signal models.
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