We develop a mutual information-based mathematical framework to quantify the information content of a parameter space composed of several pulse sequence acquisition parameters of interest for model-based image reconstruction. We apply this framework to the signal model for a multi-contrast inversion- and T2-prepared gradient echo sequence. Mutual information between parametric map uncertainty and measured data is determined for variable acquisition parameters to characterize the performance of each acquisition. This framework allows for the strategic selection of synthetic MR acquisition parameters for specific applications and also provides a quantitative understanding of parameter space information content in an acquisition for multi-parameter mapping.
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