A mutual information-based mathematical framework is developed to quantify the information content of various acquisition parameters and subsampling approaches. A recursive conditional formulation quantifies information content given previous acquisitions. This framework is applied to 3D QALAS. Mutual information between reconstructed M0, T1, and T2 uncertainty and measurement noise is calculated for an in silico phantom and the results applied to measurements on a System Standard Model 130 phantom. Reconstructions from these measurements demonstrate the potential use of information theory in guiding pulse sequence design to maximize reconstruction quality.