Information theory optimization of acquisition parameters for improved synthetic MRI reconstruction

Synthetic magnetic resonance imaging (MRI) is a method for obtaining parametric maps of tissue properties from one scan and using these to reconstruct multiple contrast weighted images. This reduces the scan time necessary to produce multiple series of different contrast weightings and potentially provides additional diagnostic utility. For synthetic MRI, current acquisition parameter selection and subsampling approaches (such as variable density Poisson disc sampling) are heuristic in nature. We develop a mutual information-based mathematical framework to quantify the information content of a parameter space composed of k-space and 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. This pulse sequence is modeled for in silico data and used for the acquisition of phantom data. Mutual information between parametric map uncertainty and measured data is determined for variable acquisition parameters to characterize the performance of each acquisition. Mutual information is calculated by Gauss-Hermite quadrature and a global search over acquisition parameter space. We demonstrate the possibility of mutual informationguided subsampling schemes on phantom image data. Fully-sampled images of a silicone gel phantom and a water phantom are acquired on a 3T imager. Subsampling methods are applied to this data before it is reconstructed using the Berkeley Advanced Reconstruction Toolbox (BART). This framework allows for the strategic selection of synthetic MR acquisition parameters and subsampling schemes for specific applications and also provides a quantitative understanding of parameter space information content in an acquisition for multi-parameter mapping.