Accelerated MR parameter mapping using compressed sensing with model-based sparsifying transform

Introduction:The estimation of MR parameters, such as the relaxation times T1, T2and diffusion coefficients D, requires the acquisition of multipleimages at different sequence parameters, which is often associated with long acquisition times. These data show a high temporalcorrelation, whichcan be described by a model facilitating accelerated image acquisition by data undersampling as shown in [1]. Recently, Compressed Sensing (CS)[2-4] was demonstrated for image reconstruction from incomplete k-space data. In this work we show that prior knowledge aboutthe data can beused to define a model-based sparsity transform for improved CS reconstruction for MR parameter estimation.