Accelerated MR Parameter Mapping Using Compressed Sensing with Model-Based Sparsifying Transform

Mariya Doneva¹, Julien Sngas², Peter Brnert², Holger Eggers², Alfred Mertins¹

The estimation of MR parameters, such as the relaxation times T1, T2 and diffusion coefficients D, requires the acquisition of multiple images at different sequence parameters, which is often associated with long acquisition times. These data show a high temporal correlation, which could be described by a model facilitating accelerated image acquisition by data undersampling. In this work we show that the prior knowledge about the data could be used to define a model-based sparsity transform for improved compressed sensing reconstruction for MR parameter estimation.