Model-based T1 Quantification Methods and their Acceleration Potential for the Variable Flip Angle Approach

Quantitative Magnetic Resonance Imaging (qMRI) techniques aim at generating images with absolute values independent of the used measurement protocol. Most T1 quantification methods suffer from long acquisition times and methods allowing for faster T1 mapping are subject of current research. Model-based Reconstruction (MBR) is a promising method in terms of scan time reduction and accuracy of fit. The present work analyzes model-based T1 quantification methods based on the Variable Flip Angle (VFA) model in terms of their stability to different scanning scenarios, focusing especially on their acceleration potential. Working on either image or k-space data an Iterative Regularized Gauss-Newton (IRGN)-framework is used for the solution of the problem. T1 estimates are in overall good agreement with reference values for numerical phantom and in vivo data. Superiority of TGV Frobenius regularization to other regularization functionals is shown in numerical simulations and Acceleration Factors (AFs) up to 19.7 are achieved using the proposed IRGN-TGV Frobenius reconstruction on in vivo data. The inclusion of other signal models could be subject of further investigation.