Parameter estimation with matrix-based signal models using VARPRO for transient- and steady-state imaging

VARPRO-based parameter estimation is extensively used in MR for its improved accuracy, precision, and convergence behavior compared to general nonlinear least-squares algorithms. This study investigates the feasibility of using matrix-based signal models with VARPRO instead of conventional analytic signal equations. Simulations and in-vivo study show that VARPRO with matrix-based signal models is identical to VARPRO with analytic signal equations, and both VARPRO approaches provide enhanced precision and accuracy in relaxometry maps compared to the conventional DESPOT1/2 methods from variable flip angle SPGR and bSSFP measurements.