NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRI ${T}_{1}$ Mapping

In quantitative magnetic resonance T₁ mapping, the variable flip angle (VFA) steady state spoiled gradient recalled echo (SPGR) imaging technique is popular as it provides a series of high resolution T₁ weighted images in a clinically feasible time. Fast, linear methods that estimate T₁ maps from these weighted images have been proposed, such as DESPOT1 and iterative re-weighted linear least squares. More accurate, non-linear least squares (NLLS) estimators are in play, but these are generally much slower and require careful initialization. In this paper, we present NOVIFAST, a novel NLLS-based algorithm specifically tailored to VFA SPGR T₁ mapping. By exploiting the particular structure of the SPGR model, a computationally efficient, yet accurate and precise T₁ map estimator is derived. Simulation and in vivo human brain experiments demonstrate a twenty-fold speed gain of NOVIFAST compared with conventional gradient-based NLLS estimators while maintaining a high precision and accuracy. Moreover, NOVIFAST is eight times faster than the efficient implementations of the variable projection (VARPRO) method. Furthermore, NOVIFAST is shown to be robust against initialization.