Myocardial T1 mapping using an Instantaneous Signal Loss Simulation modeling and a Bayesian Estimation Method

The Instantaneous Signal Loss Simulation (InSiL) model is a promising alternative to the classical mono-exponential fitting of the Modified Look-Locker Inversion-recovery (MOLLI) sequence in cardiac T 1 mapping applications, which achieves better accuracy and is less sensitive to heart rate (HR) variations. Classical nonlinear least squares (NLLS) estimation methods require some parameters of the model to be fixed a priori in order to give reliable T 1 estimations and avoid outliers. This introduces further bias in the estimation, reducing the advantages provided by the InSiL model. In this paper, a novel Bayesian estimation method using a hierarchical model is proposed to fit the parameters of the InSiL model. The hierarchical Bayesian modeling has a shrinkage effect that works as a regularizer for the estimated values, by pulling spurious estimated values toward the group-mean, hence reducing greatly the number of outliers. Simulations, physical phantoms, and in-vivo human cardiac data have been used to show that this approach estimates accurately all the InSiL parameters, and achieve high precision estimation of the T 1 compared to the classical MOLLI model and NLLS InSiL estimation.