Dictionary-free MRI parameter estimation via kernel ridge regression

Abstract

MRI parameter quantification has diverse applications, but likelihood-based methods typically require nonconvex optimization due to nonlinear signal models. To avoid expensive grid searches used in prior works, we propose to learn a nonlinear estimator from simulated training examples and (approximate) kernel ridge regression. As proof of concept, we apply kernel-based estimation to quantify six parameters per voxel describing the steady-state magnetization dynamics of two water compartments from simulated data. In relevant regions of fast-relaxing compartmental fraction estimates, kernel estimation achieves comparable mean-squared error as grid search, with dramatically reduced computation.