Regularized estimation of flow patterns in MR velocimetry

A Bayesian estimator of the magnetic resonance (MR) velocity image is proposed. It is based on a Markov model accounting for the spatial structure of the flow velocity. On the other hand, low MR signal intensity yields high uncertainty on the velocity. Such an important property is taken into account through the observation model. The resulting posterior likelihood is optimized using an iterative coordinate descent (ICD) algorithm. Compared to the usual least squares solution, simulation results on flows with parabolic and flat profiles demonstrate a significant gain of in the mean square error.