Joint reconstruction of 2-D left ventricular displacement and contours from tagged magnetic resonance images using Markov random field edge prior

Abstract

Magnetic Resonance (MR) tagging has been shown to be a useful method for non-invasively measuring the deformation of the left ventricle (LV), during the cardiac cycle. By reconstructing a displacement field based on the movement of the tag lines, one can compute myocardial contraction measures such as strain. Existing methods depend on user-defined LV contours, which require human intervention and are therefore the biggest bottleneck in the reconstruction process. Here, the authors present a method for reconstructing 2-D LV deformation without user-defined contours. They use a compound Gauss-Markov random field to model the 2-D vector displacement field, which is parameterized by two closed and smooth contours. By iteratively optimizing the contours, the displacement field, and the parameters, the authors obtain an estimate of the displacement field and the contours. Experimental results on in vivo human data are presented that demonstrate the accuracy of the authors' algorithm.