Unsupervised estimation of left ventricular displacement from MR tagged images using Markov random field edge priors

Tagged magnetic resonance (MR) imaging has been shown to be a useful means for non-invasive measurement of the deformation of the left ventricle (LV) during the cardiac cycle. One can estimate a dense displacement field based on the tag line movements and use it to compute myocardial measures such as strain. Existing methods require that the boundaries of the LV be known before the displacement field is estimated, which results in a time-consuming process since user intervention is needed for the estimation of the boundaries. In this paper, the authors propose a method that jointly estimates the LV boundaries and displacement field without user-intervention. They model the displacement field as a compound Gauss-Markov random field (CGMRF) which is parameterized by two closed and smooth contours. A partial-optimal solution is sought by iteratively updating the displacement field, the contours and the parameters. Experimental results on both simulated vector image and in vivo human data show that the authors´ method is capable of automatically tracking the contours and reconstructing displacement field with a decent accuracy