Identification of myocardial tags in tagged MR images without prior knowledge of myocardial contours

Abstract

Magnetic resonance (MR) tagging has been shown to be a useful technique for non-invasively measuring the deformation of an in vivo heart. An important step in analyzing tagged images is the identification of tag lines in each image of a cine sequence. Most existing tag identification algorithms require prior knowledge of the myocardial contours. Contour identification, however, is time consuming and requires a considerable amount of user intervention. In this paper, a new method identifying tag lines is presented that does not require prior knowledge of the myocardial contours. The method is composed of three stages. First the tags are estimated across the entire region-of-interest (ROI) with a snake algorithm based on a maximum-likelihood (ML) estimate of the tag center. Next a maximum a posteriori (MAP) hypothesis test is used to detect tag centers inside the myocardium. Finally a pruning algorithm is used to remove detected tag line centers that do not meet a spatio-temporal continuity criterion. This method is demonstrated on data from an in vivo human heart.