Estimation of 3D myocardial motion from tagged MRI using LDDMM

Non-invasive estimation of regional cardiac function is important for assessment of myocardial contractility. The use of MR tagging technique enables acquisition of intra-myocardial tissue motion by placing a spatially modulated pattern of magnetization whose deformation with the myocardium over the cardiac cycle can be imaged. Quantitative computation of parameters such as wall thickening, shearing, rotation, torsion and strain within the myocardium is traditionally achieved by processing the tag-marked MR image frames to 1) segment the tag lines and 2) detect the correspondence between points across the time-indexed frames. In this paper, we describe our approach to solving this problem using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) algorithm in which tag-line segmentation and motion reconstruction occur simultaneously. Our method differs from earlier proposed non rigid registration based cardiac motion estimation methods in that our matching cost incorporates image intensity overlap via the L² norm and the estimated tranformations are diffeomorphic. We also present a novel method of generating synthetic tag line images with known ground truth and motion characteristics that closely follow those in the original data; these can be used for validation of motion estimation algorithms. Initial validation shows that our method is able to accurately segment tag-lines and estimate a dense 3D motion field describing the motion of the myocardium in both the left and the right ventricle.